Sensitivity-Enhanced Wearable Active Voiceprint Sensor Based on Cellular Polypropylene Piezoelectret.

PubMed

Li, Wenbo; Zhao, Sheng; Wu, Nan; Zhong, Junwen; Wang, Bo; Lin, Shizhe; Chen, Shuwen; Yuan, Fang; Jiang, Hulin; Xiao, Yongjun; Hu, Bin; Zhou, Jun

2017-07-19

Wearable active sensors have extensive applications in mobile biosensing and human-machine interaction but require good flexibility, high sensitivity, excellent stability, and self-powered feature. In this work, cellular polypropylene (PP) piezoelectret was chosen as the core material of a sensitivity-enhanced wearable active voiceprint sensor (SWAVS) to realize voiceprint recognition. By virtue of the dipole orientation control method, the air layers in the piezoelectret were efficiently utilized, and the current sensitivity was enhanced (from 1.98 pA/Hz to 5.81 pA/Hz at 115 dB). The SWAVS exhibited the superiorities of high sensitivity, accurate frequency response, and excellent stability. The voiceprint recognition system could make correct reactions to human voices by judging both the password and speaker. This study presented a voiceprint sensor with potential applications in noncontact biometric recognition and safety guarantee systems, promoting the progress of wearable sensor networks.

Development of a wearable wireless body area network for health monitoring of the elderly and disabled

NASA Astrophysics Data System (ADS)

Rushambwa, Munyaradzi C.; Gezimati, Mavis; Jeeva, J. B.

2017-11-01

Novel advancements in systems miniaturization, electronics in health care and communication technologies are enabling the integration of both patients and doctors involvement in health care system. A Wearable Wireless Body Area Network (WWBAN) provides continuous, unobtrusive ambulatory, ubiquitous health monitoring, and provide real time patient’s status to the physician without any constraint on their normal daily life activities. In this project we developed a wearable wireless body area network system that continuously monitor the health of the elderly and the disabled and provide them with independent, safe and secure living. The WWBAN system monitors the following parameters; blood oxygen saturation using a pulse oximeter sensor (SpO2), heart rate (HR) pulse sensor, Temperature, hydration, glucose level and fall detection. When the wearable system is put on, the sensor values are processed and analysed. If any of the monitored parameter values falls below or exceeds the normal range, there is trigger of remote alert by which an SMS is send to a doctor or physician via GSM module and network. The developed system offers flexibility and mobility to the user; it is a real time system and has significance in revolutionizing health care system by enabling non-invasive, inexpensive, continuous health monitoring.

Monitoring activities of daily living based on wearable wireless body sensor network.

PubMed

Kańtoch, E; Augustyniak, P; Markiewicz, M; Prusak, D

2014-01-01

With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

PubMed Central

Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping

2016-01-01

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.

PubMed

Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping

2016-02-05

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

Ultrasensitive Wearable Soft Strain Sensors of Conductive, Self-healing, and Elastic Hydrogels with Synergistic "Soft and Hard" Hybrid Networks.

PubMed

Liu, Yan-Jun; Cao, Wen-Tao; Ma, Ming-Guo; Wan, Pengbo

2017-08-02

Robust, stretchable, and strain-sensitive hydrogels have recently attracted immense research interest because of their potential application in wearable strain sensors. The integration of the synergistic characteristics of decent mechanical properties, reliable self-healing capability, and high sensing sensitivity for fabricating conductive, elastic, self-healing, and strain-sensitive hydrogels is still a great challenge. Inspired by the mechanically excellent and self-healing biological soft tissues with hierarchical network structures, herein, functional network hydrogels are fabricated by the interconnection between a "soft" homogeneous polymer network and a "hard" dynamic ferric (Fe 3+ ) cross-linked cellulose nanocrystals (CNCs-Fe 3+ ) network. Under stress, the dynamic CNCs-Fe 3+ coordination bonds act as sacrificial bonds to efficiently dissipate energy, while the homogeneous polymer network leads to a smooth stress-transfer, which enables the hydrogels to achieve unusual mechanical properties, such as excellent mechanical strength, robust toughness, and stretchability, as well as good self-recovery property. The hydrogels demonstrate autonomously self-healing capability in only 5 min without the need of any stimuli or healing agents, ascribing to the reorganization of CNCs and Fe 3+ via ionic coordination. Furthermore, the resulted hydrogels display tunable electromechanical behavior with sensitive, stable, and repeatable variations in resistance upon mechanical deformations. Based on the tunable electromechanical behavior, the hydrogels can act as a wearable strain sensor to monitor finger joint motions, breathing, and even the slight blood pulse. This strategy of building synergistic "soft and hard" structures is successful to integrate the decent mechanical properties, reliable self-healing capability, and high sensing sensitivity together for assembling a high-performance, flexible, and wearable strain sensor.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

PubMed

Kim, Kee-Hoon; Cho, Sung-Bae

2017-12-11

Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

Channel Model on Various Frequency Bands for Wearable Body Area Network

NASA Astrophysics Data System (ADS)

Katayama, Norihiko; Takizawa, Kenichi; Aoyagi, Takahiro; Takada, Jun-Ichi; Li, Huan-Bang; Kohno, Ryuji

Body Area Network (BAN) is considered as a promising technology in supporting medical and healthcare services by combining with various biological sensors. In this paper, we look at wearable BAN, which provides communication links among sensors on body surface. In order to design a BAN that manages biological information with high efficiency and high reliability, the propagation characteristics of BAN must be thoroughly investigated. As a preliminary effort, we measured the propagation characteristics of BAN at frequency bands of 400MHz, 600MHz, 900MHz and 2400MHz respectively. Channel models for wearable BAN based on the measurement were derived. Our results show that the channel model can be described by using a path loss model for all frequency bands investigated.

Wearable technology: role in respiratory health and disease.

PubMed

Aliverti, Andrea

2017-06-01

In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine.

Wearable technology: role in respiratory health and disease

PubMed Central

2017-01-01

In the future, diagnostic devices will be able to monitor a patient’s physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare’s Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine. PMID:28966692

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities

PubMed Central

Kim, Kee-Hoon

2017-01-01

Recently, recognizing a user’s daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user’s obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the “Five W’s”, and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54–14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing. PMID:29232937

Radio-frequency energy harvesting for wearable sensors.

PubMed

Borges, Luís M; Chávez-Santiago, Raul; Barroca, Norberto; Velez, Fernando José; Balasingham, Ilangko

2015-02-01

The use of wearable biomedical sensors for the continuous monitoring of physiological signals will facilitate the involvement of the patients in the prevention and management of chronic diseases. The fabrication of small biomedical sensors transmitting physiological data wirelessly is possible as a result of the tremendous advances in ultra-low power electronics and radio communications. However, the widespread adoption of these devices depends very much on their ability to operate for long periods of time without the need to frequently change, recharge or even use batteries. In this context, energy harvesting (EH) is the disruptive technology that can pave the road towards the massive utilisation of wireless wearable sensors for patient self-monitoring and daily healthcare. Radio-frequency (RF) transmissions from commercial telecommunication networks represent reliable ambient energy that can be harvested as they are ubiquitous in urban and suburban areas. The state-of-the-art in RF EH for wearable biomedical sensors specifically targeting the global system of mobile 900/1800 cellular and 700 MHz digital terrestrial television networks as ambient RF energy sources are showcased. Furthermore, guidelines for the choice of the number of stages for the RF energy harvester are presented, depending on the requirements from the embedded system to power supply, which is useful for other researchers that work in the same area. The present authors' recent advances towards the development of an efficient RF energy harvester and storing system are presented and thoroughly discussed too.

Wearable Virtual White Cane Network for navigating people with visual impairment.

PubMed

Gao, Yabiao; Chandrawanshi, Rahul; Nau, Amy C; Tse, Zion Tsz Ho

2015-09-01

Navigating the world with visual impairments presents inconveniences and safety concerns. Although a traditional white cane is the most commonly used mobility aid due to its low cost and acceptable functionality, electronic traveling aids can provide more functionality as well as additional benefits. The Wearable Virtual Cane Network is an electronic traveling aid that utilizes ultrasound sonar technology to scan the surrounding environment for spatial information. The Wearable Virtual Cane Network is composed of four sensing nodes: one on each of the user's wrists, one on the waist, and one on the ankle. The Wearable Virtual Cane Network employs vibration and sound to communicate object proximity to the user. While conventional navigation devices are typically hand-held and bulky, the hands-free design of our prototype allows the user to perform other tasks while using the Wearable Virtual Cane Network. When the Wearable Virtual Cane Network prototype was tested for distance resolution and range detection limits at various displacements and compared with a traditional white cane, all participants performed significantly above the control bar (p < 4.3 × 10(-5), standard t-test) in distance estimation. Each sensor unit can detect an object with a surface area as small as 1 cm(2) (1 cm × 1 cm) located 70 cm away. Our results showed that the walking speed for an obstacle course was increased by 23% on average when subjects used the Wearable Virtual Cane Network rather than the white cane. The obstacle course experiment also shows that the use of the white cane in combination with the Wearable Virtual Cane Network can significantly improve navigation over using either the white cane or the Wearable Virtual Cane Network alone (p < 0.05, paired t-test). © IMechE 2015.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Fusion solution for soldier wearable gunfire detection systems

NASA Astrophysics Data System (ADS)

Cakiades, George; Desai, Sachi; Deligeorges, Socrates; Buckland, Bruce E.; George, Jemin

2012-06-01

Currently existing acoustic based Gunfire Detection Systems (GDS) such as soldier wearable, vehicle mounted, and fixed site devices provide enemy detection and localization capabilities to the user. However, the solution to the problem of portability versus performance tradeoff remains elusive. The Data Fusion Module (DFM), described herein, is a sensor/platform agnostic software supplemental tool that addresses this tradeoff problem by leveraging existing soldier networks to enhance GDS performance across a Tactical Combat Unit (TCU). The DFM software enhances performance by leveraging all available acoustic GDS information across the TCU synergistically to calculate highly accurate solutions more consistently than any individual GDS in the TCU. The networked sensor architecture provides additional capabilities addressing the multiple shooter/fire-fight problems in addition to sniper detection/localization. The addition of the fusion solution to the overall Size, Weight and Power & Cost (SWaP&C) is zero to negligible. At the end of the first-year effort, the DFM integrated sensor network's performance was impressive showing improvements upwards of 50% in comparison to a single sensor solution. Further improvements are expected when the networked sensor architecture created in this effort is fully exploited.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Inkjet-/3D-/4D-printed autonomous wearable RF modules for biomonitoring, positioning and sensing applications

NASA Astrophysics Data System (ADS)

Bito, Jo; Bahr, Ryan; Hester, Jimmy; Kimionis, John; Nauroze, Abdullah; Su, Wenjing; Tehrani, Bijan; Tentzeris, Manos M.

2017-05-01

In this paper, numerous inkjet-/3D-/4D-printed wearable flexible antennas, RF electronics, modules and sensors fabricated on paper and other polymer (e.g. LCP) substrates are introduced as a system-level solution for ultra-low-cost mass production of autonomous Biomonitoring, Positioning and Sensing applications. This paper briefly discusses the state-of-the-art area of fully-integrated wearable wireless sensor modules on paper or flexible LCP and show the first ever 4D sensor module integration on paper, as well as numerous 3D and 4D multilayer paper-based and LCP-based RF/microwave, flexible and wearable structures, that could potentially set the foundation for the truly convergent wireless sensor ad-hoc "on-body networks of the future with enhanced cognitive intelligence and "rugged" packaging. Also, some challenges concerning the power sources of "nearperpetual" wearable RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving electromagnetic and solar energy forms are discuessed. The final step of the paper will involve examples from mmW wearable (e.g. biomonitoring) antennas and RF modules, as well as the first examples of the integration of inkjet-printed nanotechnology-based (e.g.CNT) sensors on paper and organic substrates for Internet of Things (IoT) applications. It has to be noted that the paper will review and present challenges for inkjetprinted organic active and nonlinear devices as well as future directions in the area of environmentally-friendly "green") wearable RF electronics and "smart-skin conformal sensors.

Development and evaluation of an ambulatory stress monitor based on wearable sensors.

PubMed

Choi, Jongyoon; Ahmed, Beena; Gutierrez-Osuna, Ricardo

2012-03-01

Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects. © 2012 IEEE

Beyond activity tracking: next-generation wearable and implantable sensor technologies (Conference Presentation)

NASA Astrophysics Data System (ADS)

Mercier, Patrick

2017-05-01

Current-generation wearable devices have had success continuously measuring the activity and heart rate of subjects during exercise and daily life activities, resulting in interesting new data sets that can, though machine learning algorithms, predict a small subset of health conditions. However, this information is only very peripherally related to most health conditions, and thus offers limited utility to a wide range of the population. In this presentation, I will discuss emerging sensor technologies capable of measuring new and interesting parameters that can potentially offer much more meaningful and actionable data sets. Specifically, I will present recent work on wearable chemical sensors that can, for the first time, continuously monitor a suite of parameters like glucose, alcohol, lactate, and electrolytes, all while wirelessly delivering these results to a smart phone in real time. Demonstration platforms featuring patch, temporary tattoo, and mouthguard form factors will be described, in addition to the corresponding electronics necessary to perform sensor conditioning and wireless readout. Beyond chemical sensors, I will also discuss integration strategies with more conventional electrophysiological and physical parameters like ECG and strain gauges for cardiac and respiration rate monitoring, respectively. Finally, I will conclude the talk by introducing a new form of wireless communications in body-area networks that utilize the body itself as a channel for magnetic energy. Since the power consumption of conventional RF circuits often dominates the power of wearable devices, this new magnetic human body communication technique is specifically architected to dramatically reduce the path loss compared to conventional RF and capacitive human body communication techniques, thereby enabling ultra-low-power body area networks for next-generation wearable devices.

Manufacturing of Wearable Sensors for Human Health and Performance Monitoring

NASA Astrophysics Data System (ADS)

Alizadeh, Azar

2015-03-01

Continuous monitoring of physiological and biological parameters is expected to improve performance and medical outcomes by assessing overall health status and alerting for life-saving interventions. Continuous monitoring of these parameters requires wearable devices with an appropriate form factor (lightweight, comfortable, low energy consuming and even single-use) to avoid disrupting daily activities thus ensuring operation relevance and user acceptance. Many previous efforts to implement remote and wearable sensors have suffered from high cost and poor performance, as well as low clinical and end-use acceptance. New manufacturing and system level design approaches are needed to make the performance and clinical benefits of these sensors possible while satisfying challenging economic, regulatory, clinical, and user-acceptance criteria. In this talk we will review several recent design and manufacturing efforts aimed at designing and building prototype wearable sensors. We will discuss unique opportunities and challenges provided by additive manufacturing, including 3D printing, to drive innovation through new designs, faster prototyping and manufacturing, distributed networks, and new ecosystems. We will also show alternative hybrid self-assembly based integration techniques for low cost large scale manufacturing of single use wearable devices. Coauthors: Prabhjot Singh and Jeffrey Ashe.

An Alternative Wearable Tracking System Based on a Low-Power Wide-Area Network.

PubMed

Fernández-Garcia, Raul; Gil, Ignacio

2017-03-14

This work presents an alternative wearable tracking system based on a low-power wide area network. A complete GPS receiver was integrated with a textile substrate, and the latitude and longitude coordinates were sent to the cloud by means of the SIM-less SIGFOX network. To send the coordinates over SIGFOX protocol, a specific codification algorithm was used and a customized UHF antenna on jeans fabric was designed, simulated and tested. Moreover, to guarantee the compliance to international regulations for human body exposure to electromagnetic radiation, the electromagnetic specific absorption rate of this antenna was analyzed. A specific remote server was developed to decode the latitude and longitude coordinates. Once the coordinates have been decoded, the remote server sends this information to the open source data viewer SENTILO to show the location of the sensor node in a map. The functionality of this system has been demonstrated experimentally. The results guarantee the utility and wearability of the proposed tracking system for the development of sensor nodes and point out that it can be a low cost alternative to other commercial products based on GSM networks.

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

2016-07-05

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

A Wearable Hydration Sensor with Conformal Nanowire Electrodes.

PubMed

Yao, Shanshan; Myers, Amanda; Malhotra, Abhishek; Lin, Feiyan; Bozkurt, Alper; Muth, John F; Zhu, Yong

2017-03-01

A wearable skin hydration sensor in the form of a capacitor is demonstrated based on skin impedance measurement. The capacitor consists of two interdigitated or parallel electrodes that are made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. The flexible and stretchable nature of the AgNW/PDMS electrode allows conformal contact to the skin. The hydration sensor is insensitive to the external humidity change and is calibrated against a commercial skin hydration system on an artificial skin over a wide hydration range. The hydration sensor is packaged into a flexible wristband, together with a network analyzer chip, a button cell battery, and an ultralow power microprocessor with Bluetooth. In addition, a chest patch consisting of a strain sensor, three electrocardiography electrodes, and a skin hydration sensor is developed for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless, and continuous monitoring of skin hydration and other health parameters. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

BIOTEX--biosensing textiles for personalised healthcare management.

PubMed

Coyle, Shirley; Lau, King-Tong; Moyna, Niall; O'Gorman, Donal; Diamond, Dermot; Di Francesco, Fabio; Costanzo, Daniele; Salvo, Pietro; Trivella, Maria Giovanna; De Rossi, Danilo Emilio; Taccini, Nicola; Paradiso, Rita; Porchet, Jacque-André; Ridolfi, Andrea; Luprano, Jean; Chuzel, Cyril; Lanier, Thierry; Revol-Cavalier, Frdéric; Schoumacker, Sébastien; Mourier, Véronique; Chartier, Isabelle; Convert, Reynald; De-Moncuit, Henri; Bini, Christina

2010-03-01

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting.

3D electrode localization on wireless sensor networks for wearable BCI.

PubMed

Figueiredo, C P; Dias, N S; Hoffmann, K P; Mendes, P M

2008-01-01

This paper presents a solution for electrode localization on wearable BCI radio-enabled electrodes. Electrode positioning is a common issue in any electrical physiological recording. Although wireless node localization is a very active research topic, a precise method with few centimeters of range and a resolution in the order of millimeters is still to be found, since far-field measurements are very prone to error. The calculation of 3D coordinates for each electrode is based on anchorless range-based localization algorithms such as Multidimensional Scaling and Self-Positioning Algorithm. The implemented solution relies on the association of a small antenna to measure the magnetic field and a microcontroller to each electrode, which will be part of the wireless sensor network module. The implemented solution is suitable for EEG applications, namely the wearable BCI, with expected range of 20 cm and resolution of 5 mm.

Rodent wearable ultrasound system for wireless neural recording.

PubMed

Piech, David K; Kay, Joshua E; Boser, Bernhard E; Maharbiz, Michel M

2017-07-01

Advances in minimally-invasive, distributed biological interface nodes enable possibilities for networks of sensors and actuators to connect the brain with external devices. The recent development of the neural dust sensor mote has shown that utilizing ultrasound backscatter communication enables untethered sub-mm neural recording devices. These implanted sensor motes require a wearable external ultrasound interrogation device to enable in-vivo, freely-behaving neural interface experiments. However, minimizing the complexity and size of the implanted sensors shifts the power and processing burden to the external interrogator. In this paper, we present an ultrasound backscatter interrogator that supports real-time backscatter processing in a rodent-wearable, completely wireless device. We demonstrate a generic digital encoding scheme which is intended for transmitting neural information. The system integrates a front-end ultrasonic interface ASIC with off-the-shelf components to enable a highly compact ultrasound interrogation device intended for rodent neural interface experiments but applicable to other model systems.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

PubMed

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

[Wearable Devices for Movement Monitoring of Patients with Parkinson’s Disease].

PubMed

Li, Liang; Yu, Qian; Xu, Baoteng; Bai, Qifan; Zhang, Yunpeng; Zhang, Huijun; Mao, Chengjie; Liu, Chunfeng; Wang, Shouyan

2016-12-01

Quantitative assessment of the symptoms of Parkinson’s disease is the key for precise diagnosis and treatment and essential for long term management over years.The challenges of quantitative assessment on Parkinson’s disease are rich information,ultra-low load,long term and large range monitoring in free-moving condition.In this paper,we developed wearable devices with multiple sensors to monitor and quantify the movement symptoms of Parkinson’s disease.Five wearable sensors were used to record motion signals from bilateral forearms,legs and waist.A local area network based on low power Wi-Fi technology was built for long distance wireless data transmission.A software was developed for signal recording and analyzing.The size of each sensor was 39mm×33mm×16mm and the weight was 18 g.The sensors were rechargeable and able to run 12 hours.The wireless transmission radius is about 45 m.The wearable devices were tested in patients and normal subjects.The devices were reliable and accurate for movement monitoring in hospital.

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

PubMed

Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

2017-05-01

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

SmartStuff: A case study of a smart water bottle.

PubMed

Jovanov, Emil; Nallathimmareddygari, Vindhya R; Pryor, Jonathan E

2016-08-01

The rapid growth of Internet of Things (IoT) and miniature wearable biosensors have generated new opportunities for personalized eHealth and mHealth services. Smart objects equipped with physiological sensors can provide robust monitoring of activities of daily living and context for wearable physiological sensors. We present a case study of an intelligent water bottle that can precisely measure the amount of liquid in the bottle, monitor activity using inertial sensors, and physiological parameters using a touch and photoplethysmographic sensor. We evaluate two system configurations: a smart water bottle integrated into a personal body sensor network and a cloud based device. This paper presents system organization and the results from preliminary field testing of the prototype device.

A low-power multi-modal body sensor network with application to epileptic seizure monitoring.

PubMed

Altini, Marco; Del Din, Silvia; Patel, Shyamal; Schachter, Steven; Penders, Julien; Bonato, Paolo

2011-01-01

Monitoring patients' physiological signals during their daily activities in the home environment is one of the challenge of the health care. New ultra-low-power wireless technologies could help to achieve this goal. In this paper we present a low-power, multi-modal, wearable sensor platform for the simultaneous recording of activity and physiological data. First we provide a description of the wearable sensor platform, and its characteristics with respect to power consumption. Second we present the preliminary results of the comparison between our sensors and a reference system, on healthy subjects, to test the reliability of the detected physiological (electrocardiogram and respiration) and electromyography signals.

Real-time monitoring of ubiquitous wireless ECG sensor node for medical care using ZigBee

NASA Astrophysics Data System (ADS)

Vijayalakshmi, S. R.; Muruganand, S.

2012-01-01

Sensor networks have the potential to impact many aspects of medical care greatly. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. In this article, we propose the system architecture for smart sensor platform based on advanced wireless sensor networks. An emerging application for wireless sensor networks involves their use in medical care. In hospitals or clinics, outfitting every patient with tiny, wearable wireless vital sign sensors would allow doctors, nurses and other caregivers to continuously monitor the status of their patients. In an emergency or disaster scenario, the same technology would enable medics to more effectively care for a large number of casualties. First responders could receive immediate notifications on any changes in patient status, such as respiratory failure or cardiac arrest. Wireless sensor network is a set of small, autonomous devices, working together to solve different problems. It is a relatively new technology, experiencing true expansion in the past decade. People have realised that integration of small and cheap microcontrollers with sensors can result in the production of extremely useful devices, which can be used as an integral part of the sensor nets. These devices are called sensor nodes. Today, sensor nets are used in agriculture, ecology and tourism, but medicine is the area where they certainly meet the greatest potential. This article presents a medical smart sensor node platform. This article proposes a wireless two-lead EKG. These devices collect heart rate and EKG data and relay it over a short-range (300 m) wireless network to any number of receiving devices, including PDAs, laptops or ambulance-based terminals.

An Alternative Wearable Tracking System Based on a Low-Power Wide-Area Network

PubMed Central

Fernández-Garcia, Raul; Gil, Ignacio

2017-01-01

This work presents an alternative wearable tracking system based on a low-power wide area network. A complete GPS receiver was integrated with a textile substrate, and the latitude and longitude coordinates were sent to the cloud by means of the SIM-less SIGFOX network. To send the coordinates over SIGFOX protocol, a specific codification algorithm was used and a customized UHF antenna on jeans fabric was designed, simulated and tested. Moreover, to guarantee the compliance to international regulations for human body exposure to electromagnetic radiation, the electromagnetic specific absorption rate of this antenna was analyzed. A specific remote server was developed to decode the latitude and longitude coordinates. Once the coordinates have been decoded, the remote server sends this information to the open source data viewer SENTILO to show the location of the sensor node in a map. The functionality of this system has been demonstrated experimentally. The results guarantee the utility and wearability of the proposed tracking system for the development of sensor nodes and point out that it can be a low cost alternative to other commercial products based on GSM networks. PMID:28335424

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

PubMed Central

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

PubMed Central

Li, Chao; Zhang, Zhenjiang; Chao, Han-Chieh

2017-01-01

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. PMID:29280950

Gait Analysis Using Wearable Sensors

PubMed Central

Tao, Weijun; Liu, Tao; Zheng, Rencheng; Feng, Hutian

2012-01-01

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications. PMID:22438763

Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors

NASA Astrophysics Data System (ADS)

McLamore, E. S.; Convertino, M.; Hondred, John; Das, Suprem; Claussen, J. C.; Vanegas, D. C.; Gomes, C.

2016-05-01

Here we demonstrate a novel approach for fabricating point of care (POC) wearable electrochemical biosensors based on 3D patterning of bionanocomposite networks. To create Bio-Inspired Patterned network (BIPS) electrodes, we first generate fractal network in silico models that optimize transport of network fluxes according to an energy function. Network patterns are then inkjet printed onto flexible substrate using conductive graphene ink. We then deposit fractal nanometal structures onto the graphene to create a 3D nanocomposite network. Finally, we biofunctionalize the surface with biorecognition agents using covalent bonding. In this paper, BIPS are used to develop high efficiency, low cost biosensors for measuring glucose as a proof of concept. Our results on the fundamental performance of BIPS sensors show that the biomimetic nanostructures significantly enhance biosensor sensitivity, accuracy, response time, limit of detection, and hysteresis compared to conventional POC non fractal electrodes (serpentine, interdigitated, and screen printed electrodes). BIPs, in particular Apollonian patterned BIPS, represent a new generation of POC biosensors based on nanoscale and microscale fractal networks that significantly improve electrical connectivity, leading to enhanced sensor performance.

A Study on Secure Medical-Contents Strategies with DRM Based on Cloud Computing

PubMed Central

Měsíček, Libor; Choi, Jongsun

2018-01-01

Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients' biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely. PMID:29796233

A Study on Secure Medical-Contents Strategies with DRM Based on Cloud Computing.

PubMed

Ko, Hoon; Měsíček, Libor; Choi, Jongsun; Hwang, Seogchan

2018-01-01

Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients' biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.

Stretchable Platinum Network-Based Transparent Electrodes for Highly Sensitive Wearable Electronics.

PubMed

Wang, Yuting; Cheng, Jing; Xing, Yan; Shahid, Muhammad; Nishijima, Hiroki; Pan, Wei

2017-07-01

A platinum network-based transparent electrode has been fabricated by electrospinning. The unique nanobelt structured electrode demonstrates low sheet resistance (about 16 Ω sq -1 ) and high transparency of 80% and excellent flexibility. One of the most interesting demonstrations of this Pt nanobelt electrode is its excellent reversibly resilient characteristic. The electric conductivity of the flexible Pt electrode can recover to its initial value after 160% extending and this performance is repeatable and stable. The good linear relationship between the resistance and strain of the unique structured Pt electrode makes it possible to assemble a wearable high sensitive strain sensor. Present reported Pt nanobelt electrode also reveals potential applications in electrode for flexible fuel cells and highly transparent ultraviolet (UV) sensors. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Activity classification using realistic data from wearable sensors.

PubMed

Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka

2006-01-01

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

Nanomaterial-Enabled Wearable Sensors for Healthcare.

PubMed

Yao, Shanshan; Swetha, Puchakayala; Zhu, Yong

2018-01-01

Highly sensitive wearable sensors that can be conformably attached to human skin or integrated with textiles to monitor the physiological parameters of human body or the surrounding environment have garnered tremendous interest. Owing to the large surface area and outstanding material properties, nanomaterials are promising building blocks for wearable sensors. Recent advances in the nanomaterial-enabled wearable sensors including temperature, electrophysiological, strain, tactile, electrochemical, and environmental sensors are presented in this review. Integration of multiple sensors for multimodal sensing and integration with other components into wearable systems are summarized. Representative applications of nanomaterial-enabled wearable sensors for healthcare, including continuous health monitoring, daily and sports activity tracking, and multifunctional electronic skin are highlighted. Finally, challenges, opportunities, and future perspectives in the field of nanomaterial-enabled wearable sensors are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-Powered Multiparameter Health Sensor.

PubMed

Tobola, Andreas; Leutheuser, Heike; Pollak, Markus; Spies, Peter; Hofmann, Christian; Weigand, Christian; Eskofier, Bjoern M; Fischer, Georg

2018-01-01

Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit-for evaluation of biomedical sensors and monitoring applications (http://ulpsek.com). ULPSEK includes a multiparameter sensor measuring and processing electrocardiogram, respiration, motion, body temperature, and photoplethysmography. Instead of a battery, ULPSEK is powered using an efficient body heat harvester. The harvester produced 171 W on average, which was sufficient to power the sensor below 25 C ambient temperature. We present design issues regarding the power supply and the power distribution network of the ULPSEK sensor platform. Due to the security aspect of self-powered health sensors, we suggest a hybrid solution consisting of a battery charged by a harvester.

A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease.

PubMed

Nancy Jane, Y; Khanna Nehemiah, H; Arputharaj, Kannan

2016-04-01

Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms. Copyright © 2016 Elsevier Inc. All rights reserved.

Flexible electronics-compatible non-enzymatic glucose sensing via transparent CuO nanowire networks on PET films

NASA Astrophysics Data System (ADS)

Bell, Caroline; Nammari, Abdullah; Uttamchandani, Pranay; Rai, Amit; Shah, Pujan; Moore, Arden L.

2017-06-01

Diabetic individuals need simple, accurate, and cost effective means by which to independently assess their glucose levels in a non-invasive way. In this work, a sensor based on randomly oriented CuO nanowire networks supported by a polyethylene terephthalate thin film is evaluated as a flexible, transparent, non-enzymatic glucose sensing system analogous to those envisioned for future wearable diagnostic devices. The amperometric sensing characteristics of this type of device architecture are evaluated both before and after bending, with the system’s glucose response, sensitivity, lower limit of detection, and effect of applied bias being experimentally determined. The obtained data shows that the sensor is capable of measuring changes in glucose levels within a physiologically relevant range (0-12 mM glucose) and at lower limits of detection (0.05 mM glucose at +0.6 V bias) consistent with patient tears and saliva. Unlike existing studies utilizing a conductive backing layer or macroscopic electrode setup, this sensor demonstrates a percolation network-like trend of current versus glucose concentration. In this implementation, controlling the architectural details of the CuO nanowire network could conceivably allow the sensor’s sensitivity and optimal sensing range to be tuned. Overall, this work shows that integrating CuO nanowires into a sensor architecture compatible with transparent, flexible electronics is a promising avenue to realizing next generation wearable non-enzymatic glucose diagnostic devices.

Synchronous wearable wireless body sensor network composed of autonomous textile nodes.

PubMed

Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

2014-10-09

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system.

Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes

PubMed Central

Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

2014-01-01

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system. PMID:25302808

Survey of energy harvesting and energy scavenging approaches for on-site powering of wireless sensor- and microinstrument-networks

NASA Astrophysics Data System (ADS)

Lee, D.; Dulai, G.; Karanassios, Vassili

2013-05-01

Energy (or power) harvesting can be defined as the gathering and either storing or immediately using energy "freely" available in a local environment. Examples include harvesting energy from obvious sources such as photon-fluxes (e.g., solar), or wind or water waves, or from unusual sources such as naturally occurring pH differences. Energy scavenging can be defined as gathering and storing or immediately re-using energy that has been discarded, for instance, waste heat from air conditioning units, from in-door lights or from everyday actions such as walking or from body-heat. Although the power levels that can be harvested or scavenged are typically low (e.g., from nWatt/cm2 to mWatt/cm2), the key motivation is to harvest or to scavenge energy for a wide variety of applications. Example applications include powering devices in remote weather stations, or wireless Bluetooth headsets, or wearable computing devices or for sensor networks for health and bio-medical applications. Beyond sensors and sensor networks, there is a need to power compete systems, such as portable and energy-autonomous chemical analysis microinstruments for use on-site. A portable microinstrument is one that offers the same functionality as a large one but one that has at least one critical component in the micrometer regime. This paper surveys continuous or discontinuous energy harvesting and energy scavenging approaches (with particular emphasis on sensor and microinstrument networks) and it discusses current trends. It also briefly explores potential future directions, for example, for nature-inspired (e.g., photosynthesis), for human-power driven (e.g., for biomedical applications, or for wearable sensor networks) or for nanotechnology-enabled energy harvesting and energy scavenging approaches.

A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges.

PubMed

Ray, Partha Pratim; Dash, Dinesh; De, Debashis

2017-10-02

Rapid growth of sensor and computing platforms have introduced the wearable systems. In recent years, wearable systems have led to new applications across all medical fields. The aim of this review is to present current state-of-the-art approach in the field of wearable system based cancer detection and identify key challenges that resist it from clinical adoption. A total of 472 records were screened and 11 were finally included in this study. Two types of records were studied in this context that includes 45% research articles and 55% manufactured products. The review was performed per PRISMA guidelines where considerations was given to records that were published or reported between 2009 and 2017. The identified records included 4 cancer detecting wearable systems such as breast cancer (36.3%), skin cancer (36.3%), prostate cancer (18.1%), and multi-type cancer (9%). Most works involved sensor based smart systems comprising of microcontroller, Bluetooth module, and smart phone. Few demonstrated Ultra-Wide Band (i.e. UWB) antenna based wearable systems. Skin cancer detecting wearable systems were most comprehensible ones. The current works are gradually progressing with seamless integration of sensory units along with smart networking. However, they lack in cloud computing and long-range communication paradigms. Artificial intelligence and machine learning are key ports that need to be attached with current wearable systems. Further, clinical inertia, lack of awareness, and high cost are altogether pulling back the actual growth of such system. It is well comprehended that upon sincere orientation of all identified challenges, wearable systems would emerge as vital alternative to futuristic cancer detection.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes.

PubMed

Smieszek, Timo; Castell, Stefanie; Barrat, Alain; Cattuto, Ciro; White, Peter J; Krause, Gérard

2016-07-22

Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability. We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol).

PubMed

Kestens, Yan; Chaix, Basile; Gerber, Philippe; Desprès, Michel; Gauvin, Lise; Klein, Olivier; Klein, Sylvain; Köppen, Bernhard; Lord, Sébastien; Naud, Alexandre; Payette, Hélène; Richard, Lucie; Rondier, Pierre; Shareck, Martine; Sueur, Cédric; Thierry, Benoit; Vallée, Julie; Wasfi, Rania

2016-05-05

Given the challenges of aging populations, calls have been issued for more sustainable urban re-development and implementation of local solutions to address global environmental and healthy aging issues. However, few studies have considered older adults' daily mobility to better understand how local built and social environments may contribute to healthy aging. Meanwhile, wearable sensors and interactive map-based applications offer novel means for gathering information on people's mobility, levels of physical activity, or social network structure. Combining such data with classical questionnaires on well-being, physical activity, perceived environments and qualitative assessment of experience of places opens new opportunities to assess the complex interplay between individuals and environments. In line with current gaps and novel analytical capabilities, this research proposes an international research agenda to collect and analyse detailed data on daily mobility, social networks and health outcomes among older adults using interactive web-based questionnaires and wearable sensors. Our study resorts to a battery of innovative data collection methods including use of a novel multisensor device for collection of location and physical activity, interactive map-based questionnaires on regular destinations and social networks, and qualitative assessment of experience of places. This rich data will allow advanced quantitative and qualitative analyses in the aim to disentangle the complex people-environment interactions linking urban local contexts to healthy aging, with a focus on active living, social networks and participation, and well-being. This project will generate evidence about what characteristics of urban environments relate to active mobility, social participation, and well-being, three important dimensions of healthy aging. It also sets the basis for an international research agenda on built environment and healthy aging based on a shared and comprehensive data collection protocol.

Flexible, Highly Sensitive, and Wearable Pressure and Strain Sensors with Graphene Porous Network Structure.

PubMed

Pang, Yu; Tian, He; Tao, Luqi; Li, Yuxing; Wang, Xuefeng; Deng, Ningqin; Yang, Yi; Ren, Tian-Ling

2016-10-03

A mechanical sensor with graphene porous network (GPN) combined with polydimethylsiloxane (PDMS) is demonstrated by the first time. Using the nickel foam as template and chemically etching method, the GPN can be created in the PDMS-nickel foam coated with graphene, which can achieve both pressure and strain sensing properties. Because of the pores in the GPN, the composite as pressure and strain sensor exhibit wide pressure sensing range and highest sensitivity among the graphene foam-based sensors, respectively. In addition, it shows potential applications in monitoring or even recognize the walking states, finger bending degree, and wrist blood pressure.

A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments

PubMed Central

Ghamari, Mohammad; Janko, Balazs; Sherratt, R. Simon; Harwin, William; Piechockic, Robert; Soltanpur, Cinna

2016-01-01

Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments. PMID:27338377

A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments.

PubMed

Ghamari, Mohammad; Janko, Balazs; Sherratt, R Simon; Harwin, William; Piechockic, Robert; Soltanpur, Cinna

2016-06-07

Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.

A review of wearable sensors and systems with application in rehabilitation

PubMed Central

2012-01-01

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed. PMID:22520559

What Does Big Data Mean for Wearable Sensor Systems?

PubMed Central

Lovell, N. H.; Yang, G. Z.; Horsch, A.; Lukowicz, P.; Murrugarra, L.; Marschollek, M.

2014-01-01

Summary Objectives The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals. PMID:25123733

Recent Progress of Self-Powered Sensing Systems for Wearable Electronics.

PubMed

Lou, Zheng; Li, La; Wang, Lili; Shen, Guozhen

2017-12-01

Wearable/flexible electronic sensing systems are considered to be one of the key technologies in the next generation of smart personal electronics. To realize personal portable devices with mobile electronics application, i.e., wearable electronic sensors that can work sustainably and continuously without an external power supply are highly desired. The recent progress and advantages of wearable self-powered electronic sensing systems for mobile or personal attachable health monitoring applications are presented. An overview of various types of wearable electronic sensors, including flexible tactile sensors, wearable image sensor array, biological and chemical sensor, temperature sensors, and multifunctional integrated sensing systems is provided. Self-powered sensing systems with integrated energy units are then discussed, separated as energy harvesting self-powered sensing systems, energy storage integrated sensing systems, and all-in-on integrated sensing systems. Finally, the future perspectives of self-powered sensing systems for wearable electronics are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

PubMed

He, Jian; Bai, Shuang; Wang, Xiaoyi

2017-06-16

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

Wireless body sensor networks for health-monitoring applications.

PubMed

Hao, Yang; Foster, Robert

2008-11-01

Current wireless technologies, such as wireless body area networks and wireless personal area networks, provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organizations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low-cost, low-power, non-invasive and unobtrusive system.

A wearable biofeedback control system based body area network for freestyle swimming.

PubMed

Rui Li; Zibo Cai; WeeSit Lee; Lai, Daniel T H

2016-08-01

Wearable posture measurement units are capable of enabling real-time performance evaluation and providing feedback to end users. This paper presents a wearable feedback prototype designed for freestyle swimming with focus on trunk rotation measurement. The system consists of a nine-degree-of-freedom inertial sensor, which is built in a central data collection and processing unit, and two vibration motors for delivering real-time feedback. Theses devices form a fundamental body area network (BAN). In the experiment setup, four recreational swimmers were asked to do two sets of 4 x 25m freestyle swimming without and with feedback provided respectively. Results showed that real-time biofeedback mechanism improves swimmers kinematic performance by an average of 4.5% reduction in session time. Swimmers can gradually adapt to feedback signals, and the biofeedback control system can be employed in swimmers daily training for fitness maintenance.

Identifying balance impairments in people with Parkinson's disease using video and wearable sensors.

PubMed

Stack, Emma; Agarwal, Veena; King, Rachel; Burnett, Malcolm; Tahavori, Fatemeh; Janko, Balazs; Harwin, William; Ashburn, Ann; Kunkel, Dorit

2018-05-01

Falls and near falls are common among people with Parkinson's (PwP). To date, most wearable sensor research focussed on fall detection, few studies explored if wearable sensors can detect instability. Can instability (caution or near-falls) be detected using wearable sensors in comparison to video analysis? Twenty-four people (aged 60-86) with and without Parkinson's were recruited from community groups. Movements (e.g. walking, turning, transfers and reaching) were observed in the gait laboratory and/or at home; recorded using clinical measures, video and five wearable sensors (attached on the waist, ankles and wrists). After defining 'caution' and 'instability', two researchers evaluated video data and a third the raw wearable sensor data; blinded to each other's evaluations. Agreement between video and sensor data was calculated on stability, timing, step count and strategy. Data was available for 117 performances: 82 (70%) appeared stable on video. Ratings agreed in 86/117 cases (74%). Highest agreement was noted for chair transfer, timed up and go test and 3 m walks. Video analysts noted caution (slow, contained movements, safety-enhancing postures and concentration) and/or instability (saving reactions, stopping after stumbling or veering) in 40/134 performances (30%): raw wearable sensor data identified 16/35 performances rated cautious or unstable (sensitivity 46%) and 70/82 rated stable (specificity 85%). There was a 54% chance that a performance identified from wearable sensors as cautious/unstable was so; rising to 80% for stable movements. Agreement between wearable sensor and video data suggested that wearable sensors can detect subtle instability and near-falls. Caution and instability were observed in nearly a third of performances, suggesting that simple, mildly challenging actions, with clearly defined start- and end-points, may be most amenable to monitoring during free-living at home. Using the genuine near-falls recorded, work continues to automatically detect subtle instability using algorithms. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Wearable sensor systems for infants.

PubMed

Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

2015-02-05

Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant's body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future.

Wearable Sensor Systems for Infants

PubMed Central

Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

2015-01-01

Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant's body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future. PMID:25664432

Remote monitoring of soldier safety through body posture identification using wearable sensor networks

NASA Astrophysics Data System (ADS)

Biswas, Subir; Quwaider, Muhannad

2008-04-01

The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naÃve threshold based mechanism, and its variations over different human subjects. A large spectrum of target human postures, including lie down, sit (straight and reclined), stand, walk, run, sprint and stair climbing, are used for validating the proposed system.

An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice

PubMed Central

Özdemir, Ahmet Turan

2016-01-01

Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications. PMID:27463719

An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.

PubMed

Özdemir, Ahmet Turan

2016-07-25

Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications.

Impact of Sensor Misplacement on Dynamic Time Warping Based Human Activity Recognition using Wearable Computers.

PubMed

Kale, Nimish; Lee, Jaeseong; Lotfian, Reza; Jafari, Roozbeh

2012-10-01

Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.

A novel dynamic sensing of wearable digital textile sensor with body motion analysis.

PubMed

Yang, Chang-Ming; Lin, Zhan-Sheng; Hu, Chang-Lin; Chen, Yu-Shih; Ke, Ling-Yi; Chen, Yin-Rui

2010-01-01

This work proposes an innovative textile sensor system to monitor dynamic body movement and human posture by attaching wearable digital sensors to analyze body motion. The proposed system can display and analyze signals when individuals are walking, running, veering around, walking up and down stairs, as well as falling down with a wearable monitoring system, which reacts to the coordination between the body and feet. Several digital sensor designs are embedded in clothing and wear apparel. Any pressure point can determine which activity is underway. Importantly, wearable digital sensors and a wearable monitoring system allow adaptive, real-time postures, real time velocity, acceleration, non-invasive, transmission healthcare, and point of care (POC) for home and non-clinical environments.

Flexible wearable sensor nodes with solar energy harvesting.

PubMed

Taiyang Wu; Arefin, Md Shamsul; Redoute, Jean-Michel; Yuce, Mehmet Rasit

2017-07-01

Wearable sensor nodes have gained a lot of attention during the past few years as they can monitor and record people's physical parameters in real time. Wearable sensor nodes can promote healthy lifestyles and prevent the occurrence of potential illness or injuries. This paper presents a flexible wearable sensor system powered by an efficient solar energy harvesting technique. It can measure the subject's heartbeats using a photoplethysmography (PPG) sensor and perform activity monitoring using an accelerometer. The solar energy harvester adopts an output current based maximum power point tracking (MPPT) algorithm, which controls the solar panel to operate within its high output power range. The power consumption of the flexible sensor nodes has been investigated under different operation conditions. Experimental results demonstrate that wearable sensor nodes can work for more than 12 hours when they are powered by the solar energy harvester for 3 hours in the bright sunlight.

Light-controlling, flexible and transparent ethanol gas sensor based on ZnO nanoparticles for wearable devices

PubMed Central

Zheng, Z. Q.; Yao, J. D.; Wang, B.; Yang, G. W.

2015-01-01

In recent years, owing to the significant applications of health monitoring, wearable electronic devices such as smart watches, smart glass and wearable cameras have been growing rapidly. Gas sensor is an important part of wearable electronic devices for detecting pollutant, toxic, and combustible gases. However, in order to apply to wearable electronic devices, the gas sensor needs flexible, transparent, and working at room temperature, which are not available for traditional gas sensors. Here, we for the first time fabricate a light-controlling, flexible, transparentand working at room-temperature ethanol gas sensor by using commercial ZnO nanoparticles. The fabricated sensor not only exhibits fast and excellent photoresponse, but also shows high sensing response to ethanol under UV irradiation. Meanwhile, its transmittance exceeds 62% in the visible spectral range, and the sensing performance keeps the same even bent it at a curvature angle of 90o. Additionally, using commercial ZnO nanoparticles provides a facile and low-cost route to fabricate wearable electronic devices. PMID:26076705

Light-controlling, flexible and transparent ethanol gas sensor based on ZnO nanoparticles for wearable devices.

PubMed

Zheng, Z Q; Yao, J D; Wang, B; Yang, G W

2015-06-16

In recent years, owing to the significant applications of health monitoring, wearable electronic devices such as smart watches, smart glass and wearable cameras have been growing rapidly. Gas sensor is an important part of wearable electronic devices for detecting pollutant, toxic, and combustible gases. However, in order to apply to wearable electronic devices, the gas sensor needs flexible, transparent, and working at room temperature, which are not available for traditional gas sensors. Here, we for the first time fabricate a light-controlling, flexible, transparent, and working at room-temperature ethanol gas sensor by using commercial ZnO nanoparticles. The fabricated sensor not only exhibits fast and excellent photoresponse, but also shows high sensing response to ethanol under UV irradiation. Meanwhile, its transmittance exceeds 62% in the visible spectral range, and the sensing performance keeps the same even bent it at a curvature angle of 90(o). Additionally, using commercial ZnO nanoparticles provides a facile and low-cost route to fabricate wearable electronic devices.

Performance Evaluation of Wearable Sensor Systems: A Case Study in Moderate-Scale Deployment in Hospital Environment.

PubMed

Sun, Wen; Ge, Yu; Zhang, Zhiqiang; Wong, Wai-Choong

2015-09-25

A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER) in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05) even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study.

A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

PubMed Central

Hu, Sheng; Wei, Hongxing; Chen, Youdong; Tan, Jindong

2012-01-01

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. PMID:23112746

A 300-mV 220-nW event-driven ADC with real-time QRS detection for wearable ECG sensors.

PubMed

Zhang, Xiaoyang; Lian, Yong

2014-12-01

This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 μm CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network.

PubMed

Zhao, Yongjia; Zhou, Suiping

2017-02-28

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN's input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

PubMed Central

Zhao, Yongjia; Zhou, Suiping

2017-01-01

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. PMID:28264503

Unintended Consequences of Wearable Sensor Use in Healthcare. Contribution of the IMIA Wearable Sensors in Healthcare WG.

PubMed

Schukat, M; McCaldin, D; Wang, K; Schreier, G; Lovell, N H; Marschollek, M; Redmond, S J

2016-11-10

As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations. Where possible, we proposed potential solutions to unwanted consequences. Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal nature of wearable sensors also exposes them to a number of unique security vulnerabilities. Regulation plays an important role in managing these security risks, but also has the dual responsibility of ensuring that wearable devices are fit for purpose. However, the burden of validating the function and security of medical devices is becoming infeasible for regulators, given the many software apps and wearable sensors entering the market each year, which are only a subset of an even larger 'internet of things'. Wearable sensors may serve to improve wellbeing, but we must be vigilant against the occurrence of unintended consequences. With collaboration between device manufacturers, regulators, and end-users, we balance the risk of unintended consequences occurring against the incredible benefit that wearable sensors promise to bring to the world.

Wearable sweat detector device design for health monitoring and clinical diagnosis

NASA Astrophysics Data System (ADS)

Wu, Qiuchen; Zhang, Xiaodong; Tian, Bihao; Zhang, Hongyan; Yu, Yang; Wang, Ming

2017-06-01

Miniaturized sensor is necessary part for wearable detector for biomedical applications. Wearable detector device is indispensable for online health care. This paper presents a concept of an wearable digital health monitoring device design for sweat analysis. The flexible sensor is developed to quantify the amount of hydrogen ions in sweat and skin temperature in real time. The detection system includes pH sensor, temperature sensor, signal processing module, power source, microprocessor, display module and so on. The sweat monitoring device is designed for sport monitoring or clinical diagnosis.

Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys.

PubMed

Mastrandrea, Rossana; Fournet, Julie; Barrat, Alain

2015-01-01

Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.

Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys

PubMed Central

Mastrandrea, Rossana; Fournet, Julie; Barrat, Alain

2015-01-01

Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations. PMID:26325289

Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.

PubMed

Schall, Mark C; Sesek, Richard F; Cavuoto, Lora A

2018-05-01

To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals; (b) potential benefits of using such technologies in the workplace; and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry. Wearable sensors are increasingly being promoted as a means to improve employee health and well-being, and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies. An electronic survey was emailed to 28,428 registered members of the American Society of Safety Engineers (ASSE) and 1,302 professionals certified by the Board of Certification in Professional Ergonomics (BCPE). A total of 952 valid responses were returned. Over half of respondents described being in favor of using wearable sensors to track OSH-related risk factors and relevant exposure metrics at their respective workplaces. However, barriers including concerns regarding employee privacy/confidentiality of collected data, employee compliance, sensor durability, the cost/benefit ratio of using wearables, and good manufacturing practice requirements were described as challenges precluding adoption. The broad adoption of wearable technologies appears to depend largely on the scientific community's ability to successfully address the identified barriers. Investigators may use the information provided to develop research studies that better address OSH practitioner concerns and help technology developers operationalize wearable sensors to improve employee health and well-being.

Sensing human physiological response using wearable carbon nanotube-based fabrics

NASA Astrophysics Data System (ADS)

Wang, Long; Loh, Kenneth J.; Koo, Helen S.

2016-04-01

Flexible and wearable sensors for human monitoring have received increased attention. Besides detecting motion and physical activity, measuring human vital signals (e.g., respiration rate and body temperature) provide rich data for assessing subjects' physiological or psychological condition. Instead of using conventional, bulky, sensing transducers, the objective of this study was to design and test a wearable, fabric-like sensing system. In particular, multi-walled carbon nanotube (MWCNT)-latex thin films of different MWCNT concentrations were first fabricated using spray coating. Freestanding MWCNT-latex films were then sandwiched between two layers of flexible fabric using iron-on adhesive to form the wearable sensor. Second, to characterize its strain sensing properties, the fabric sensors were subjected to uniaxial and cyclic tensile load tests, and they exhibited relatively stable electromechanical responses. Finally, the wearable sensors were placed on a human subject for monitoring simple motions and for validating their practical strain sensing performance. Overall, the wearable fabric sensor design exhibited advances such as flexibility, ease of fabrication, light weight, low cost, noninvasiveness, and user comfort.

Piezoresistive Sensor with High Elasticity Based on 3D Hybrid Network of Sponge@CNTs@Ag NPs.

PubMed

Zhang, Hui; Liu, Nishuang; Shi, Yuling; Liu, Weijie; Yue, Yang; Wang, Siliang; Ma, Yanan; Wen, Li; Li, Luying; Long, Fei; Zou, Zhengguang; Gao, Yihua

2016-08-31

Pressure sensors with high elasticity are in great demand for the realization of intelligent sensing, but there is a need to develope a simple, inexpensive, and scalable method for the manufacture of the sensors. Here, we reported an efficient, simple, facile, and repeatable "dipping and coating" process to manufacture a piezoresistive sensor with high elasticity, based on homogeneous 3D hybrid network of carbon nanotubes@silver nanoparticles (CNTs@Ag NPs) anchored on a skeleton sponge. Highly elastic, sensitive, and wearable sensors are obtained using the porous structure of sponge and the synergy effect of CNTs/Ag NPs. Our sensor was also tested for over 2000 compression-release cycles, exhibiting excellent elasticity and cycling stability. Sensors with high performance and a simple fabrication process are promising devices for commercial production in various electronic devices, for example, sport performance monitoring and man-machine interfaces.

A wearable context aware system for ubiquitous healthcare.

PubMed

Kang, Dong-Oh; Lee, Hyung-Jik; Ko, Eun-Jung; Kang, Kyuchang; Lee, Jeunwoo

2006-01-01

Recent developments of information technologies are leading the advent of the era of ubiquitous healthcare, which means healthcare services at any time and at any places. The ubiquitous healthcare service needs a wearable system for more continual measurement of biological signals of a user, which gives information of the user from wearable sensors. In this paper, we propose a wearable context aware system for ubiquitous healthcare, and its systematic design process of a ubiquitous healthcare service. Some wearable sensor systems are introduced with Zigbee communication. We develop a context aware framework to send information from wearable sensors to healthcare service entities as a middleware to solve the interoperability problem between sensor makers and healthcare service providers. And, we propose a systematic process of design of ubiquitous healthcare services with the context aware framework. In order to show the feasibility of the proposed system, some application examples are given, which are applied to remote monitoring, and a self check service.

Wearable Performance Devices in Sports Medicine.

PubMed

Li, Ryan T; Kling, Scott R; Salata, Michael J; Cupp, Sean A; Sheehan, Joseph; Voos, James E

2016-01-01

Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Clinical review. Level 4. Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. © 2015 The Author(s).

Wearable Performance Devices in Sports Medicine

PubMed Central

Li, Ryan T.; Kling, Scott R.; Salata, Michael J.; Cupp, Sean A.; Sheehan, Joseph; Voos, James E.

2016-01-01

Context: Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Evidence Acquisition: Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Study Design: Clinical review. Level of Evidence: Level 4. Results: Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Conclusion: Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. PMID:26733594

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.

PubMed

Lopez-Meyer, Paulo; Tiffany, Stephen; Sazonov, Edward

2012-01-01

This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

The Application of Wearable Technology in Surgery: Ensuring the Positive Impact of the Wearable Revolution on Surgical Patients

PubMed Central

Slade Shantz, Jesse Alan; Veillette, Christian J. H.

2014-01-01

Wearable technology has become an important trend in consumer electronics in the past year. The miniaturization and mass production of myriad sensors have made possible the integration of sensors and output devices in wearable platforms. Despite the consumer focus of the wearable revolution some surgical applications are being developed. These fall into augmentative, assistive, and assessment functions and primarily layer onto current surgical workflows. Some challenges to the adoption of wearable technologies are discussed and a conceptual framework for understanding the potential of wearable technology to revolutionize surgical practice are presented. PMID:25593963

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

PubMed Central

Barkallah, Eya; Freulard, Johan; Otis, Martin J. -D.; Ngomo, Suzy; Ayena, Johannes C.; Desrosiers, Christian

2017-01-01

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture. PMID:28862665

Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

DTIC Science & Technology

2013-10-01

AD_________________ Award Number: W81XWH-12-1-0541 TITLE: Wearable Wireless Sensor for Multi-Scale...TYPE Annual 3. DATES COVERED 25 12- 13 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Wearable Wireless Sensor for Multi-Scale Physiological...peripheral management • Procedures for low power mode activation and wake - up • Routines for start- up state detection • Flash memory management

A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

PubMed Central

Chen, Hongyu; Xue, Mengru; Mei, Zhenning; Bambang Oetomo, Sidarto; Chen, Wei

2016-01-01

Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design. PMID:27983664

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

PubMed

Yurtman, Aras; Barshan, Billur

2017-08-09

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

A wearable strain sensor based on a carbonized nano-sponge/silicone composite for human motion detection.

PubMed

Yu, Xiao-Guang; Li, Yuan-Qing; Zhu, Wei-Bin; Huang, Pei; Wang, Tong-Tong; Hu, Ning; Fu, Shao-Yun

2017-05-25

Melamine sponge, also known as nano-sponge, is widely used as an abrasive cleaner in our daily life. In this work, the fabrication of a wearable strain sensor for human motion detection is first demonstrated with a commercially available nano-sponge as a starting material. The key resistance sensitive material in the wearable strain sensor is obtained by the encapsulation of a carbonized nano-sponge (CNS) with silicone resin. The as-fabricated CNS/silicone sensor is highly sensitive to strain with a maximum gauge factor of 18.42. In addition, the CNS/silicone sensor exhibits a fast and reliable response to various cyclic loading within a strain range of 0-15% and a loading frequency range of 0.01-1 Hz. Finally, the CNS/silicone sensor as a wearable device for human motion detection including joint motion, eye blinking, blood pulse and breathing is demonstrated by attaching the sensor to the corresponding parts of the human body. In consideration of the simple fabrication technique, low material cost and excellent strain sensing performance, the CNS/silicone sensor is believed to have great potential in the next-generation of wearable devices for human motion detection.

Unintended Consequences of Wearable Sensor Use in Healthcare

PubMed Central

McCaldin, D.; Wang, K.; Schreier, G.; Lovell, N. H.; Marschollek, M.; Redmond, S. J.

2016-01-01

Summary Objectives As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. Methods We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations. Where possible, we proposed potential solutions to unwanted consequences. Results Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal nature of wearable sensors also exposes them to a number of unique security vulnerabilities. Regulation plays an important role in managing these security risks, but also has the dual responsibility of ensuring that wearable devices are fit for purpose. However, the burden of validating the function and security of medical devices is becoming infeasible for regulators, given the many software apps and wearable sensors entering the market each year, which are only a subset of an even larger ‘internet of things’. Conclusion Wearable sensors may serve to improve wellbeing, but we must be vigilant against the occurrence of unintended consequences. With collaboration between device manufacturers, regulators, and end-users, we balance the risk of unintended consequences occurring against the incredible benefit that wearable sensors promise to bring to the world. PMID:27830234

Stretchable Electronic Sensors of Nanocomposite Network Films for Ultrasensitive Chemical Vapor Sensing.

PubMed

Yan, Hong; Zhong, Mengjuan; Lv, Ze; Wan, Pengbo

2017-11-01

A stretchable, transparent, and body-attachable chemical sensor is assembled from the stretchable nanocomposite network film for ultrasensitive chemical vapor sensing. The stretchable nanocomposite network film is fabricated by in situ preparation of polyaniline/MoS 2 (PANI/MoS 2 ) nanocomposite in MoS 2 suspension and simultaneously nanocomposite deposition onto prestrain elastomeric polydimethylsiloxane substrate. The assembled stretchable electronic sensor demonstrates ultrasensitive sensing performance as low as 50 ppb, robust sensing stability, and reliable stretchability for high-performance chemical vapor sensing. The ultrasensitive sensing performance of the stretchable electronic sensors could be ascribed to the synergistic sensing advantages of MoS 2 and PANI, higher specific surface area, the reliable sensing channels of interconnected network, and the effectively exposed sensing materials. It is expected to hold great promise for assembling various flexible stretchable chemical vapor sensors with ultrasensitive sensing performance, superior sensing stability, reliable stretchability, and robust portability to be potentially integrated into wearable electronics for real-time monitoring of environment safety and human healthcare. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wearable and flexible electronics for continuous molecular monitoring.

PubMed

Yang, Yiran; Gao, Wei

2018-04-03

Wearable biosensors have received tremendous attention over the past decade owing to their great potential in predictive analytics and treatment toward personalized medicine. Flexible electronics could serve as an ideal platform for personalized wearable devices because of their unique properties such as light weight, low cost, high flexibility and great conformability. Unlike most reported flexible sensors that mainly track physical activities and vital signs, the new generation of wearable and flexible chemical sensors enables real-time, continuous and fast detection of accessible biomarkers from the human body, and allows for the collection of large-scale information about the individual's dynamic health status at the molecular level. In this article, we review and highlight recent advances in wearable and flexible sensors toward continuous and non-invasive molecular analysis in sweat, tears, saliva, interstitial fluid, blood, wound exudate as well as exhaled breath. The flexible platforms, sensing mechanisms, and device and system configurations employed for continuous monitoring are summarized. We also discuss the key challenges and opportunities of the wearable and flexible chemical sensors that lie ahead.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

PubMed

Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

2016-08-01

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training

PubMed Central

Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M.

2016-01-01

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user’s hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98.30% (±1.26%) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills. PMID:27527167

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training.

PubMed

Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M

2016-08-03

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user's hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor.

PubMed

Kim, Woosuk; Kim, Myunggyu

2018-03-19

In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

A Novel Wearable Electronic Nose for Healthcare Based on Flexible Printed Chemical Sensor Array

PubMed Central

Lorwongtragool, Panida; Sowade, Enrico; Watthanawisuth, Natthapol; Baumann, Reinhard R.; Kerdcharoen, Teerakiat

2014-01-01

A novel wearable electronic nose for armpit odor analysis is proposed by using a low-cost chemical sensor array integrated in a ZigBee wireless communication system. We report the development of a carbon nanotubes (CNTs)/polymer sensor array based on inkjet printing technology. With this technique both composite-like layer and actual composite film of CNTs/polymer were prepared as sensing layers for the chemical sensor array. The sensor array can response to a variety of complex odors and is installed in a prototype of wearable e-nose for monitoring the axillary odor released from human body. The wearable e-nose allows the classification of different armpit odors and the amount of the volatiles released as a function of level of skin hygiene upon different activities. PMID:25340447

Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

PubMed

Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

2018-01-10

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

Identifying compensatory movement patterns in the upper extremity using a wearable sensor system.

PubMed

Ranganathan, Rajiv; Wang, Rui; Dong, Bo; Biswas, Subir

2017-11-30

Movement impairments such as those due to stroke often result in the nervous system adopting atypical movements to compensate for movement deficits. Monitoring these compensatory patterns is critical for improving functional outcomes during rehabilitation. The purpose of this study was to test the feasibility and validity of a wearable sensor system for detecting compensatory trunk kinematics during activities of daily living. Participants with no history of neurological impairments performed reaching and manipulation tasks with their upper extremity, and their movements were recorded by a wearable sensor system and validated using a motion capture system. Compensatory movements of the trunk were induced using a brace that limited range of motion at the elbow. Our results showed that the elbow brace elicited compensatory movements of the trunk during reaching tasks but not manipulation tasks, and that a wearable sensor system with two sensors could reliably classify compensatory movements (~90% accuracy). These results show the potential of the wearable system to assess and monitor compensatory movements outside of a lab setting.

On the security of consumer wearable devices in the Internet of Things.

PubMed

Tahir, Hasan; Tahir, Ruhma; McDonald-Maier, Klaus

2018-01-01

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands.

On the security of consumer wearable devices in the Internet of Things

PubMed Central

Tahir, Hasan; Tahir, Ruhma; McDonald-Maier, Klaus

2018-01-01

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands. PMID:29668756

Wearable sensors: modalities, challenges, and prospects.

PubMed

Heikenfeld, J; Jajack, A; Rogers, J; Gutruf, P; Tian, L; Pan, T; Li, R; Khine, M; Kim, J; Wang, J; Kim, J

2018-01-16

Wearable sensors have recently seen a large increase in both research and commercialization. However, success in wearable sensors has been a mix of both progress and setbacks. Most of commercial progress has been in smart adaptation of existing mechanical, electrical and optical methods of measuring the body. This adaptation has involved innovations in how to miniaturize sensing technologies, how to make them conformal and flexible, and in the development of companion software that increases the value of the measured data. However, chemical sensing modalities have experienced greater challenges in commercial adoption, especially for non-invasive chemical sensors. There have also been significant challenges in making significant fundamental improvements to existing mechanical, electrical, and optical sensing modalities, especially in improving their specificity of detection. Many of these challenges can be understood by appreciating the body's surface (skin) as more of an information barrier than as an information source. With a deeper understanding of the fundamental challenges faced for wearable sensors and of the state-of-the-art for wearable sensor technology, the roadmap becomes clearer for creating the next generation of innovations and breakthroughs.

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

PubMed Central

Sprint, Gina; Cook, Diane J.; Weeks, Douglas L.; Borisov, Vladimir

2016-01-01

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported. PMID:27054054

Estimation of ground reaction forces and joint moments on the basis on plantar pressure insoles and wearable sensors for joint angle measurement.

PubMed

Ostaszewski, Michal; Pauk, Jolanta

2018-05-16

Gait analysis is a useful tool medical staff use to support clinical decision making. There is still an urgent need to develop low-cost and unobtrusive mobile health monitoring systems. The goal of this study was twofold. Firstly, a wearable sensor system composed of plantar pressure insoles and wearable sensors for joint angle measurement was developed. Secondly, the accuracy of the system in the measurement of ground reaction forces and joint moments was examined. The measurements included joint angles and plantar pressure distribution. To validate the wearable sensor system and examine the effectiveness of the proposed method for gait analysis, an experimental study on ten volunteer subjects was conducted. The accuracy of measurement of ground reaction forces and joint moments was validated against the results obtained from a reference motion capture system. Ground reaction forces and joint moments measured by the wearable sensor system showed a root mean square error of 1% for min. GRF and 27.3% for knee extension moment. The correlation coefficient was over 0.9, in comparison with the stationary motion capture system. The study suggests that the wearable sensor system could be recommended both for research and clinical applications outside a typical gait laboratory.

Freestanding, Fiber-Based, Wearable Temperature Sensor with Tunable Thermal Index for Healthcare Monitoring.

PubMed

Trung, Tran Quang; Le, Hoang Sinh; Dang, Thi My Linh; Ju, Sanghyun; Park, Sang Yoon; Lee, Nae-Eung

2018-06-01

Fiber-based sensors integrated on textiles or clothing systems are required for the next generation of wearable electronic platforms. Fiber-based physical sensors are developed, but the development of fiber-based temperature sensors is still limited. Herein, a new approach to develop wearable temperature sensors that use freestanding single reduction graphene oxide (rGO) fiber is proposed. A freestanding and wearable temperature-responsive rGO fiber with tunable thermal index is obtained using simple wet spinning and a controlled graphene oxide reduction time. The freestanding fiber-based temperature sensor shows high responsivity, fast response time (7 s), and good recovery time (20 s) to temperature. It also maintains its response under an applied mechanical deformation. The fiber device fabricated by means of a simple process is easily integrated into fabric such as socks or undershirts and can be worn by a person to monitor the temperature of the environment and skin temperature without interference during movement and various activities. These results demonstrate that the freestanding fiber-based temperature sensor has great potential for fiber-based wearable electronic platforms. It is also promising for applications in healthcare and biomedical monitoring. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Wireless Biomedical Signal Interface System-on-Chip for Body Sensor Networks.

PubMed

Lei Wang; Guang-Zhong Yang; Jin Huang; Jinyong Zhang; Li Yu; Zedong Nie; Cumming, D R S

2010-04-01

Recent years have seen the rapid development of biosensor technology, system-on-chip design, wireless technology. and ubiquitous computing. When assembled into an autonomous body sensor network (BSN), the technologies become powerful tools in well-being monitoring, medical diagnostics, and personal connectivity. In this paper, we describe the first demonstration of a fully customized mixed-signal silicon chip that has most of the attributes required for use in a wearable or implantable BSN. Our intellectual-property blocks include low-power analog sensor interface for temperature and pH, a data multiplexing and conversion module, a digital platform based around an 8-b microcontroller, data encoding for spread-spectrum wireless transmission, and a RF section requiring very few off-chip components. The chip has been fully evaluated and tested by connection to external sensors, and it satisfied typical system requirements.

Monitoring of Vital Signs with Flexible and Wearable Medical Devices.

PubMed

Khan, Yasser; Ostfeld, Aminy E; Lochner, Claire M; Pierre, Adrien; Arias, Ana C

2016-06-01

Advances in wireless technologies, low-power electronics, the internet of things, and in the domain of connected health are driving innovations in wearable medical devices at a tremendous pace. Wearable sensor systems composed of flexible and stretchable materials have the potential to better interface to the human skin, whereas silicon-based electronics are extremely efficient in sensor data processing and transmission. Therefore, flexible and stretchable sensors combined with low-power silicon-based electronics are a viable and efficient approach for medical monitoring. Flexible medical devices designed for monitoring human vital signs, such as body temperature, heart rate, respiration rate, blood pressure, pulse oxygenation, and blood glucose have applications in both fitness monitoring and medical diagnostics. As a review of the latest development in flexible and wearable human vitals sensors, the essential components required for vitals sensors are outlined and discussed here, including the reported sensor systems, sensing mechanisms, sensor fabrication, power, and data processing requirements. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interface-Controlled Conductive Fibers for Wearable Strain Sensors and Stretchable Conducting Wires.

PubMed

Cao, Zherui; Wang, Ranran; He, Tengyu; Xu, Fangfang; Sun, Jing

2018-04-25

As an important subfield of flexible electronics, conductive fibers have been an active area of research. The interfacial interaction between nanostructured conductive materials with elastic substrates plays a vital role in the electromechanical performance of conductive fibers. However, the underlying mechanism has seldom been investigated. Here, we propose a fabricating strategy for a silver nanowire (Ag NW)/polyurethane composite fiber with a sheath-core architecture. The interfacial bonding layer is regulated, and its influence on the performance of conductive fibers is investigated, based on which an interfacial interaction model is proposed. The model underlines the significance of the embedding depth of the Ag NW network. Both supersensitive (gauge factor up to 9557) and ultrastable (negligible conductance degradation below the strain of 150%) conductive fibers are obtained via interface regulating, exhibiting great potential in the applications of wearable sensors and stretchable conducting connections.

Highly sensitive, self-powered and wearable electronic skin based on pressure-sensitive nanofiber woven fabric sensor.

PubMed

Zhou, Yuman; He, Jianxin; Wang, Hongbo; Qi, Kun; Nan, Nan; You, Xiaolu; Shao, Weili; Wang, Lidan; Ding, Bin; Cui, Shizhong

2017-10-11

The wearable electronic skin with high sensitivity and self-power has shown increasing prospects for applications such as human health monitoring, robotic skin, and intelligent electronic products. In this work, we introduced and demonstrated a design of highly sensitive, self-powered, and wearable electronic skin based on a pressure-sensitive nanofiber woven fabric sensor fabricated by weaving PVDF electrospun yarns of nanofibers coated with PEDOT. Particularly, the nanofiber woven fabric sensor with multi-leveled hierarchical structure, which significantly induced the change in contact area under ultra-low load, showed combined superiority of high sensitivity (18.376 kPa -1 , at ~100 Pa), wide pressure range (0.002-10 kPa), fast response time (15 ms) and better durability (7500 cycles). More importantly, an open-circuit voltage signal of the PPNWF pressure sensor was obtained through applying periodic pressure of 10 kPa, and the output open-circuit voltage exhibited a distinct switching behavior to the applied pressure, indicating the wearable nanofiber woven fabric sensor could be self-powered under an applied pressure. Furthermore, we demonstrated the potential application of this wearable nanofiber woven fabric sensor in electronic skin for health monitoring, human motion detection, and muscle tremor detection.

'Do-It-Yourself' Healthcare? Quality of Health and Healthcare Through Wearable Sensors.

PubMed

Vesnic-Alujevic, Lucia; Breitegger, Melina; Guimarães Pereira, Ângela

2018-06-01

Wearable sensors are an integral part of the new telemedicine concept supporting the idea that Information Technologies will improve the quality and efficiency of healthcare. The use of sensors in diagnosis, treatment and monitoring of patients not only potentially changes medical practice but also one's relationship with one's body and mind, as well as the role and responsibilities of patients and healthcare professionals. In this paper, we focus on knowledge assessment of the online communities of Fitbit (a commercial wearable device) and the Quantified Self movement. Through their online forums, we investigate how users' knowledge claims, shared experiences and imaginations about wearable sensors interrogate or confirm the narratives through which they are introduced to the publics. Citizen initiatives like the Quantified Self movement claim the right to 'own' the sensor generated data. But how these data can be used through traditional healthcare systems is an open question. More importantly, wearable sensors trigger a social function that is transformative of the current idea of care and healthcare, focused on sharing, socialising and collectively reflecting about individual problems. Whether this is aligned with current policy making about healthcare, whose central narrative is focused on efficiency and productivity, is to be seen.

Measurement and visualization of face-to-face interaction among community-dwelling older adults using wearable sensors.

PubMed

Masumoto, Kouhei; Yaguchi, Takaharu; Matsuda, Hiroshi; Tani, Hideaki; Tozuka, Keisuke; Kondo, Narihiko; Okada, Shuichi

2017-10-01

A number of interventions have been undertaken to develop and promote social networks among community-dwelling older adults. However, it has been difficult to examine the effects of these interventions, because of problems in assessing interactions. The present study was designed to quantitatively measure and visualize face-to-face interactions among elderly participants in an exercise program. We also examined relationships among interactional variables, personality and interest in community involvement, including interactions with the local community. Older adults living in the same community were recruited to participate in an exercise program that consisted of four sessions. We collected data on face-to-face interactions of the participants by using a wearable sensor technology device. Network analysis identified the communication networks of participants in the exercise program, as well as changes in these networks. Additionally, there were significant correlations between the number of people involved in face-to-face interactions and changes in both interest in community involvement and interactions with local community residents, as well as personality traits, including agreeableness. Social networks in the community are essential for solving problems caused by the aging society. We showed the possible applications of face-to-face interactional data for identifying core participants having many interactions, and isolated participants having only a few interactions within the community. Such data would be useful for carrying out efficient interventions for increasing participants' involvement with their community. Geriatr Gerontol Int 2017; 17: 1752-1758. © 2017 Japan Geriatrics Society.

A Wearable Capacitive Sensor for Monitoring Human Respiratory Rate

NASA Astrophysics Data System (ADS)

Kundu, Subrata Kumar; Kumagai, Shinya; Sasaki, Minoru

2013-04-01

Realizing an untethered, low-cost, and comfortably wearable respiratory rate sensor for long-term breathing monitoring application still remains a challenge. In this paper, a conductive-textile-based wearable respiratory rate sensing technique based on the capacitive sensing approach is proposed. The sensing unit consists of two conductive textile electrodes that can be easily fabricated, laminated, and integrated in garments. Respiration cycle is detected by measuring the capacitance of two electrodes placed on the inner anterior and posterior sides of a T-shirt at either the abdomen or chest position. A convenient wearable respiratory sensor setup with a capacitance-to-voltage converter has been devised. Respiratory rate as well as breathing mode can be accurately identified using the designed sensor. The sensor output provides significant information on respiratory flow. The effectiveness of the proposed system for different breathing patterns has been evaluated by experiments.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Highly stretchable and ultrathin nanopaper composites for epidermal strain sensors.

PubMed

Sun, Jingyao; Zhao, Yanan; Yang, Zhaogang; Shen, Jingjing; Cabrera, Eusebio; Lertola, Matthew J; Yang, Willie; Zhang, Dan; Benatar, Avi; Castro, Jose M; Wu, Daming; Lee, L James

2018-08-31

Multifunctional electronics are attracting great interest with the increasing demand and fast development of wearable electronic devices. Here, we describe an epidermal strain sensor based on an all-carbon conductive network made from multi-walled carbon nanotubes (MWCNTs) impregnated with poly(dimethyl siloxane) (PDMS) matrix through a vacuum filtration process. An ultrasonication treatment was performed to complete the penetration of PDMS resin in seconds. The entangled and overlapped MWCNT network largely enhances the electrical conductivity (1430 S m -1 ), uniformity (remaining stable on different layers), reliable sensing range (up to 80% strain), and cyclic stability of the strain sensor. The homogeneous dispersion of MWCNTs within the PDMS matrix leads to a strong interaction between the two phases and greatly improves the mechanical stability (ca. 160% strain at fracture). The flexible, reversible and ultrathin (<100 μm) film can be directly attached on human skin as epidermal strain sensors for high accuracy and real-time human motion detection.

Secured remote health monitoring system

PubMed Central

Ganesh Kumar, Pugalendhi

2017-01-01

Wireless medical sensor network is used in healthcare applications that have the collections of biosensors connected to a human body or emergency care unit to monitor the patient's physiological vital status. The real-time medical data collected using wearable medical sensors are transmitted to a diagnostic centre. The data generated from the sensors are aggregated at this centre and transmitted further to the doctor's personal digital assistant for diagnosis. The unauthorised access of one's health data may lead to misuse and legal complications while unreliable data transmission or storage may lead to life threatening risk to patients. So, this Letter combines the symmetric algorithm and attribute-based encryption to secure the data transmission and access control system for medical sensor network. In this work, existing systems and their algorithm are compared for identifying the best performance. The work also shows the graphical comparison of encryption time, decryption time and total computation time of the existing and the proposed systems. PMID:29383257

Detection-gap-independent optical sensor design using divergence-beam-controlled slit lasers for wearable devices

NASA Astrophysics Data System (ADS)

Yoon, Young Zoon; Kim, Hyochul; Park, Yeonsang; Kim, Jineun; Lee, Min Kyung; Kim, Un Jeong; Roh, Young-Geun; Hwang, Sung Woo

2016-09-01

Wearable devices often employ optical sensors, such as photoplethysmography sensors, for detecting heart rates or other biochemical factors. Pulse waveforms, rather than simply detecting heartbeats, can clarify arterial conditions. However, most optical sensor designs require close skin contact to reduce power consumption while obtaining good quality signals without distortion. We have designed a detection-gap-independent optical sensor array using divergence-beam-controlled slit lasers and distributed photodiodes in a pulse-detection device wearable over the wrist's radial artery. It achieves high biosignal quality and low power consumption. The top surface of a vertical-cavity surface-emitting laser of 850 nm wavelength was covered by Au film with an open slit of width between 500 nm and 1500 nm, which generated laser emissions across a large divergence angle along an axis orthogonal to the slit direction. The sensing coverage of the slit laser diode (LD) marks a 50% improvement over nonslit LD sensor coverage. The slit LD sensor consumes 100% more input power than the nonslit LD sensor to obtain similar optical output power. The slit laser sensor showed intermediate performance between LD and light-emitting diode sensors. Thus, designing sensors with multiple-slit LD arrays can provide useful and convenient ways for incorporating optical sensors in wrist-wearable devices.

Next Generation RFID-Based Medical Service Management System Architecture in Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Tolentino, Randy S.; Lee, Kijeong; Kim, Yong-Tae; Park, Gil-Cheol

Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two important wireless technologies that have wide variety of applications and provide unlimited future potentials most especially in healthcare systems. RFID is used to detect presence and location of objects while WSN is used to sense and monitor the environment. Integrating RFID with WSN not only provides identity and location of an object but also provides information regarding the condition of the object carrying the sensors enabled RFID tag. However, there isn't any flexible and robust communication infrastructure to integrate these devices into an emergency care setting. An efficient wireless communication substrate for medical devices that addresses ad hoc or fixed network formation, naming and discovery, transmission efficiency of data, data security and authentication, as well as filtration and aggregation of vital sign data need to be study and analyze. This paper proposed an efficient next generation architecture for RFID-based medical service management system in WSN that possesses the essential elements of each future medical application that are integrated with existing medical practices and technologies in real-time, remote monitoring, in giving medication, and patient status tracking assisted by embedded wearable wireless sensors which are integrated in wireless sensor network.

Differential Privacy Preserving in Big Data Analytics for Connected Health.

PubMed

Lin, Chi; Song, Zihao; Song, Houbing; Zhou, Yanhong; Wang, Yi; Wu, Guowei

2016-04-01

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.

Channel-Based Key Generation for Encrypted Body-Worn Wireless Sensor Networks.

PubMed

Van Torre, Patrick

2016-09-08

Body-worn sensor networks are important for rescue-workers, medical and many other applications. Sensitive data are often transmitted over such a network, motivating the need for encryption. Body-worn sensor networks are deployed in conditions where the wireless communication channel varies dramatically due to fading and shadowing, which is considered a disadvantage for communication. Interestingly, these channel variations can be employed to extract a common encryption key at both sides of the link. Legitimate users share a unique physical channel and the variations thereof provide data series on both sides of the link, with highly correlated values. An eavesdropper, however, does not share this physical channel and cannot extract the same information when intercepting the signals. This paper documents a practical wearable communication system implementing channel-based key generation, including an implementation and a measurement campaign comprising indoor as well as outdoor measurements. The results provide insight into the performance of channel-based key generation in realistic practical conditions. Employing a process known as key reconciliation, error free keys are generated in all tested scenarios. The key-generation system is computationally simple and therefore compatible with the low-power micro controllers and low-data rate transmissions commonly used in wireless sensor networks.

Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period.

PubMed

Profit, Deborah; Rohatagi, Shashank; Zhao, Cathy; Hatch, Ainslie; Docherty, John P; Peters-Strickland, Timothy S

2016-09-01

A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development. Two sequential substudies enrolled 30 and 29 healthy volunteers between March-April 2014 and February-March 2015, respectively, to assess detection accuracy of the ingestible sensor by the DMS and the latency period between ingestion and detection of the ingestion by the wearable sensor or the cloud-based server. The first substudy identified areas for improvement using early versions of the wearable sensor and the mobile application. The second substudy tested updated versions of the components and showed an overall ingestion detection rate of 96.6%. Mean latency times for the signal transmission were 1.1-1.3 minutes (from ingestion to the wearable sensor detection) and 6.2-10.3 minutes (from the wearable sensor detection to the server detection). Half of transmissions were completed in < 2 minutes, and ~90% of ingestions were registered by the smartphone within 30 minutes of ingestion. No serious adverse events, discontinuations, or clinically significant laboratory/vital signs findings were reported. The DMS implementing modified versions of the smartphone application and the wearable sensor has the technical capability to detect and report tablet ingestion with high accuracy and acceptable latency time. ClinicalTrials.gov identifier: NCT02091882. © Copyright 2016 Physicians Postgraduate Press, Inc.

Physiological parameters monitoring of fire-fighters by means of a wearable wireless sensor system

NASA Astrophysics Data System (ADS)

Stelios, M.; Mitilineos, Stelios A.; Chatzistamatis, Panagiotis; Vassiliadis, Savvas; Primentas, Antonios; Kogias, Dimitris; Michailidis, Emmanouel T.; Rangoussi, Maria; Kurşun Bahadir, Senem; Atalay, Özgür; Kalaoğlu, Fatma; Sağlam, Yusuf

2016-03-01

Physiological parameter monitoring may be useful in many different groups of the population, such as infants, elderly people, athletes, soldiers, drivers, fire-fighters, police etc. This can provide a variety of information ranging from health status to operational readiness. In this article, we focus on the case of first responders and specifically fire-fighters. Firefighters can benefit from a physiological monitoring system that is used to extract multiple indications such as the present position, the possible life risk level, the stress level etc. This work presents a wearable wireless sensor network node, based on low cost, commercial-off- the-self (COTS) electronic modules, which can be easily attached on a standard fire-fighters’ uniform. Due to the low frequency wired interface between the selected electronic components, the proposed solution can be used as a basis for a textile system where all wired connections will be implemented by means of conductive yarn routing in the textile structure, while some of the standard sensors can be replaced by textile ones. System architecture is described in detail, while indicative samples of acquired signals are also presented.

Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.

PubMed

Gaura, Elena; Kemp, John; Brusey, James

2013-12-01

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.

Soft bio-integrated systems for continuous health monitoring

NASA Astrophysics Data System (ADS)

Raj, M.; Wei, P. H.; Morey, B.; Wang, X.; Keen, B.; DePetrillo, P.; Hsu, Y. Y.; Ghaffari, R.

2014-06-01

Electronically-enabled wearable systems that monitor physiological activity and electrophysiological activity hold the key to truly personalized medical care outside of the hospital setting. However, fundamental technical challenges exist in achieving medical systems that are comfortable, unobtrusive and fully integrated without external connections to bench top instruments. In particular, there is a fundamental mismatch in mechanical coupling between existing classes of rigid electronics and soft biological substrates, like the skin. Here we describe new mechanical and electrical design strategies for wearable devices with mechanical properties that approach that of biological tissue. These systems exploit stretchable networks of conformal sensors (i.e. electrodes, temperature sensors, and accelerometers) and associated circuitry (i.e. microcontroller, memory, voltage regulators, rechargeable battery, wireless communication modules) embedded in ultrathin, elastomeric substrates. Quantitative analyses of sensor performance and mechanics under tensile and torsional stresses illustrate the ability to mechanically couple with soft tissues in a way that is mechanically invisible to the user. Representative examples of these soft biointegrated systems can be applied for continuous sensing of muscle and movement activity in the home and ambulatory settings.

User-Wearable Devices that Monitor Exposure to Blue Light and Recommend Adjustments Thereto

NASA Technical Reports Server (NTRS)

Lee, Yong Jin (Inventor)

2017-01-01

Described herein are user-wearable devices that include an optical sensor, and methods for use therewith. In certain embodiments, an optical sensor of a user-wearable device (e.g., a wrist-worn device) is used to detect blue light that is incident on the optical sensor and to produce a blue light detection signal indicative thereof, and thus, indicative of the response of the user's intrinsically photosensitive Retinal Ganglion Cells (ipRGCs). In dependence on the blue light detection signal, there is a determination of a metric indicative of an amount of blue light detected by the optical sensor. The metric is compared to a corresponding threshold, and a user notification is triggered in dependence on results of the comparing, wherein the user notification informs a person wearing the user-wearable device to adjust their exposure to light.

Harvesting electrostatic energy using super-hydrophobic surfaces

NASA Astrophysics Data System (ADS)

Pociecha, Dominik; Zylka, Pawel

2016-11-01

Almost all environments are now being extensively populated by miniaturized, nano-powered electronic sensor devices communicated together through wireless sensor networks building Internet of Things (IoT). Various energy harvesting techniques are being more and more frequently proposed for battery-less powering of such remote, unattended, implantable or wearable sensors or other low-power electronic gadgets. Energy harvesting relays on extracting energy from the ambient sources readily accessible at the sensor location and converting it into electrical power. The paper exploits possibility of generating electric energy safely accessible for nano-power electronics using tribo-electric and electrostatic induction phenomena displayed at super-hydrophobic surfaces impinged by water droplets. Mechanism of such interaction is discussed and illustrated by experimental results.

Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity

PubMed Central

2018-01-01

Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. PMID:29712629

Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey

PubMed Central

Kumar, Sunil; Kambhatla, Kashyap; Hu, Fei; Lifson, Mark; Xiao, Yang

2008-01-01

New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation. PMID:18604301

Ubiquitous computing for remote cardiac patient monitoring: a survey.

PubMed

Kumar, Sunil; Kambhatla, Kashyap; Hu, Fei; Lifson, Mark; Xiao, Yang

2008-01-01

New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation.

System perspectives for mobile platform design in m-Health

NASA Astrophysics Data System (ADS)

Roveda, Janet M.; Fink, Wolfgang

2016-05-01

Advances in integrated circuit technologies have led to the integration of medical sensor front ends with data processing circuits, i.e., mobile platform design for wearable sensors. We discuss design methodologies for wearable sensor nodes and their applications in m-Health. From the user perspective, flexibility, comfort, appearance, fashion, ease-of-use, and visibility are key form factors. From the technology development point of view, high accuracy, low power consumption, and high signal to noise ratio are desirable features. From the embedded software design standpoint, real time data analysis algorithms, application and database interfaces are the critical components to create successful wearable sensor-based products.

RGO-coated elastic fibres as wearable strain sensors for full-scale detection of human motions

NASA Astrophysics Data System (ADS)

Mi, Qing; Wang, Qi; Zang, Siyao; Mao, Guoming; Zhang, Jinnan; Ren, Xiaomin

2018-01-01

In this study, we chose highly-elastic fabric fibres as the functional carrier and then simply coated the fibres with reduced graphene oxide (rGO) using plasma treatment, dip coating and hydrothermal reduction steps, finally making a wearable strain sensor. As a result, the full-scale detection of human motions, ranging from bending joints to the pulse beat, has been achieved by these sensors. Moreover, high sensitivity, good stability and excellent repeatability were realized. The good sensing performances and economical fabrication process of this wearable strain sensor have strengthened our confidence in practical applications in smart clothing, smart fabrics, healthcare, and entertainment fields.

An integrated biomedical telemetry system for sleep monitoring employing a portable body area network of sensors (SENSATION).

PubMed

Astaras, Alexander; Arvanitidou, Marina; Chouvarda, Ioanna; Kilintzis, Vassilis; Koutkias, Vassilis; Sanchez, Eduardo Monton; Stalidis, George; Triantafyllidis, Andreas; Maglaveras, Nicos

2008-01-01

A flexible, scaleable and cost-effective medical telemetry system is described for monitoring sleep-related disorders in the home environment. The system was designed and built for real-time data acquisition and processing, allowing for additional use in intensive care unit scenarios where rapid medical response is required in case of emergency. It comprises a wearable body area network of Zigbee-compatible wireless sensors worn by the subject, a central database repository residing in the medical centre and thin client workstations located at the subject's home and in the clinician's office. The system supports heterogeneous setup configurations, involving a variety of data acquisition sensors to suit several medical applications. All telemetry data is securely transferred and stored in the central database under the clinicians' ownership and control.

A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.

PubMed

Li, Yuecheng; Jia, Wenyan; Yu, Tianjian; Luan, Bo; Mao, Zhi-Hong; Zhang, Hong; Sun, Mingui

2015-04-01

In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.

Sulfophenyl-Functionalized Reduced Graphene Oxide Networks on Electrospun 3D Scaffold for Ultrasensitive NO₂ Gas Sensor.

PubMed

Zou, Bin; Guo, Yunlong; Shen, Nannan; Xiao, Anshan; Li, Mingjun; Zhu, Liang; Wan, Pengbo; Sun, Xiaoming

2017-12-19

Ultrasensitive room temperature real-time NO₂ sensors are highly desirable due to potential threats on environmental security and personal respiratory. Traditional NO₂ gas sensors with highly operated temperatures (200-600 °C) and limited reversibility are mainly constructed from semiconducting oxide-deposited ceramic tubes or inter-finger probes. Herein, we report the functionalized graphene network film sensors assembled on an electrospun three-dimensional (3D) nanonetwork skeleton for ultrasensitive NO₂ sensing. The functional 3D scaffold was prepared by electrospinning interconnected polyacrylonitrile (PAN) nanofibers onto a nylon window screen to provide a 3D nanonetwork skeleton. Then, the sulfophenyl-functionalized reduced graphene oxide (SFRGO) was assembled on the electrospun 3D nanonetwork skeleton to form SFRGO network films. The assembled functionalized graphene network film sensors exhibit excellent NO₂ sensing performance (10 ppb to 20 ppm) at room temperature, reliable reversibility, good selectivity, and better sensing cycle stability. These improvements can be ascribed to the functionalization of graphene with electron-withdrawing sulfophenyl groups, the high surface-to-volume ratio, and the effective sensing channels from SFRGO wrapping onto the interconnected 3D scaffold. The SFRGO network-sensing film has the advantages of simple preparation, low cost, good processability, and ultrasensitive NO₂ sensing, all advantages that can be utilized for potential integration into smart windows and wearable electronic devices for real-time household gas sensors.

Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring.

PubMed

Park, Jung Jin; Hyun, Woo Jin; Mun, Sung Cik; Park, Yong Tae; Park, O Ok

2015-03-25

Because of their outstanding electrical and mechanical properties, graphene strain sensors have attracted extensive attention for electronic applications in virtual reality, robotics, medical diagnostics, and healthcare. Although several strain sensors based on graphene have been reported, the stretchability and sensitivity of these sensors remain limited, and also there is a pressing need to develop a practical fabrication process. This paper reports the fabrication and characterization of new types of graphene strain sensors based on stretchable yarns. Highly stretchable, sensitive, and wearable sensors are realized by a layer-by-layer assembly method that is simple, low-cost, scalable, and solution-processable. Because of the yarn structures, these sensors exhibit high stretchability (up to 150%) and versatility, and can detect both large- and small-scale human motions. For this study, wearable electronics are fabricated with implanted sensors that can monitor diverse human motions, including joint movement, phonation, swallowing, and breathing.

Flexible piezoelectric nanogenerator in wearable self-powered active sensor for respiration and healthcare monitoring

NASA Astrophysics Data System (ADS)

Liu, Z.; Zhang, S.; Jin, Y. M.; Ouyang, H.; Zou, Y.; Wang, X. X.; Xie, L. X.; Li, Z.

2017-06-01

A wearable self-powered active sensor for respiration and healthcare monitoring was fabricated based on a flexible piezoelectric nanogenerator. An electrospinning poly(vinylidene fluoride) thin film on silicone substrate was polarized to fabricate the flexible nanogenerator and its electrical property was measured. When periodically stretched by a linear motor, the flexible piezoelectric nanogenerator generated an output open-circuit voltage and short-circuit current of up to 1.5 V and 400 nA, respectively. Through integration with an elastic bandage, a wearable self-powered sensor was fabricated and used to monitor human respiration, subtle muscle movement, and voice recognition. As respiration proceeded, the electrical output signals of the sensor corresponded to the signals measured by a physiological signal recording system with good reliability and feasibility. This self-powered, wearable active sensor has significant potential for applications in pulmonary function evaluation, respiratory monitoring, and detection of gesture and vocal cord vibration for the personal healthcare monitoring of disabled or paralyzed patients.

Gait Partitioning Methods: A Systematic Review

PubMed Central

Taborri, Juri; Palermo, Eduardo; Rossi, Stefano; Cappa, Paolo

2016-01-01

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments. PMID:26751449

Gait Partitioning Methods: A Systematic Review.

PubMed

Taborri, Juri; Palermo, Eduardo; Rossi, Stefano; Cappa, Paolo

2016-01-06

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments.

A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

PubMed Central

Yang, Che-Chang; Hsu, Yeh-Liang

2010-01-01

Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies. PMID:22163626

Wearable Sensors in Transportation - Exploratory Advanced Research Program Initial Stage Investigation

DOT National Transportation Integrated Search

2016-03-01

This report summarizes an initial stage investigation into wearable sensors for transportation research : applications. The Federal Highway Administration (FHWA) has observed significant activity in this area and : seeks to obtain an understanding of...

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare.

PubMed

Bai, Jinwei; Shen, Li; Sun, Huimin; Shen, Bairong

2017-01-01

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

Bioinspired Composite Microfibers for Skin Adhesion and Signal Amplification of Wearable Sensors.

PubMed

Drotlef, Dirk-M; Amjadi, Morteza; Yunusa, Muhammad; Sitti, Metin

2017-07-01

A facile approach is proposed for superior conformation and adhesion of wearable sensors to dry and wet skin. Bioinspired skin-adhesive films are composed of elastomeric microfibers decorated with conformal and mushroom-shaped vinylsiloxane tips. Strong skin adhesion is achieved by crosslinking the viscous vinylsiloxane tips directly on the skin surface. Furthermore, composite microfibrillar adhesive films possess a high adhesion strength of 18 kPa due to the excellent shape adaptation of the vinylsiloxane tips to the multiscale roughness of the skin. As a utility of the skin-adhesive films in wearable-device applications, they are integrated with wearable strain sensors for respiratory and heart-rate monitoring. The signal-to-noise ratio of the strain sensor is significantly improved to 59.7 because of the considerable signal amplification of microfibrillar skin-adhesive films. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany

PubMed Central

Wang, Tianqi; Ramnarayanan, Ashwin

2017-01-01

The growing demand for real time analysis of bioanalytes has spurred development in the field of wearable technology to offer non-invasive data collection at a low cost. The manufacturing processes for creating these sensing systems vary significantly by the material used, the type of sensors needed and the subject of study as well. The methods predominantly involve stretchable electronic sensors to monitor targets and transmit data mainly through flexible wires or short-range wireless communication devices. Capable of conformal contact, the application of wearable technology goes beyond the healthcare to fields of food, zoology and botany. With a brief review of wearable technology and its applications to various fields, we believe this mini review would be of interest to the reader in broad fields of materials, sensor development and areas where wearable sensors can provide data that are not available elsewhere. PMID:29267256

Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany.

PubMed

Wang, Tianqi; Ramnarayanan, Ashwin; Cheng, Huanyu

2017-12-21

The growing demand for real time analysis of bioanalytes has spurred development in the field of wearable technology to offer non-invasive data collection at a low cost. The manufacturing processes for creating these sensing systems vary significantly by the material used, the type of sensors needed and the subject of study as well. The methods predominantly involve stretchable electronic sensors to monitor targets and transmit data mainly through flexible wires or short-range wireless communication devices. Capable of conformal contact, the application of wearable technology goes beyond the healthcare to fields of food, zoology and botany. With a brief review of wearable technology and its applications to various fields, we believe this mini review would be of interest to the reader in broad fields of materials, sensor development and areas where wearable sensors can provide data that are not available elsewhere.

Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoringand Personal Healthcare.

PubMed

Trung, Tran Quang; Lee, Nae-Eung

2016-06-01

Flexible and stretchable physical sensors that can measure and quantify electrical signals generated by human activities are attracting a great deal of attention as they have unique characteristics, such as ultrathinness, low modulus, light weight, high flexibility, and stretchability. These flexible and stretchable physical sensors conformally attached on the surface of organs or skin can provide a new opportunity for human-activity monitoring and personal healthcare. Consequently, in recent years there has been considerable research effort devoted to the development of flexible and stretchable physical sensors to fulfill the requirements of future technology, and much progress has been achieved. Here, the most recent developments of flexible and stretchable physical sensors are described, including temperature, pressure, and strain sensors, and flexible and stretchable sensor-integrated platforms. The latest successful examples of flexible and stretchable physical sensors for the detection of temperature, pressure, and strain, as well as their novel structures, technological innovations, and challenges, are reviewed first. In the next section, recent progress regarding sensor-integrated wearable platforms is overviewed in detail. Some of the latest achievements regarding self-powered sensor-integrated wearable platform technologies are also reviewed. Further research direction and challenges are also proposed to develop a fully sensor-integrated wearable platform for monitoring human activity and personal healthcare in the near future. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wearable sensor glove based on conducting fabric using electrodermal activity and pulse-wave sensors for e-health application.

PubMed

Lee, Youngbum; Lee, Byungwoo; Lee, Myoungho

2010-03-01

Improvement of the quality and efficiency of health in medicine, both at home and the hospital, calls for improved sensors that might be included in a common carrier such as a wearable sensor device to measure various biosignals and provide healthcare services that use e-health technology. Designed to be user-friendly, smart clothes and gloves respond well to the end users for health monitoring. This study describes a wearable sensor glove that is equipped with an electrodermal activity (EDA) sensor, pulse-wave sensor, conducting fabric, and an embedded system. The EDA sensor utilizes the relationship between drowsiness and the EDA signal. The EDA sensors were made using a conducting fabric instead of silver chloride electrodes, as a more practical and practically wearable device. The pulse-wave sensor measurement system, which is widely applied in oriental medicinal practices, is also a strong element in e-health monitoring systems. The EDA and pulse-wave signal acquisition module was constructed by connecting the sensor to the glove via a conductive fabric. The signal acquisition module is then connected to a personal computer that displays the results of the EDA and pulse-wave signal processing analysis and gives accurate feedback to the user. This system is designed for a number of applications for the e-health services, including drowsiness detection and oriental medicine.

Tattoolike Polyaniline Microparticle-Doped Gold Nanowire Patches as Highly Durable Wearable Sensors.

PubMed

Gong, Shu; Lai, Daniel T H; Wang, Yan; Yap, Lim Wei; Si, Kae Jye; Shi, Qianqian; Jason, Naveen Noah; Sridhar, Tam; Uddin, Hemayet; Cheng, Wenlong

2015-09-09

Wearable and highly sensitive strain sensors are essential components of electronic skin for future biomonitoring and human machine interfaces. Here we report a low-cost yet efficient strategy to dope polyaniline microparticles into gold nanowire (AuNW) films, leading to 10 times enhancement in conductivity and ∼8 times improvement in sensitivity. Simultaneously, tattoolike wearable sensors could be fabricated simply by a direct "draw-on" strategy with a Chinese penbrush. The stretchability of the sensors could be enhanced from 99.7% to 149.6% by designing curved tattoo with different radius of curvatures. We also demonstrated roller coating method to encapusulate AuNWs sensors, exhibiting excellent water resistibility and durability. Because of improved conductivity of our sensors, they can directly interface with existing wireless circuitry, allowing for fabrication of wireless flexion sensors for a human finger-controlled robotic arm system.

Graphene-Based Three-Dimensional Capacitive Touch Sensor for Wearable Electronics.

PubMed

Kang, Minpyo; Kim, Jejung; Jang, Bongkyun; Chae, Youngcheol; Kim, Jae-Hyun; Ahn, Jong-Hyun

2017-08-22

The development of input device technology in a conformal and stretchable format is important for the advancement of various wearable electronics. Herein, we report a capacitive touch sensor with good sensing capabilities in both contact and noncontact modes, enabled by the use of graphene and a thin device geometry. This device can be integrated with highly deformable areas of the human body, such as the forearms and palms. This touch sensor detects multiple touch signals in acute recordings and recognizes the distance and shape of the approaching objects before direct contact is made. This technology offers a convenient and immersive human-machine interface and additional potential utility as a multifunctional sensor for emerging wearable electronics and robotics.

A Printed Organic Amplification System for Wearable Potentiometric Electrochemical Sensors.

PubMed

Shiwaku, Rei; Matsui, Hiroyuki; Nagamine, Kuniaki; Uematsu, Mayu; Mano, Taisei; Maruyama, Yuki; Nomura, Ayako; Tsuchiya, Kazuhiko; Hayasaka, Kazuma; Takeda, Yasunori; Fukuda, Takashi; Kumaki, Daisuke; Tokito, Shizuo

2018-03-02

Electrochemical sensor systems with integrated amplifier circuits play an important role in measuring physiological signals via in situ human perspiration analysis. Signal processing circuitry based on organic thin-film transistors (OTFTs) have significant potential in realizing wearable sensor devices due to their superior mechanical flexibility and biocompatibility. Here, we demonstrate a novel potentiometric electrochemical sensing system comprised of a potassium ion (K + ) sensor and amplifier circuits employing OTFT-based pseudo-CMOS inverters, which have a highly controllable switching voltage and closed-loop gain. The ion concentration sensitivity of the fabricated K + sensor was 34 mV/dec, which was amplified to 160 mV/dec (by a factor of 4.6) with high linearity. The developed system is expected to help further the realization of ultra-thin and flexible wearable sensor devices for healthcare applications.

A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone.

PubMed

Miao, Fen; Cheng, Yayu; He, Yi; He, Qingyun; Li, Ye

2015-05-19

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user's physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.

Highly Stretchable Core-Sheath Fibers via Wet-Spinning for Wearable Strain Sensors.

PubMed

Tang, Zhenhua; Jia, Shuhai; Wang, Fei; Bian, Changsheng; Chen, Yuyu; Wang, Yonglin; Li, Bo

2018-02-21

Lightweight, stretchable, and wearable strain sensors have recently been widely studied for the development of health monitoring systems, human-machine interfaces, and wearable devices. Herein, highly stretchable polymer elastomer-wrapped carbon nanocomposite piezoresistive core-sheath fibers are successfully prepared using a facile and scalable one-step coaxial wet-spinning assembly approach. The carbon nanotube-polymeric composite core of the stretchable fiber is surrounded by an insulating sheath, similar to conventional cables, and shows excellent electrical conductivity with a low percolation threshold (0.74 vol %). The core-sheath elastic fibers are used as wearable strain sensors, exhibiting ultra-high stretchability (above 300%), excellent stability (>10 000 cycles), fast response, low hysteresis, and good washability. Furthermore, the piezoresistive core-sheath fiber possesses bending-insensitiveness and negligible torsion-sensitive properties, and the strain sensing performance of piezoresistive fibers maintains a high degree of stability under harsh conditions. On the basis of this high level of performance, the fiber-shaped strain sensor can accurately detect both subtle and large-scale human movements by embedding it in gloves and garments or by directly attaching it to the skin. The current results indicate that the proposed stretchable strain sensor has many potential applications in health monitoring, human-machine interfaces, soft robotics, and wearable electronics.

Self-Powered Wearable Electronics Based on Moisture Enabled Electricity Generation.

PubMed

Shen, Daozhi; Xiao, Ming; Zou, Guisheng; Liu, Lei; Duley, Walter W; Zhou, Y Norman

2018-05-01

Most state-of-the-art electronic wearable sensors are powered by batteries that require regular charging and eventual replacement, which would cause environmental issues and complex management problems. Here, a device concept is reported that can break this paradigm in ambient moisture monitoring-a new class of simple sensors themselves can generate moisture-dependent voltage that can be used to determine the ambient humidity level directly. It is demonstrated that a moisture-driven electrical generator, based on the diffusive flow of water in titanium dioxide (TiO 2 ) nanowire networks, can yield an output power density of up to 4 µW cm -2 when exposed to a highly moist environment. This performance is two orders of magnitude better than that reported for carbon-black generators. The output voltage is strongly dependent on humidity of ambient environment. As a big breakthrough, this new type of device is successfully used as self-powered wearable human-breathing monitors and touch pads, which is not achievable by any existing moisture-induced-electricity technology. The availability of high-output self-powered electrical generators will facilitate the design and application of a wide range of new innovative flexible electronic devices. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cybercare 2.0: meeting the challenge of the global burden of disease in 2030.

PubMed

Rosen, Joseph M; Kun, Luis; Mosher, Robyn E; Grigg, Elliott; Merrell, Ronald C; Macedonia, Christian; Klaudt-Moreau, Julien; Price-Smith, Andrew; Geiling, James

In this paper, we propose to advance and transform today's healthcare system using a model of networked health care called Cybercare. Cybercare means "health care in cyberspace" - for example, doctors consulting with patients via videoconferencing across a distributed network; or patients receiving care locally - in neighborhoods, "minute clinics," and homes - using information technologies such as telemedicine, smartphones, and wearable sensors to link to tertiary medical specialists. This model contrasts with traditional health care, in which patients travel (often a great distance) to receive care from providers in a central hospital. The Cybercare model shifts health care provision from hospital to home; from specialist to generalist; and from treatment to prevention. Cybercare employs advanced technology to deliver services efficiently across the distributed network - for example, using telemedicine, wearable sensors and cell phones to link patients to specialists and upload their medical data in near-real time; using information technology (IT) to rapidly detect, track, and contain the spread of a global pandemic; or using cell phones to manage medical care in a disaster situation. Cybercare uses seven "pillars" of technology to provide medical care: genomics; telemedicine; robotics; simulation, including virtual and augmented reality; artificial intelligence (AI), including intelligent agents; the electronic medical record (EMR); and smartphones. All these technologies are evolving and blending. The technologies are integrated functionally because they underlie the Cybercare network, and/or form part of the care for patients using that distributed network. Moving health care provision to a networked, distributed model will save money, improve outcomes, facilitate access, improve security, increase patient and provider satisfaction, and may mitigate the international global burden of disease. In this paper we discuss how Cybercare is being implemented now, and envision its growth by 2030.

Secure Publish-Subscribe Protocols for Heterogeneous Medical Wireless Body Area Networks

PubMed Central

Picazo-Sanchez, Pablo; Tapiador, Juan E.; Peris-Lopez, Pedro; Suarez-Tangil, Guillermo

2014-01-01

Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we describe a WBAN architecture based on the well-known publish-subscribe paradigm. We present two protocols for publishing data and sending commands to a sensor that guarantee confidentiality and fine-grained access control. Both protocols are based on a recently proposed ciphertext policy attribute-based encryption (CP-ABE) scheme that is lightweight enough to be embedded into wearable sensors. We show how sensors can implement lattice-based access control (LBAC) policies using this scheme, which are highly appropriate for the eHealth domain. We report experimental results with a prototype implementation demonstrating the suitability of our proposed solution. PMID:25460814

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE.

PubMed

Adibi, Sasan

2014-01-01

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Soft Sensors and Actuators based on Nanomaterials

NASA Astrophysics Data System (ADS)

Yao, Shanshan

The focus of this research is using novel bottom-up synthesized nanomaterials and structures to build up devices for wearable sensors and soft actuators. The applications of the wearable sensors towards motion detection and health monitoring are investigated. In addition, flexible heaters for bimorph actuators and stretchable patches made of microgel depots containing drug-loaded nanoparticles (NPs) for stretch-triggered wearable drug delivery are studied. Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to 1 MPa) and finger touch with good sensitivity, fast response time ( 40 ms) and good pressure mapping function were developed. The sensors were demonstrated for several wearable applications including monitoring thumb movements and knee motions, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels. In addition to mechanical sensors, a wearable skin hydration sensor made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix was demonstrated based on skin impedance measurement. The hydration sensors were packaged into a flexible wristband for skin hydration monitoring and a chest patch consisting of a strain sensor, three electrocardiogram (ECG) electrodes and a skin hydration sensor for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless and continuous sensing of skin hydration and other health parameters. Two representative applications of the nanomaterials for soft actuators were investigated. In the first application on bimorph actuation, low-voltage and extremely flexible electrothermal bimorph actuators were fabricated in a simple, efficient and scalable process. The bimorph actuators were made of flexible AgNW based heaters, which exhibited a fast heating rate of 18°C/s and stable heating performance under large bending. The actuators offered the largest bending angle (720°) or curvature (2.6 cm-1) at a very low actuation voltage (0.2 V sq-1 or 4.5 V) among all types of bimorph actuators that have been reported. The actuators can be designed and fabricated in different configurations that can achieve complex patterns and shapes upon actuation. Two applications of this type of soft actuators were demonstrated towards biomimetic robotics - a crawling robot that can walk spontaneously on ratchet surfaces and a soft gripper that is capable of manipulating lightweight and delicate objects. In another application towards wearable drug delivery, a wearable, tensile strain-triggered drug delivery device consisting of a stretchable elastomer and microgel depots containing drug loaded nanoparticles is described. By applying a tensile strain to the elastomer film, the release of drug from the micro-depot is promoted. Correspondingly, both sustained drug release by daily body motions and pulsatile release by intentional administration can be conveniently achieved. The work demonstrated that the tensile strain, applied to the stretchable device, facilitated release of therapeutics from micro-depots for anticancer and antibacterial treatments, respectively. Moreover, polymeric microneedles were further integrated with the stretch-responsive device for transcutaneous delivery of insulin and regulation of blood glucose levels of chemically-induced type 1 diabetic mice.

A wearable sensor system for lower-limb rehabilitation evaluation using the GRF and CoP distributions

NASA Astrophysics Data System (ADS)

Tao, Weijun; Zhang, Jianyun; Li, Guangyi; Liu, Tao; Liu, Fengping; Yi, Jingang; Wang, Hesheng; Inoue, Yoshio

2016-02-01

Wearable sensors are attractive for gait analysis because these systems can measure and obtain real-time human gait and motion information outside of the laboratory for a longer duration. In this paper, we present a new wearable ground reaction force (GRF) sensing system for ambulatory gait measurement. In addition, the GRF sensor system is also used to quantify the patients' lower-limb gait rehabilitation. We conduct a validation experiment for the sensor system on seven volunteer subjects (weight 62.39 +/- 9.69 kg and height 169.13 +/- 5.64 cm). The experiments include the use of the GRF sensing system for the subjects in the following conditions: (1) normal walking; (2) walking with the rehabilitation training device; and (3) walking with a knee brace and the rehabilitation training device. The experiment results support the hypothesis that the wearable GRF sensor system is capable of quantifying patients' lower-limb rehabilitation. The proposed GRF sensing system can also be used for assessing the effectiveness of a gait rehabilitation system and for providing bio-feedback information to the subjects.

Monitoring stage fright outside the laboratory: an example in a professional musician using wearable sensors.

PubMed

Kusserow, Martin; Candia, Victor; Amft, Oliver; Hildebrandt, Horst; Folkers, Gerd; Tröster, Gerhard

2012-03-01

We implemented and tested a wearable sensor system to measure patterns of stress responses in a professional musician under public performance conditions. Using this sensor system, we monitored the cellist's heart activity, the motion of multiple body parts, and their gradual changes during three repeated performances of a skill-demanding piece in front of a professional audience. From the cellist and her teachers, we collected stage fright self-reports and performance ratings that were related to our sensor data analysis results. Concomitant to changes in body motion and heart rate, the cellist perceived a reduction in stage fright. Performance quality was objectively improved, as technical playing errors decreased throughout repeated renditions. In particular, from performance 1 to 3, the wearable sensors measured a significant increase in the cellist's bowing motion dynamics of approximately 6% and a decrease in heart rate. Bowing motion showed a marginal correlation to the observed heart rate patterns during playing. The wearable system did not interfere with the cellist's performance, thereby allowing investigation of stress responses during natural public performances.

Can energy expenditure be accurately assessed using accelerometry-based wearable motion detectors for physical activity monitoring in post-stroke patients in the subacute phase?

PubMed

Mandigout, Stéphane; Lacroix, Justine; Ferry, Béatrice; Vuillerme, Nicolas; Compagnat, Maxence; Daviet, Jean-Christophe

2017-12-01

Background In the subacute stroke phase, the monitoring of ambulatory activity and activities of daily life with wearable sensors may have relevant clinical applications. Do current commercially available wearable activity trackers allow us to objectively assess the energy expenditure of these activities? The objective of the present study was to compare the energy expenditure evaluated by indirect calorimetry during the course of a scenario consisting of everyday activities while estimating the energy expenditure using several commercialised wearable sensors in post-stroke patients (less than six months since stroke). Method Twenty-four patients (age 68.2 ± 13.9; post-stroke delay 34 ± 25 days) voluntarily participated in this study. Each patient underwent a scenario of various everyday tasks (transfer, walking, etc.). During the implementation, patients wore 14 wearable sensors (Armband, Actigraph GT3X, Actical, pedometer) to obtain an estimate of the energy expenditure. The actual energy expenditure was concurrently determined by indirect calorimetry. Results Except for the Armband worn on the non-plegic side, the results of our study show a significant difference between the energy expenditure values estimated by the various sensors and the actual energy expenditure when the scenario is considered as a whole. Conclusion The present results suggest that, for a series of everyday tasks, the wearable sensors underestimate the actual energy expenditure values in post-stroke patients in the subacute phase and are therefore not accurate. Several factors are likely to confound the results: types of activity, prediction equations, the position of the sensor and the hemiplegia side.

Design, fabrication and metrological evaluation of wearable pressure sensors.

PubMed

Goy, C B; Menichetti, V; Yanicelli, L M; Lucero, J B; López, M A Gómez; Parodi, N F; Herrera, M C

2015-04-01

Pressure sensors are valuable transducers that are necessary in a huge number of medical application. However, the state of the art of compact and lightweight pressure sensors with the capability of measuring the contact pressure between two surfaces (contact pressure sensors) is very poor. In this work, several types of wearable contact pressure sensors are fabricated using different conductive textile materials and piezo-resistive films. The fabricated sensors differ in size, the textile conductor used and/or the number of layers of the sandwiched piezo-resistive film. The intention is to study, through the obtaining of their calibration curves, their metrological properties (repeatability, sensitivity and range) and determine which physical characteristics improve their ability for measuring contact pressures. It has been found that it is possible to obtain wearable contact pressure sensors through the proposed fabrication process with satisfactory repeatability, range and sensitivity; and that some of these properties can be improved by the physical characteristics of the sensors.

Smart wearable body sensors for patient self-assessment and monitoring.

PubMed

Appelboom, Geoff; Camacho, Elvis; Abraham, Mickey E; Bruce, Samuel S; Dumont, Emmanuel Lp; Zacharia, Brad E; D'Amico, Randy; Slomian, Justin; Reginster, Jean Yves; Bruyère, Olivier; Connolly, E Sander

2014-01-01

Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors. We reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians. Smart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine. Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.

Extraordinarily Stretchable All-Carbon Collaborative Nanoarchitectures for Epidermal Sensors.

PubMed

Cai, Yichen; Shen, Jie; Dai, Ziyang; Zang, Xiaoxian; Dong, Qiuchun; Guan, Guofeng; Li, Lain-Jong; Huang, Wei; Dong, Xiaochen

2017-08-01

Multifunctional microelectronic components featuring large stretchability, high sensitivity, high signal-to-noise ratio (SNR), and broad sensing range have attracted a huge surge of interest with the fast developing epidermal electronic systems. Here, the epidermal sensors based on all-carbon collaborative percolation network are demonstrated, which consist 3D graphene foam and carbon nanotubes (CNTs) obtained by two-step chemical vapor deposition processes. The nanoscaled CNT networks largely enhance the stretchability and SNR of the 3D microarchitectural graphene foams, endowing the strain sensor with a gauge factor as high as 35, a wide reliable sensing range up to 85%, and excellent cyclic stability (>5000 cycles). The flexible and reversible strain sensor can be easily mounted on human skin as a wearable electronic device for real-time and high accuracy detecting of electrophysiological stimuli and even for acoustic vibration recognition. The rationally designed all-carbon nanoarchitectures are scalable, low cost, and promising in practical applications requiring extraordinary stretchability and ultrahigh SNRs. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Toward wearable sensors: optical sensor for detection of ammonium nitrate-based explosives, ANFO and ANNM.

PubMed

Sheykhi, Sara; Mosca, Lorenzo; Anzenbacher, Pavel

2017-05-04

Increasing security needs require compact and portable detection tools for the rapid and reliable identification of explosives used in improvised explosive devices (IEDs). We report of an easy-to-use optical sensor for both vapour-phase and solution-phase identification of explosive mixtures that uses a cross-reactive fluorimetric sensor array comprising chemically responsive fluorimetric indicators composed of aromatic aldehydes and polyethyleneimine. Ammonium nitrate-nitromethane (ANNM) was analyzed by paper microzone arrays and nanofiber sensor mats. Progress toward wearable sensors based on electrospun nanofiber mats is outlined.

Interactive wearable systems for upper body rehabilitation: a systematic review.

PubMed

Wang, Qi; Markopoulos, Panos; Yu, Bin; Chen, Wei; Timmermans, Annick

2017-03-11

The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies. A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010-April 2016). Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial. This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring.

PubMed

Servati, Amir; Zou, Liang; Wang, Z Jane; Ko, Frank; Servati, Peyman

2017-07-13

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.

Advancing from offline to online activity recognition with wearable sensors.

PubMed

Ermes, Miikka; Parkka, Juha; Cluitmans, Luc

2008-01-01

Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application.

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

PubMed Central

Servati, Amir; Wang, Z. Jane; Ko, Frank; Servati, Peyman

2017-01-01

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology. PMID:28703744

Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring.

PubMed

Saeedi, Ramyar; Purath, Janet; Venkatasubramanian, Krishna; Ghasemzadeh, Hassan

2014-01-01

Mobile wearable sensors have demonstrated great potential in a broad range of applications in healthcare and wellness. These technologies are known for their potential to revolutionize the way next generation medical services are supplied and consumed by providing more effective interventions, improving health outcomes, and substantially reducing healthcare costs. Despite these potentials, utilization of these sensor devices is currently limited to lab settings and in highly controlled clinical trials. A major obstacle in widespread utilization of these systems is that the sensors need to be used in predefined locations on the body in order to provide accurate outcomes such as type of physical activity performed by the user. This has reduced users' willingness to utilize such technologies. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms for accurate and automatic localization of wearable sensors. Our results based on real data collected using wearable motion sensors demonstrate that the proposed approach can perform sensor localization with 98.4% accuracy which is 30.7% more accurate than an approach without a feature selection mechanism. Furthermore, utilizing our node localization algorithm aids the activity recognition algorithm to achieve 98.8% accuracy (an increase from 33.6% for the system without node localization).

The Baetylus Theorem—the central disconnect driving consumer behavior and investment returns in Wearable Technologies

PubMed Central

Levine, James A.

2016-01-01

The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health. PMID:27617162

The Baetylus Theorem-the central disconnect driving consumer behavior and investment returns in Wearable Technologies.

PubMed

Levine, James A

2016-08-01

The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health.

Wearable multifunctional sensors using printed stretchable conductors made of silver nanowires

NASA Astrophysics Data System (ADS)

Yao, Shanshan; Zhu, Yong

2014-01-01

Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Here we develop highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to ~1.2 MPa) and finger touch with high sensitivity, fast response time (~40 ms) and good pressure mapping function. The reported sensors utilize the capacitive sensing mechanism, where silver nanowires are used as electrodes (conductors) and Ecoflex is used as a dielectric. The silver nanowire electrodes are screen printed. Our sensors have been demonstrated for several wearable applications including monitoring thumb movement, sensing the strain of the knee joint in patellar reflex (knee-jerk) and other human motions such as walking, running and jumping from squatting, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels.Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Here we develop highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to ~1.2 MPa) and finger touch with high sensitivity, fast response time (~40 ms) and good pressure mapping function. The reported sensors utilize the capacitive sensing mechanism, where silver nanowires are used as electrodes (conductors) and Ecoflex is used as a dielectric. The silver nanowire electrodes are screen printed. Our sensors have been demonstrated for several wearable applications including monitoring thumb movement, sensing the strain of the knee joint in patellar reflex (knee-jerk) and other human motions such as walking, running and jumping from squatting, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr05496a

Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions.

PubMed

Yue Deng; Cheng Chen; Tsow, Francis; Xiaojun Xian; Forzani, Erica

2016-08-01

Volatile organic compounds (VOC) are organic chemicals that have high vapor pressure at regular conditions. Some VOC could be dangerous to human health, therefore it is important to determine real-time indoor and outdoor personal exposures to VOC. To achieve this goal, our group has developed a wearable gas monitor with a complete sensor fabrication and calibration protocol for free-living conditions. Correction factors for calibrating the sensors, including sensitivity, aging effect, and temperature effect are implemented into a Quick Response Code (QR code), so that the pre-calibrated quartz tuning fork (QTF) sensor can be used with the wearable monitor under free-living conditions.

Smart electronic yarns and wearable fabrics for human biomonitoring made by carbon nanotube coating with polyelectrolytes.

PubMed

Shim, Bong Sup; Chen, Wei; Doty, Chris; Xu, Chuanlai; Kotov, Nicholas A

2008-12-01

The idea of electronic yarns and textiles has appeared for quite some time, but their properties often do not meet practical expectations. In addition to chemicallmechanical durability and high electrical conductivity, important materials qualifications include weavablity, wearability, light weight, and "smart" functionalities. Here we demonstrate a simple process of transforming general commodity cotton threads into intelligent e-textiles using a polyelectrolyte-based coating with carbon nanotubes (CNTs). Efficient charge transport through the network of nanotubes (20 omega/cm) and the possibility to engineer tunneling junctions make them promising materials for many high-knowledge-content garments. Along with integrated humidity sensing, we demonstrate that CNT-cotton threads can be used to detect albumin, the key protein of blood, with high sensitivity and selectivity. Notwithstanding future challenges, these proof-of-concept demonstrations provide a direct pathway for the application of these materials as wearable biomonitoring and telemedicine sensors, which are simple, sensitive, selective, and versatile.

Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity.

PubMed

Düking, Peter; Fuss, Franz Konstantin; Holmberg, Hans-Christer; Sperlich, Billy

2018-04-30

Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. ©Peter Düking, Franz Konstantin Fuss, Hans-Christer Holmberg, Billy Sperlich. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 30.04.2018.

Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices

PubMed Central

Thurow, Kerstin; Stoll, Regina

2017-01-01

Objectives Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. Methods MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. Results Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. Conclusions Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization. PMID:28261526

Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices.

PubMed

Haghi, Mostafa; Thurow, Kerstin; Stoll, Regina

2017-01-01

Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

PubMed Central

Liu, Yan; Wang, Hai; Zhao, Wei; Qin, Hongbo; Xie, Yongqiang

2018-01-01

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed. PMID:29470408

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features.

PubMed

Liu, Yan; Wang, Hai; Zhao, Wei; Zhang, Min; Qin, Hongbo; Xie, Yongqiang

2018-02-22

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed.

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.

PubMed

Nukala, Bhargava Teja; Nakano, Taro; Rodriguez, Amanda; Tsay, Jerry; Lopez, Jerry; Nguyen, Tam Q; Zupancic, Steven; Lie, Donald Y C

2016-11-29

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k -nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

UHF wearable battery free sensor module for activity and falling detection.

PubMed

Nam Trung Dang; Thang Viet Tran; Wan-Young Chung

2016-08-01

Falling is one of the most serious medical and social problems in aging population. Therefore taking care of the elderly by detecting activity and falling for preventing and mitigating the injuries caused by falls needs to be concerned. This study proposes a wearable, wireless, battery free ultra-high frequency (UHF) smart sensor tag module for falling and activity detection. The proposed tag is powered by UHF RF wave from reader and read by a standard UHF Electronic Product Code (EPC) Class-1 Generation-2 reader. The battery free sensor module could improve the wearability of the wireless device. The combination of accelerometer signal and received signal strength indication (RSSI) from a reader in the passive smart sensor tag detect the activity and falling of the elderly very successfully. The fabricated smart sensor tag module has an operating range of up to 2.5m and conducting in real-time activity and falling detection.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Wearable Wireless Tyrosinase Bandage and Microneedle Sensors: Toward Melanoma Screening.

PubMed

Ciui, Bianca; Martin, Aida; Mishra, Rupesh K; Brunetti, Barbara; Nakagawa, Tatsuo; Dawkins, Thomas J; Lyu, Mengjia; Cristea, Cecilia; Sandulescu, Robert; Wang, Joseph

2018-04-01

Wearable bendable bandage-based sensor and a minimally invasive microneedle biosensor are described toward rapid screening of skin melanoma. These wearable electrochemical sensors are capable of detecting the presence of the tyrosinase (TYR) enzyme cancer biomarker in the presence of its catechol substrate, immobilized on the transducer surface. In the presence of the surface TYR biomarker, the immobilized catechol is rapidly converted to benzoquinone that is detected amperometrically, with a current signal proportional to the TYR level. The flexible epidermal bandage sensor relies on printing stress-enduring inks which display good resiliency against mechanical deformations, whereas the hollow microneedle device is filled with catechol-coated carbon paste for assessing tissue TYR levels. The bandage sensor can thus be used directly on the skin whereas microneedle device can reach melanoma tissues under the skin. Both wearable sensors are interfaced to an ultralight flexible electronic board, which transmits data wirelessly to a mobile device. The analytical performance of the resulting bandage and microneedle sensing systems are evaluated using TYR-containing agarose phantom gel and porcine skin. The new integrated conformal portable sensing platforms hold considerable promise for decentralized melanoma screening, and can be extended to the screening of other key biomarkers in skin moles. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A review of wearable technology in medicine.

PubMed

Iqbal, Mohammed H; Aydin, Abdullatif; Brunckhorst, Oliver; Dasgupta, Prokar; Ahmed, Kamran

2016-10-01

With rapid advances in technology, wearable devices have evolved and been adopted for various uses, ranging from simple devices used in aiding fitness to more complex devices used in assisting surgery. Wearable technology is broadly divided into head-mounted displays and body sensors. A broad search of the current literature revealed a total of 13 different body sensors and 11 head-mounted display devices. The latter have been reported for use in surgery (n = 7), imaging (n = 3), simulation and education (n = 2) and as navigation tools (n = 1). Body sensors have been used as vital signs monitors (n = 9) and for posture-related devices for posture and fitness (n = 4). Body sensors were found to have excellent functionality in aiding patient posture and rehabilitation while head-mounted displays can provide information to surgeons to while maintaining sterility during operative procedures. There is a potential role for head-mounted wearable technology and body sensors in medicine and patient care. However, there is little scientific evidence available proving that the application of such technologies improves patient satisfaction or care. Further studies need to be conducted prior to a clear conclusion. © The Royal Society of Medicine.

Optical sensor array platform based on polymer electronic devices

NASA Astrophysics Data System (ADS)

Koetse, Marc M.; Rensing, Peter A.; Sharpe, Ruben B. A.; van Heck, Gert T.; Allard, Bart A. M.; Meulendijks, Nicole N. M. M.; Kruijt, Peter G. M.; Tijdink, Marcel W. W. J.; De Zwart, René M.; Houben, René J.; Enting, Erik; van Veen, Sjaak J. J. F.; Schoo, Herman F. M.

2007-10-01

Monitoring of personal wellbeing and optimizing human performance are areas where sensors have only begun to be used. One of the reasons for this is the specific demands that these application areas put on the underlying technology and system properties. In many cases these sensors will be integrated in clothing, be worn on the skin, or may even be placed inside the body. This implies that flexibility and wearability of the systems is essential for their success. Devices based on polymer semiconductors allow for these demands since they can be fabricated with thin film technology. The use of thin film device technology allows for the fabrication of very thin sensors (e.g. integrated in food product packaging), flexible or bendable sensors in wearables, large area/distributed sensors, and intrinsically low-cost applications in disposable products. With thin film device technology a high level of integration can be achieved with parts that analyze signals, process and store data, and interact over a network. Integration of all these functions will inherently lead to better cost/performance ratios, especially if printing and other standard polymer technology such as high precision moulding is applied for the fabrication. In this paper we present an optical transmission sensor array based on polymer semiconductor devices made by thin film technology. The organic devices, light emitting diodes, photodiodes and selective medium chip, are integrated with classic electronic components. Together they form a versatile sensor platform that allows for the quantitative measurement of 100 channels and communicates wireless with a computer. The emphasis is given to the sensor principle, the design, fabrication technology and integration of the thin film devices.

Flexible Graphene-Based Wearable Gas and Chemical Sensors.

PubMed

Singh, Eric; Meyyappan, M; Nalwa, Hari Singh

2017-10-11

Wearable electronics is expected to be one of the most active research areas in the next decade; therefore, nanomaterials possessing high carrier mobility, optical transparency, mechanical robustness and flexibility, lightweight, and environmental stability will be in immense demand. Graphene is one of the nanomaterials that fulfill all these requirements, along with other inherently unique properties and convenience to fabricate into different morphological nanostructures, from atomically thin single layers to nanoribbons. Graphene-based materials have also been investigated in sensor technologies, from chemical sensing to detection of cancer biomarkers. The progress of graphene-based flexible gas and chemical sensors in terms of material preparation, sensor fabrication, and their performance are reviewed here. The article provides a brief introduction to graphene-based materials and their potential applications in flexible and stretchable wearable electronic devices. The role of graphene in fabricating flexible gas sensors for the detection of various hazardous gases, including nitrogen dioxide (NO 2 ), ammonia (NH 3 ), hydrogen (H 2 ), hydrogen sulfide (H 2 S), carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), and humidity in wearable technology, is discussed. In addition, applications of graphene-based materials are also summarized in detecting toxic heavy metal ions (Cd, Hg, Pb, Cr, Fe, Ni, Co, Cu, Ag), and volatile organic compounds (VOCs) including nitrobenzene, toluene, acetone, formaldehyde, amines, phenols, bisphenol A (BPA), explosives, chemical warfare agents, and environmental pollutants. The sensitivity, selectivity and strategies for excluding interferents are also discussed for graphene-based gas and chemical sensors. The challenges for developing future generation of flexible and stretchable sensors for wearable technology that would be usable for the Internet of Things (IoT) are also highlighted.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Video Analysis Verification of Head Impact Events Measured by Wearable Sensors.

PubMed

Cortes, Nelson; Lincoln, Andrew E; Myer, Gregory D; Hepburn, Lisa; Higgins, Michael; Putukian, Margot; Caswell, Shane V

2017-08-01

Wearable sensors are increasingly used to quantify the frequency and magnitude of head impact events in multiple sports. There is a paucity of evidence that verifies head impact events recorded by wearable sensors. To utilize video analysis to verify head impact events recorded by wearable sensors and describe the respective frequency and magnitude. Cohort study (diagnosis); Level of evidence, 2. Thirty male (mean age, 16.6 ± 1.2 years; mean height, 1.77 ± 0.06 m; mean weight, 73.4 ± 12.2 kg) and 35 female (mean age, 16.2 ± 1.3 years; mean height, 1.66 ± 0.05 m; mean weight, 61.2 ± 6.4 kg) players volunteered to participate in this study during the 2014 and 2015 lacrosse seasons. Participants were instrumented with GForceTracker (GFT; boys) and X-Patch sensors (girls). Simultaneous game video was recorded by a trained videographer using a single camera located at the highest midfield location. One-third of the field was framed and panned to follow the ball during games. Videographic and accelerometer data were time synchronized. Head impact counts were compared with video recordings and were deemed valid if (1) the linear acceleration was ≥20 g, (2) the player was identified on the field, (3) the player was in camera view, and (4) the head impact mechanism could be clearly identified. Descriptive statistics of peak linear acceleration (PLA) and peak rotational velocity (PRV) for all verified head impacts ≥20 g were calculated. For the boys, a total recorded 1063 impacts (2014: n = 545; 2015: n = 518) were logged by the GFT between game start and end times (mean PLA, 46 ± 31 g; mean PRV, 1093 ± 661 deg/s) during 368 player-games. Of these impacts, 690 were verified via video analysis (65%; mean PLA, 48 ± 34 g; mean PRV, 1242 ± 617 deg/s). The X-Patch sensors, worn by the girls, recorded a total 180 impacts during the course of the games, and 58 (2014: n = 33; 2015: n = 25) were verified via video analysis (32%; mean PLA, 39 ± 21 g; mean PRV, 1664 ± 619 rad/s). The current data indicate that existing wearable sensor technologies may substantially overestimate head impact events. Further, while the wearable sensors always estimated a head impact location, only 48% of the impacts were a result of direct contact to the head as characterized on video. Using wearable sensors and video to verify head impacts may decrease the inclusion of false-positive impacts during game activity in the analysis.

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data

ERIC Educational Resources Information Center

Prieto, L. P.; Sharma, K.; Kidzinski, L.; Rodríguez-Triana, M. J.; Dillenbourg, P.

2018-01-01

The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time)…

How honest are the signals? A protocol for validating wearable sensors.

PubMed

Kayhan, Varol Onur; Chen, Zheng Chris; French, Kimberly A; Allen, Tammy D; Salomon, Kristen; Watkins, Alison

2018-02-01

There is growing interest among organizational researchers in tapping into alternative sources of data beyond self-reports to provide a new avenue for measuring behavioral constructs. Use of alternative data sources such as wearable sensors is necessary for developing theory and enhancing organizational practice. Although wearable sensors are now commercially available, the veracity of the data they capture is largely unknown and mostly based on manufacturers' claims. The goal of this research is to test the validity and reliability of data captured by one such wearable badge (by Humanyze) in the context of structured meetings where all individuals wear a badge for the duration of the encounter. We developed a series of studies, each targeting a specific sensor of this badge that is relevant for structured meetings, and we make specific recommendations for badge data usage based on our validation results. We have incorporated the insights from our studies on a website that researchers can use to conduct validation tests for their badges, upload their data, and assess the validity of the data. We discuss this website in the corresponding studies.

Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using Personality Traits, Wearable Sensors and Mobile Phones

PubMed Central

Sano, Akane; Phillips, Andrew J.; Yu, Amy Z.; McHill, Andrew W.; Taylor, Sara; Jaques, Natasha; Czeisler, Charles A.; Klerman, Elizabeth B.; Picard, Rosalind W.

2017-01-01

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%. PMID:28516162

Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications

PubMed Central

Muro-de-la-Herran, Alvaro; Garcia-Zapirain, Begonya; Mendez-Zorrilla, Amaia

2014-01-01

This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis. PMID:24556672

A novel and lightweight system to secure wireless medical sensor networks.

PubMed

He, Daojing; Chan, Sammy; Tang, Shaohua

2014-01-01

Wireless medical sensor networks (MSNs) are a key enabling technology in e-healthcare that allows the data of a patient's vital body parameters to be collected by the wearable or implantable biosensors. However, the security and privacy protection of the collected data is a major unsolved issue, with challenges coming from the stringent resource constraints of MSN devices, and the high demand for both security/privacy and practicality. In this paper, we propose a lightweight and secure system for MSNs. The system employs hash-chain based key updating mechanism and proxy-protected signature technique to achieve efficient secure transmission and fine-grained data access control. Furthermore, we extend the system to provide backward secrecy and privacy preservation. Our system only requires symmetric-key encryption/decryption and hash operations and is thus suitable for the low-power sensor nodes. This paper also reports the experimental results of the proposed system in a network of resource-limited motes and laptop PCs, which show its efficiency in practice. To the best of our knowledge, this is the first secure data transmission and access control system for MSNs until now.

The Museum Wearable: Real-Time Sensor-Driven Understanding of Visitors' Interests for Personalized Visually-Augmented Museum Experiences.

ERIC Educational Resources Information Center

Sparacino, Flavia

This paper describes the museum wearable: a wearable computer that orchestrates an audiovisual narration as a function of the visitors' interests gathered from their physical path in the museum and length of stops. The wearable consists of a lightweight and small computer that people carry inside a shoulder pack. It offers an audiovisual…

Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors.

PubMed

Jayaraman, Chandrasekaran; Mummidisetty, Chaithanya Krishna; Mannix-Slobig, Alannah; McGee Koch, Lori; Jayaraman, Arun

2018-03-13

Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied. 28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements. The sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms. Implementing population specific customized algorithms accounting for the influences of sensor location, type and activity characteristics for estimating physical activity metrics in individuals with stroke and iSCI could be beneficial.

Admission Control Over Internet of Vehicles Attached With Medical Sensors for Ubiquitous Healthcare Applications.

PubMed

Lin, Di; Labeau, Fabrice; Yao, Yuanzhe; Vasilakos, Athanasios V; Tang, Yu

2016-07-01

Wireless technologies and vehicle-mounted or wearable medical sensors are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by radio frequency transmission. A high level of EMI may lead to a critical malfunction of medical sensors, and in such a scenario, a few users who are not transmitting emergency data could be required to reduce their transmit power or even temporarily disconnect from the network in order to guarantee the normal operation of medical sensors as well as the transmission of emergency data. In this paper, we propose a joint power and admission control algorithm to schedule the users' transmission of medical data. The objective of this algorithm is to minimize the number of users who are forced to disconnect from the network while keeping the EMI on medical sensors at an acceptable level. We show that a fixed point of proposed algorithm always exists, and at the fixed point, our proposed algorithm can minimize the number of low-priority users who are required to disconnect from the network. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.

Skin inspired fractal strain sensors using a copper nanowire and graphite microflake hybrid conductive network.

PubMed

Jason, Naveen N; Wang, Stephen J; Bhanushali, Sushrut; Cheng, Wenlong

2016-09-22

This work demonstrates a facile "paint-on" approach to fabricate highly stretchable and highly sensitive strain sensors by combining one-dimensional copper nanowire networks with two-dimensional graphite microflakes. This paint-on approach allows for the fabrication of electronic skin (e-skin) patches which can directly replicate with high fidelity the human skin surface they are on, regardless of the topological complexity. This leads to high accuracy for detecting biometric signals for applications in personalised wearable sensors. The copper nanowires contribute to high stretchability and the graphite flakes offer high sensitivity, and their hybrid coating offers the advantages of both. To understand the topological effects on the sensing performance, we utilized fractal shaped elastomeric substrates and systematically compared their stretchability and sensitivity. We could achieve a high stretchability of up to 600% and a maximum gauge factor of 3000. Our simple yet efficient paint-on approach enabled facile fine-tuning of sensitivity/stretchability simply by adjusting ratios of 1D vs. 2D materials in the hybrid coating, and the topological structural designs. This capability leads to a wide range of biomedical sensors demonstrated here, including pulse sensors, prosthetic hands, and a wireless ankle motion sensor.

Fall risks assessment among community dwelling elderly using wearable wireless sensors

NASA Astrophysics Data System (ADS)

Lockhart, Thurmon E.; Soangra, Rahul; Frames, Chris

2014-06-01

Postural stability characteristics are considered to be important in maintaining functional independence free of falls and healthy life style especially for the growing elderly population. This study focuses on developing tools of clinical value in fall prevention: 1) Implementation of sensors that are minimally obtrusive and reliably record movement data. 2) Unobtrusively gather data from wearable sensors from four community centers 3) developed and implemented linear and non-linear signal analysis algorithms to extract clinically relevant information using wearable technology. In all a total of 100 community dwelling elderly individuals (66 non-fallers and 34 fallers) participated in the experiment. All participants were asked to stand-still in eyes open (EO) and eyes closed (EC) condition on forceplate with one wireless inertial sensor affixed at sternum level. Participants' history of falls had been recorded for last 2 years, with emphasis on frequency and characteristics of falls. Any participant with at least one fall in the prior year were classified as faller and the others as non-faller. The results indicated several key factors/features of postural characteristics relevant to balance control and stability during quite stance and, showed good predictive capability of fall risks among older adults. Wearable technology allowed us to gather data where it matters the most to answer fall related questions, i.e. the community setting environments. This study opens new prospects of clinical testing using postural variables with a wearable sensor that may be relevant for assessing fall risks at home and patient environment in near future.

Highly Stretchable Multifunctional Wearable Devices Based on Conductive Cotton and Wool Fabrics.

PubMed

Souri, Hamid; Bhattacharyya, Debes

2018-06-05

The demand for stretchable, flexible, and wearable multifunctional devices based on conductive nanomaterials is rapidly increasing considering their interesting applications including human motion detection, robotics, and human-machine interface. There still exists a great challenge to manufacture stretchable, flexible, and wearable devices through a scalable and cost-effective fabrication method. Herein, we report a simple method for the mass production of electrically conductive textiles, made of cotton and wool, by hybridization of graphene nanoplatelets and carbon black particles. Conductive textiles incorporated into a highly elastic elastomer are utilized as highly stretchable and wearable strain sensors and heaters. The electromechanical characterizations of our multifunctional devices establish their excellent performance as wearable strain sensors to monitor various human motions, such as finger, wrist, and knee joint movements, and to recognize sound with high durability. Furthermore, the electrothermal behavior of our devices shows their potential application as stretchable and wearable heaters working at a maximum temperature of 103 °C powered with 20 V.

Detecting falls with wearable sensors using machine learning techniques.

PubMed

Özdemir, Ahmet Turan; Barshan, Billur

2014-06-18

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.

PubMed

Sano, Akane; Taylor, Sara; McHill, Andrew W; Phillips, Andrew Jk; Barger, Laura K; Klerman, Elizabeth; Picard, Rosalind

2018-06-08

Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping. ©Akane Sano, Sara Taylor, Andrew W McHill, Andrew JK Phillips, Laura K Barger, Elizabeth Klerman, Rosalind Picard. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.06.2018.

Wearable carbon nanotube-based fabric sensors for monitoring human physiological performance

NASA Astrophysics Data System (ADS)

Wang, Long; Loh, Kenneth J.

2017-05-01

A target application of wearable sensors is to detect human motion and to monitor physical activity for improving athletic performance and for delivering better physical therapy. In addition, measuring human vital signals (e.g., respiration rate and body temperature) provides rich information that can be used to assess a subject’s physiological or psychological condition. This study aims to design a multifunctional, wearable, fabric-based sensing system. First, carbon nanotube (CNT)-based thin films were fabricated by spraying. Second, the thin films were integrated with stretchable fabrics to form the fabric sensors. Third, the strain and temperature sensing properties of sensors fabricated using different CNT concentrations were characterized. Furthermore, the sensors were demonstrated to detect human finger bending motions, so as to validate their practical strain sensing performance. Finally, to monitor human respiration, the fabric sensors were integrated with a chest band, which was directly worn by a human subject. Quantification of respiration rates were successfully achieved. Overall, the fabric sensors were characterized by advantages such as flexibility, ease of fabrication, lightweight, low-cost, noninvasiveness, and user comfort.

Gas sensors boosted by two-dimensional h-BN enabled transfer on thin substrate foils: towards wearable and portable applications.

PubMed

Ayari, Taha; Bishop, Chris; Jordan, Matthew B; Sundaram, Suresh; Li, Xin; Alam, Saiful; ElGmili, Youssef; Patriarche, Gilles; Voss, Paul L; Salvestrini, Jean Paul; Ougazzaden, Abdallah

2017-11-09

The transfer of GaN based gas sensors to foreign substrates provides a pathway to enhance sensor performance, lower the cost and extend the applications to wearable, mobile or disposable systems. The main keys to unlocking this pathway is to grow and fabricate the sensors on large h-BN surface and to transfer them to the flexible substrate without any degradation of the performances. In this work, we develop a new generation of AlGaN/GaN gas sensors with boosted performances on a low cost flexible substrate. We fabricate 2-inch wafer scale AlGaN/GaN gas sensors on sacrificial two-dimensional (2D) nano-layered h-BN without any delamination or cracks and subsequently transfer sensors to an acrylic surface on metallic foil. This technique results in a modification of relevant device properties, leading to a doubling of the sensitivity to NO 2 gas and a response time that is more than 6 times faster than before transfer. This new approach for GaN-based sensor design opens new avenues for sensor improvement via transfer to more suitable substrates, and is promising for next-generation wearable and portable opto-electronic devices.

Laser-assisted fabrication of single-layer flexible touch sensor

PubMed Central

Son, Seokwoo; Park, Jong Eun; Lee, Joohyung; Yang, Minyang; Kang, Bongchul

2016-01-01

Single-layer flexible touch sensor that is designed for the indium-tin-oxide (ITO)-free, bendable, durable, multi-sensible, and single layer transparent touch sensor was developed via a low-cost and one-step laser-induced fabrication technology. To this end, an entirely novel approach involving material, device structure, and even fabrication method was adopted. Conventional metal oxides based multilayer touch structure was substituted by the single layer structure composed of integrated silver wire networks of sensors and bezel interconnections. This structure is concurrently fabricated on a glass substitutive plastic film via the laser-induced fabrication method using the low-cost organometallic/nanoparticle hybrid complex. In addition, this study addresses practical solutions to heterochromia and interference problem with a color display unit. As a result, a practical touch sensor is successfully demonstrated through resolving the heterochromia and interference problems with color display unit. This study could provide the breakthrough for early realization of wearable device. PMID:27703204

Flexible, highly sensitive pressure sensor with a wide range based on graphene-silk network structure

NASA Astrophysics Data System (ADS)

Liu, Ying; Tao, Lu-Qi; Wang, Dan-Yang; Zhang, Tian-Yu; Yang, Yi; Ren, Tian-Ling

2017-03-01

In this paper, a flexible, simple-preparation, and low-cost graphene-silk pressure sensor based on soft silk substrate through thermal reduction was demonstrated. Taking silk as the support body, the device had formed a three-dimensional structure with ordered multi-layer structure. Through a simple and low-cost process technology, graphene-silk pressure sensor can achieve the sensitivity value of 0.4 kPa - 1 , and the measurement range can be as high as 140 kPa. Besides, pressure sensor can have a good combination with knitted clothing and textile product. The signal had good reproducibility in response to different pressures. Furthermore, graphene-silk pressure sensor can not only detect pressure higher than 100 kPa, but also can measure weak body signals. The characteristics of high-sensitivity, good repeatability, flexibility, and comfort for skin provide the high possibility to fit on various wearable electronics.

A compact perspiration meter system with capacitive humidity sensor for wearable health-care applications

NASA Astrophysics Data System (ADS)

Mitani, Yusuke; Miyaji, Kousuke; Kaneko, Satoshi; Uekura, Takaharu; Momose, Hideya; Johguchi, Koh

2018-04-01

This paper presents a compact wearable perspiration meter system using a 180-nm CMOS technology. With custom chip and board design, the proposed perspiration meter, which can measure a qualitative sweating rate, is integrated into 15 × 20 mm2. From the experimental results, the capacitances of the humidity sensors with analog-to-digital converter and band-gap reference circuits can operate accurately without hysteresis. In addition, a demonstration with simulated human skin is carried out to investigate the sensor’s performance under real environments. The proposed perspiration meter can output values equivalent to a conventional meter. As a result, it is verified that the proposed system can be used as a human sweat sensor for wearable application.

Detecting Vital Signs with Wearable Wireless Sensors

PubMed Central

Yilmaz, Tuba; Foster, Robert; Hao, Yang

2010-01-01

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented. PMID:22163501

A Sensor Middleware for integration of heterogeneous medical devices.

PubMed

Brito, M; Vale, L; Carvalho, P; Henriques, J

2010-01-01

In this paper, the architecture of a modular, service-oriented, Sensor Middleware for data acquisition and processing is presented. The described solution was developed with the purpose of solving two increasingly relevant problems in the context of modern pHealth systems: i) to aggregate a number of heterogeneous, off-the-shelf, devices from which clinical measurements can be acquired and ii) to provide access and integration with an 802.15.4 network of wearable sensors. The modular nature of the Middleware provides the means to easily integrate pre-processing algorithms into processing pipelines, as well as new drivers for adding support for new sensor devices or communication technologies. Tests performed with both real and artificially generated data streams show that the presented solution is suitable for use both in a Windows PC or a Windows Mobile PDA with minimal overhead.

A wearable physiological sensor suite for unobtrusive monitoring of physiological and cognitive state.

PubMed

Matthews, Robert; McDonald, Neil J; Hervieux, Paul; Turner, Peter J; Steindorf, Martin A

2007-01-01

This paper describes an integrated Physiological Sensor Suite (PSS) based upon QUASAR's innovative non-invasive bioelectric sensor technologies that will provide, for the first time, a fully integrated, noninvasive methodology for physiological sensing. The PSS currently under development at QUASAR is a state-of-the-art multimodal array of sensors that, along with an ultra-low power personal area wireless network, form a comprehensive body-worn system for real-time monitoring of subject physiology and cognitive status. Applications of the PSS extend from monitoring of military personnel to long-term monitoring of patients diagnosed with cardiac or neurological conditions. Results for side-by-side comparisons between QUASAR's biosensor technology and conventional wet electrodes are presented. The signal fidelity for bioelectric measurements using QUASAR's biosensors is comparable to that for wet electrodes.

Anatomical calibration for wearable motion capture systems: Video calibrated anatomical system technique.

PubMed

Bisi, Maria Cristina; Stagni, Rita; Caroselli, Alessio; Cappello, Angelo

2015-08-01

Inertial sensors are becoming widely used for the assessment of human movement in both clinical and research applications, thanks to their usability out of the laboratory. This work aims to propose a method for calibrating anatomical landmark position in the wearable sensor reference frame with an ease to use, portable and low cost device. An off-the-shelf camera, a stick and a pattern, attached to the inertial sensor, compose the device. The proposed technique is referred to as video Calibrated Anatomical System Technique (vCAST). The absolute orientation of a synthetic femur was tracked both using the vCAST together with an inertial sensor and using stereo-photogrammetry as reference. Anatomical landmark calibration showed mean absolute error of 0.6±0.5 mm: these errors are smaller than those affecting the in-vivo identification of anatomical landmarks. The roll, pitch and yaw anatomical frame orientations showed root mean square errors close to the accuracy limit of the wearable sensor used (1°), highlighting the reliability of the proposed technique. In conclusion, the present paper proposes and preliminarily verifies the performance of a method (vCAST) for calibrating anatomical landmark position in the wearable sensor reference frame: the technique is low time consuming, highly portable, easy to implement and usable outside laboratory. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Silver Nanowire Embedded Colorless Polyimide Heater for Wearable Chemical Sensors: Improved Reversible Reaction Kinetics of Optically Reduced Graphene Oxide.

PubMed

Choi, Seon-Jin; Kim, Sang-Joon; Jang, Ji-Soo; Lee, Ji-Hyun; Kim, Il-Doo

2016-09-14

Optically reduced graphene oxide (ORGO) sheets are successfully integrated on silver nanowire (Ag NW)-embedded transparent and flexible substrate. As a heating element, Ag NWs are embedded in a colorless polyimide (CPI) film by covering Ag NW networks using polyamic acid and subsequent imidization. Graphene oxide dispersed aqueous solution is drop-coated on the Ag NW-embedded CPI (Ag NW-CPI) film and directly irradiated by intense pulsed light to obtain ORGO sheets. The heat generation property of Ag NW-CPI film is investigated by applying DC voltage, which demonstrates unprecedentedly reliable and stable characteristics even in dynamic bending condition. To demonstrate the potential application in wearable chemical sensors, NO 2 sensing characteristic of ORGO is investigated with respect to the different heating temperature (22.7-71.7 °C) of Ag NW-CPI film. The result reveals that the ORGO sheets exhibit high sensitivity of 2.69% with reversible response/recovery sensing properties and minimal deviation of baseline resistance of around 1% toward NO 2 molecules when the temperature of Ag NW-CPI film is 71.7 °C. This work first demonstrates the improved reversible NO 2 sensing properties of ORGO sheets on flexible and transparent Ag NW-CPI film assisted by Ag NW heating networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Design of QoS-Aware Multi-Level MAC-Layer for Wireless Body Area Network.

PubMed

Hu, Long; Zhang, Yin; Feng, Dakui; Hassan, Mohammad Mehedi; Alelaiwi, Abdulhameed; Alamri, Atif

2015-12-01

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving.

A wearable, low-power, health-monitoring instrumentation based on a Programmable System-on-Chip.

PubMed

Massot, Bertrand; Gehin, Claudine; Nocua, Ronald; Dittmar, Andre; McAdams, Eric

2009-01-01

Improvement in quality and efficiency of health and medicine, at home and in hospital, has become of paramount importance. The solution of this problem would require the continuous monitoring of several key patient parameters, including the assessment of autonomic nervous system (ANS) activity using non-invasive sensors, providing information for emotional, sensorial, cognitive and physiological analysis of the patient. Recent advances in embedded systems, microelectronics, sensors and wireless networking enable the design of wearable systems capable of such advanced health monitoring. The subject of this article is an ambulatory system comprising a small wrist device connected to several sensors for the detection of the autonomic nervous system activity. It affords monitoring of skin resistance, skin temperature and heart activity. It is also capable of recording the data on a removable media or sending it to computer via a wireless communication. The wrist device is based on a Programmable System-on-Chip (PSoC) from Cypress: PSoCs are mixed-signal arrays, with dynamic, configurable digital and analogical blocks and an 8-bit Microcontroller unit (MCU) core on a single chip. In this paper we present first of all the hardware and software architecture of the device, and then results obtained from initial experiments.

Flexible graphene bio-nanosensor for lactate.

PubMed

Labroo, Pratima; Cui, Yue

2013-03-15

The development of a flexible nanosensor for detecting lactate could expand opportunities for using graphene, both in fundamental studies for a variety of device platforms and in practical applications. Graphene is a delicate single-layer, two-dimensional network of carbon atoms with ultrasensitive sensing capabilities. Lactic acid is important for clinical analysis, sports medicine, and the food industry. Recently, wearable and flexible bioelectronics on plastics have attracted great interest for healthcare, sports and defense applications due to their advantages of being light-weight, bendable, or stretchable. Here, we demonstrate for the first time the development of a flexible graphene-based bio-nanosensor to detect lactate. Our results show that flexible lactate biosensors can be fabricated on a variety of plastic substrates. The sensor can detect lactate sensitively from 0.08 μM to 20 μM with a fast steady-state measuring time of 2s. The sensor can also detect lactate under different mechanical bending conditions, the sensor response decreased as the bending angle and number of bending repetitions increased. We anticipate that these results could open exciting opportunities for fundamental studies of flexible graphene bioelectronics by using other bioreceptors, as well as a variety of wearable, implantable, real-time, or on-site applications in fields ranging from clinical analysis to defense. Copyright © 2012 Elsevier B.V. All rights reserved.

Textile Organic Electrochemical Transistors as a Platform for Wearable Biosensors

NASA Astrophysics Data System (ADS)

Gualandi, I.; Marzocchi, M.; Achilli, A.; Cavedale, D.; Bonfiglio, A.; Fraboni, B.

2016-09-01

The development of wearable chemical sensors is receiving a great deal of attention in view of non-invasive and continuous monitoring of physiological parameters in healthcare applications. This paper describes the development of a fully textile, wearable chemical sensor based on an organic electrochemical transistor (OECT) entirely made of conductive polymer (PEDOT:PSS). The active polymer patterns are deposited into the fabric by screen printing processes, thus allowing the device to actually “disappear” into it. We demonstrate the reliability of the proposed textile OECTs as a platform for developing chemical sensors capable to detect in real-time various redox active molecules (adrenaline, dopamine and ascorbic acid), by assessing their performance in two different experimental contexts: i) ideal operation conditions (i.e. totally dipped in an electrolyte solution); ii) real-life operation conditions (i.e. by sequentially adding few drops of electrolyte solution onto only one side of the textile sensor). The OECTs response has also been measured in artificial sweat, assessing how these sensors can be reliably used for the detection of biomarkers in body fluids. Finally, the very low operating potentials (<1 V) and absorbed power (~10-4 W) make the here described textile OECTs very appealing for portable and wearable applications.

Textile Organic Electrochemical Transistors as a Platform for Wearable Biosensors

PubMed Central

Gualandi, I.; Marzocchi, M.; Achilli, A.; Cavedale, D.; Bonfiglio, A.; Fraboni, B.

2016-01-01

The development of wearable chemical sensors is receiving a great deal of attention in view of non-invasive and continuous monitoring of physiological parameters in healthcare applications. This paper describes the development of a fully textile, wearable chemical sensor based on an organic electrochemical transistor (OECT) entirely made of conductive polymer (PEDOT:PSS). The active polymer patterns are deposited into the fabric by screen printing processes, thus allowing the device to actually “disappear” into it. We demonstrate the reliability of the proposed textile OECTs as a platform for developing chemical sensors capable to detect in real-time various redox active molecules (adrenaline, dopamine and ascorbic acid), by assessing their performance in two different experimental contexts: i) ideal operation conditions (i.e. totally dipped in an electrolyte solution); ii) real-life operation conditions (i.e. by sequentially adding few drops of electrolyte solution onto only one side of the textile sensor). The OECTs response has also been measured in artificial sweat, assessing how these sensors can be reliably used for the detection of biomarkers in body fluids. Finally, the very low operating potentials (<1 V) and absorbed power (~10−4 W) make the here described textile OECTs very appealing for portable and wearable applications. PMID:27667396

Efficient Skin Temperature Sensor and Stable Gel-Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch.

PubMed

Yamamoto, Yuki; Yamamoto, Daisuke; Takada, Makoto; Naito, Hiroyoshi; Arie, Takayuki; Akita, Seiji; Takei, Kuniharu

2017-09-01

Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Carbon nanotubes (CNTs) based strain sensors for a wearable monitoring and biofeedback system for pressure ulcer prevention and rehabilitation.

PubMed

Boissy, Patrick; Genest, Jonathan; Patenaude, Johanne; Poirier, Marie-Sol; Chenel, Vanessa; Béland, Jean-Pierre; Legault, Georges-Auguste; Bernier, Louise; Tapin, Danielle; Beauvais, Jacques

2011-01-01

This paper presents an overview of the functioning principles of CNTs and their electrical and mechanical properties when used as strain sensors and describes a system embodiment for a wearable monitoring and biofeedback platform for use in pressure ulcer prevention and rehabilitation. Two type of CNTs films (multi-layered CNTs film vs purified film) were characterized electrically and mechanically for potential use as source material. The loosely woven CNTs film (multi-layered) showed substantial less sensitivity than the purified CNTs film but had an almost linear response to stress and better mechanical properties. CNTs have the potential to achieve a much higher sensitivity to strain than other piezoresistors based on regular of conductive particles such as commercially available resistive inks and could become an innovative source material for wearable strain sensors. We are currently continuing the characterization of CNTs based strain sensors and exploring their use in a design for 3-axis strain sensors.

Highly Stretchable and Transparent Electromagnetic Interference Shielding Film Based on Silver Nanowire Percolation Network for Wearable Electronics Applications.

PubMed

Jung, Jinwook; Lee, Habeom; Ha, Inho; Cho, Hyunmin; Kim, Kyun Kyu; Kwon, Jinhyeong; Won, Phillip; Hong, Sukjoon; Ko, Seung Hwan

2017-12-27

Future electronics are expected to develop into wearable forms, and an adequate stretchability is required for the forthcoming wearable electronics considering various motions occurring in human body. Along with stretchability, transparency can increase both the functionality and esthetic features in future wearable electronics. In this study, we demonstrate, for the first time, a highly stretchable and transparent electromagnetic interference shielding layer for wearable electronic applications with silver nanowire percolation network on elastic poly(dimethylsiloxane) substrate. The proposed stretchable and transparent electromagnetic interference shielding layer shows a high electromagnetic wave shielding effectiveness even under a high tensile strain condition. It is expected for the silver nanowire percolation network-based electromagnetic interference shielding layer to be beyond the conventional electromagnetic interference shielding materials and to broaden its application range to various fields that require optical transparency or nonplanar surface environment, such as biological system, human skin, and wearable electronics.

Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations

PubMed Central

Kim, Dong Hyun; Lee, Sang Wook; Park, Hyung-Soon

2016-01-01

Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices. PMID:27240364

Fabric-based integrated energy devices for wearable activity monitors.

PubMed

Jung, Sungmook; Lee, Jongsu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

2014-09-01

A wearable fabric-based integrated power-supply system that generates energy triboelectrically using human activity and stores the generated energy in an integrated supercapacitor is developed. This system can be utilized as either a self-powered activity monitor or as a power supply for external wearable sensors. These demonstrations give new insights for the research of wearable electronics. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review.

PubMed

Hubble, Ryan P; Naughton, Geraldine A; Silburn, Peter A; Cole, Michael H

2015-01-01

Postural instability and gait disability threaten the independence and well-being of people with Parkinson's disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson's disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson's disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population. Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized. Quality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson's disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson's disease patients and/or healthy controls. These data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson's disease symptoms and for assessing falls risk in this population. CRD42014010838.

Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review

PubMed Central

Hubble, Ryan P.; Naughton, Geraldine A.; Silburn, Peter A.; Cole, Michael H.

2015-01-01

Background Postural instability and gait disability threaten the independence and well-being of people with Parkinson’s disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson’s disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson’s disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population. Methods Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized. Results Quality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson’s disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson’s disease patients and/or healthy controls. Conclusion These data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson’s disease symptoms and for assessing falls risk in this population. PROSPERO Registration CRD42014010838 PMID:25894561

Enhancing the evaluation of pathogen transmission risk in a hospital by merging hand-hygiene compliance and contact data: a proof-of-concept study.

PubMed

Mastrandrea, Rossana; Soto-Aladro, Alberto; Brouqui, Philippe; Barrat, Alain

2015-09-10

Hand-hygiene compliance and contacts of health-care workers largely determine the potential paths of pathogen transmission in hospital wards. We explored how the combination of data collected by two automated infrastructures based on wearable sensors and recording (1) use of hydro-alcoholic solution and (2) contacts of health-care workers provide an enhanced view of the risk of transmission events in the ward. We perform a proof-of-concept observational study. Detailed data on contact patterns and hand-hygiene compliance of health-care workers were collected by wearable sensors over 12 days in an infectious disease unit of a hospital in Marseilles, France. 10,837 contact events among 10 doctors, 4 nurses, 4 nurses' aids and 4 housekeeping staff were recorded during the study. Most contacts took place among medical doctors. Aggregate contact durations were highly heterogeneous and the resulting contact network was highly structured. 510 visits of health-care workers to patients' rooms were recorded, with a low rate of hand-hygiene compliance. Both data sets were used to construct histories and statistics of contacts informed by the use of hydro-alcoholic solution, or lack thereof, of the involved health-care workers. Hand-hygiene compliance data strongly enrich the information concerning contacts among health-care workers, by assigning a 'safe' or 'at-risk' value to each contact. The global contact network can thus be divided into 'at-risk' and 'safe' contact networks. The combined data could be of high relevance for outbreak investigation and to inform data-driven models of nosocomial disease spread.

Sensible organizations: technology and methodology for automatically measuring organizational behavior.

PubMed

Olguin Olguin, Daniel; Waber, Benjamin N; Kim, Taemie; Mohan, Akshay; Ara, Koji; Pentland, Alex

2009-02-01

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.

Wearable wireless photoplethysmography sensors

NASA Astrophysics Data System (ADS)

Spigulis, Janis; Erts, Renars; Nikiforovs, Vladimirs; Kviesis-Kipge, Edgars

2008-04-01

Wearable health monitoring sensors may support early detection of abnormal conditions and prevention of their consequences. Recent designs of three wireless photoplethysmography monitoring devices embedded in hat, glove and sock, and connected to PC or mobile phone by means of the Bluetooth technology, are described. First results of distant monitoring of heart rate and pulse wave transit time using the newly developed devices are presented.

Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

PubMed Central

Cresswell, Kellen Garrison; Shin, Yongyun; Chen, Shanshan

2017-01-01

The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis. PMID:28245602

Flexible Transparent Electronic Gas Sensors.

PubMed

Wang, Ting; Guo, Yunlong; Wan, Pengbo; Zhang, Han; Chen, Xiaodong; Sun, Xiaoming

2016-07-01

Flexible and transparent electronic gas sensors capable of real-time, sensitive, and selective analysis at room-temperature, have gained immense popularity in recent years for their potential to be integrated into various smart wearable electronics and display devices. Here, recent advances in flexible transparent sensors constructed from semiconducting oxides, carbon materials, conducting polymers, and their nanocomposites are presented. The sensing material selection, sensor device construction, and sensing mechanism of flexible transparent sensors are discussed in detail. The critical challenges and future development associated with flexible and transparent electronic gas sensors are presented. Smart wearable gas sensors are believed to have great potential in environmental monitoring and noninvasive health monitoring based on disease biomarkers in exhaled gas. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Activity recognition with wearable sensors on loose clothing

PubMed Central

Howard, Matthew

2017-01-01

Observing human motion in natural everyday environments (such as the home), has evoked a growing interest in the development of on-body wearable sensing technology. However, wearable sensors suffer from motion artefacts introduced by the non-rigid attachment of sensors to the body, and the prevailing view is that it is necessary to eliminate these artefacts. This paper presents findings that suggest that these artefacts can, in fact, be used to distinguish between similar motions, by exploiting additional information provided by the fabric motion. An experimental study is presented whereby factors of both the motion and the properties of the fabric are analysed in the context of motion similarity. It is seen that while standard rigidly attached sensors have difficultly in distinguishing between similar motions, sensors mounted onto fabric exhibit significant differences (p < 0.01). An evaluation of the physical properties of the fabric shows that the stiffness of the material plays a role in this, with a trade-off between additional information and extraneous motion. This effect is evaluated in an online motion classification task, and the use of fabric-mounted sensors demonstrates an increase in prediction accuracy over rigidly attached sensors. PMID:28976978

Activity recognition with wearable sensors on loose clothing.

PubMed

Michael, Brendan; Howard, Matthew

2017-01-01

Observing human motion in natural everyday environments (such as the home), has evoked a growing interest in the development of on-body wearable sensing technology. However, wearable sensors suffer from motion artefacts introduced by the non-rigid attachment of sensors to the body, and the prevailing view is that it is necessary to eliminate these artefacts. This paper presents findings that suggest that these artefacts can, in fact, be used to distinguish between similar motions, by exploiting additional information provided by the fabric motion. An experimental study is presented whereby factors of both the motion and the properties of the fabric are analysed in the context of motion similarity. It is seen that while standard rigidly attached sensors have difficultly in distinguishing between similar motions, sensors mounted onto fabric exhibit significant differences (p < 0.01). An evaluation of the physical properties of the fabric shows that the stiffness of the material plays a role in this, with a trade-off between additional information and extraneous motion. This effect is evaluated in an online motion classification task, and the use of fabric-mounted sensors demonstrates an increase in prediction accuracy over rigidly attached sensors.

An activity recognition model using inertial sensor nodes in a wireless sensor network for frozen shoulder rehabilitation exercises.

PubMed

Lin, Hsueh-Chun; Chiang, Shu-Yin; Lee, Kai; Kan, Yao-Chiang

2015-01-19

This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%-95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises.

Real-time signal processing of accelerometer data for wearable medical patient monitoring devices.

PubMed

Van Wieringen, Matt; Eklund, J

2008-01-01

Elderly and other people who live at home but required some physical assistance to do so are often more susceptible injury causing falls in and around their place of residence. In the event that a fall does occur, as a direct result of a previous medical condition or the fall itself, these people are typically less likely to be able to seek timely medical help without assistance. The goal of this research is to develop a wearable sensor device that uses an accelerometer for monitoring the movement of the person to detect falls after they have occurred in order to enable timely medical assistance. The data coming from the accelerometer is processed in real-time in the device and sent to a remote monitoring station where operators can attempt to make contact with the person and/or notify medical personnel of the situation. The ADXL330 accelerometer is contained within a Nintendo WiiMote controller, which forms the basis of the wearable medical sensor. The accelerometer data can then be sent via Bluetooth connection and processed by a local gateway processor. If a fall is detected, the gateway will then contact a remote monitoring station, on a cellular network, for example, via satellite, and/or through a hardwired phone or Internet connection. To detect the occurrence of ta fall, the accelerometer data is passed through a matched filter and the data is compared to benchmark analysis data that will define the conditions that represents the occurrence of a fall.

Printable elastic conductors by in situ formation of silver nanoparticles from silver flakes

NASA Astrophysics Data System (ADS)

Matsuhisa, Naoji; Inoue, Daishi; Zalar, Peter; Jin, Hanbit; Matsuba, Yorishige; Itoh, Akira; Yokota, Tomoyuki; Hashizume, Daisuke; Someya, Takao

2017-08-01

Printable elastic conductors promise large-area stretchable sensor/actuator networks for healthcare, wearables and robotics. Elastomers with metal nanoparticles are one of the best approaches to achieve high performance, but large-area utilization is limited by difficulties in their processability. Here we report a printable elastic conductor containing Ag nanoparticles that are formed in situ, solely by mixing micrometre-sized Ag flakes, fluorine rubbers, and surfactant. Our printable elastic composites exhibit conductivity higher than 4,000 S cm-1 (highest value: 6,168 S cm-1) at 0% strain, and 935 S cm-1 when stretched up to 400%. Ag nanoparticle formation is influenced by the surfactant, heating processes, and elastomer molecular weight, resulting in a drastic improvement of conductivity. Fully printed sensor networks for stretchable robots are demonstrated, sensing pressure and temperature accurately, even when stretched over 250%.

Simple electrical model and initial experiments for intra-body communications.

PubMed

Gao, Y M; Pun, S H; Du, M; Mak, P U; Vai, M I

2009-01-01

Intra-Body Communication(IBC) is a short range "wireless" communication technique appeared in recent years. This technique relies on the conductive property of human tissue to transmit the electric signal among human body. This is beneficial for devices networking and sensors among human body, and especially suitable for wearable sensors, telemedicine system and home health care system as in general the data rates of physiologic parameters are low. In this article, galvanic coupling type IBC application on human limb was investigated in both its mathematical model and related experiments. The experimental results showed that the proposed mathematical model was capable in describing the galvanic coupling type IBC under low frequency. Additionally, the calculated result and experimental result also indicated that the electric signal induced by the transmitters of IBC can penetrate deep into human muscle and thus, provide an evident that IBC is capable of acting as networking technique for implantable devices.

[Exploration of the design of media access control layer of wireless body area network for medical healthcare].

PubMed

Liu, Xuemei; Ge, Baofeng

2012-04-01

This paper proposes a media access control (MAC) layer design for wireless body area network (WBAN) systems. WBAN is a technology that targets for wireless networking of wearable and implantable body sensors which monitor vital body signs, such as heart-rate, body temperature, blood pressure, etc. It has been receiving attentions from international organizations, e. g. the Institute of Electrical and Electronics Engineers (IEEE), due to its capability of providing efficient healthcare services and clinical management. This paper reviews the standardization procedure of WBAN and summarizes the challenge of the MAC layer design. It also discusses the methods of improving power consumption performance, which is one of the major issues of WBAN systems.

Assessing physical activity using wearable monitors: measures of physical activity.

PubMed

Butte, Nancy F; Ekelund, Ulf; Westerterp, Klaas R

2012-01-01

Physical activity may be defined broadly as "all bodily actions produced by the contraction of skeletal muscle that increase energy expenditure above basal level." Physical activity is a complex construct that can be classified into major categories qualitatively, quantitatively, or contextually. The quantitative assessment of physical activity using wearable monitors is grounded in the measurement of energy expenditure. Six main categories of wearable monitors are currently available to investigators: pedometers, load transducers/foot-contact monitors, accelerometers, HR monitors, combined accelerometer and HR monitors, and multiple sensor systems. Currently available monitors are capable of measuring total physical activity as well as components of physical activity that play important roles in human health. The selection of wearable monitors for measuring physical activity will depend on the physical activity component of interest, study objectives, characteristics of the target population, and study feasibility in terms of cost and logistics. Future development of sensors and analytical techniques for assessing physical activity should focus on the dynamic ranges of sensors, comparability for sensor output across manufacturers, and the application of advanced modeling techniques to predict energy expenditure and classify physical activities. New approaches for qualitatively classifying physical activity should be validated using direct observation or recording. New sensors and methods for quantitatively assessing physical activity should be validated in laboratory and free-living populations using criterion methods of calorimetry or doubly labeled water.

Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application.

PubMed

Patel, Shyamal; Chen, Bor-Rong; Buckley, Thomas; Rednic, Ramona; McClure, Doug; Tarsy, Daniel; Shih, Ludy; Dy, Jennifer; Welsh, Matt; Bonato, Paolo

2010-01-01

Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a web-based application provide reliable quantitative information that can be used for clinical decision making.

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease

PubMed Central

Hahn, Tim; Evers, Luc J. W.; de Vries, Nienke M.; Cohen, Eli; Afek, Michal; Bataille, Lauren; Daeschler, Margaret; Claes, Kasper; Boroojerdi, Babak; Terricabras, Dolors; Little, Max A.; Baldus, Heribert; Bloem, Bastiaan R.; Faber, Marjan J.

2017-01-01

Wearable devices can capture objective day-to-day data about Parkinson’s Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort. PMID:29261709

Wearable technology for spine movement assessment: A systematic review.

PubMed

Papi, Enrica; Koh, Woon Senn; McGregor, Alison H

2017-11-07

Continuous monitoring of spine movement function could enhance our understanding of low back pain development. Wearable technologies have gained popularity as promising alternative to laboratory systems in allowing ambulatory movement analysis. This paper aims to review the state of art of current use of wearable technology to assess spine kinematics and kinetics. Four electronic databases and reference lists of relevant articles were searched to find studies employing wearable technologies to assess the spine in adults performing dynamic movements. Two reviewers independently identified relevant papers. Customised data extraction and quality appraisal form were developed to extrapolate key details and identify risk of biases of each study. Twenty-two articles were retrieved that met the inclusion criteria: 12 were deemed of medium quality (score 33.4-66.7%), and 10 of high quality (score >66.8%). The majority of articles (19/22) reported validation type studies. Only 6 reported data collection in real-life environments. Multiple sensors type were used: electrogoniometers (3/22), strain gauges based sensors (3/22), textile piezoresistive sensor (1/22) and accelerometers often used with gyroscopes and magnetometers (15/22). Two sensors units were mainly used and placing was commonly reported on the spine lumbar and sacral regions. The sensors were often wired to data transmitter/logger resulting in cumbersome systems. Outcomes were mostly reported relative to the lumbar segment and in the sagittal plane, including angles, range of motion, angular velocity, joint moments and forces. This review demonstrates the applicability of wearable technology to assess the spine, although this technique is still at an early stage of development. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A stretchable strain sensor based on a metal nanoparticle thin film for human motion detection

NASA Astrophysics Data System (ADS)

Lee, Jaehwan; Kim, Sanghyeok; Lee, Jinjae; Yang, Daejong; Park, Byong Chon; Ryu, Seunghwa; Park, Inkyu

2014-09-01

Wearable strain sensors for human motion detection are being highlighted in various fields such as medical, entertainment and sports industry. In this paper, we propose a new type of stretchable strain sensor that can detect both tensile and compressive strains and can be fabricated by a very simple process. A silver nanoparticle (Ag NP) thin film patterned on the polydimethylsiloxane (PDMS) stamp by a single-step direct transfer process is used as the strain sensing material. The working principle is the change in the electrical resistance caused by the opening/closure of micro-cracks under mechanical deformation. The fabricated stretchable strain sensor shows highly sensitive and durable sensing performances in various tensile/compressive strains, long-term cyclic loading and relaxation tests. We demonstrate the applications of our stretchable strain sensors such as flexible pressure sensors and wearable human motion detection devices with high sensitivity, response speed and mechanical robustness.Wearable strain sensors for human motion detection are being highlighted in various fields such as medical, entertainment and sports industry. In this paper, we propose a new type of stretchable strain sensor that can detect both tensile and compressive strains and can be fabricated by a very simple process. A silver nanoparticle (Ag NP) thin film patterned on the polydimethylsiloxane (PDMS) stamp by a single-step direct transfer process is used as the strain sensing material. The working principle is the change in the electrical resistance caused by the opening/closure of micro-cracks under mechanical deformation. The fabricated stretchable strain sensor shows highly sensitive and durable sensing performances in various tensile/compressive strains, long-term cyclic loading and relaxation tests. We demonstrate the applications of our stretchable strain sensors such as flexible pressure sensors and wearable human motion detection devices with high sensitivity, response speed and mechanical robustness. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr03295k

A remote assessment system with a vision robot and wearable sensors.

PubMed

Zhang, Tong; Wang, Jue; Ren, Yumiao; Li, Jianjun

2004-01-01

This paper describes an ongoing researched remote rehabilitation assessment system that has a 6-freedom double-eyes vision robot to catch vision information, and a group of wearable sensors to acquire biomechanical signals. A server computer is fixed on the robot, to provide services to the robot's controller and all the sensors. The robot is connected to Internet by wireless channel, and so do the sensors to the robot. Rehabilitation professionals can semi-automatically practise an assessment program via Internet. The preliminary results show that the smart device, including the robot and the sensors, can improve the quality of remote assessment, and reduce the complexity of operation at a distance.

Wearable Wide-Range Strain Sensors Based on Ionic Liquids and Monitoring of Human Activities

PubMed Central

Zhang, Shao-Hui; Wang, Feng-Xia; Li, Jia-Jia; Peng, Hong-Dan; Yan, Jing-Hui; Pan, Ge-Bo

2017-01-01

Wearable sensors for detection of human activities have encouraged the development of highly elastic sensors. In particular, to capture subtle and large-scale body motion, stretchable and wide-range strain sensors are highly desired, but still a challenge. Herein, a highly stretchable and transparent stain sensor based on ionic liquids and elastic polymer has been developed. The as-obtained sensor exhibits impressive stretchability with wide-range strain (from 0.1% to 400%), good bending properties and high sensitivity, whose gauge factor can reach 7.9. Importantly, the sensors show excellent biological compatibility and succeed in monitoring the diverse human activities ranging from the complex large-scale multidimensional motions to subtle signals, including wrist, finger and elbow joint bending, finger touch, breath, speech, swallow behavior and pulse wave. PMID:29135928

Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis

PubMed Central

Goršič, Maja; Kamnik, Roman; Ambrožič, Luka; Vitiello, Nicola; Lefeber, Dirk; Pasquini, Guido; Munih, Marko

2014-01-01

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training. PMID:24521944

A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors.

PubMed

Chen, Bor-Rong; Patel, Shyamal; Buckley, Thomas; Rednic, Ramona; McClure, Douglas J; Shih, Ludy; Tarsy, Daniel; Welsh, Matt; Bonato, Paolo

2011-03-01

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.

Wearable Spiral Passive Electromagnetic Sensor (SPES) glove for sign language recognition of alphabet letters and numbers: a preliminary study

NASA Astrophysics Data System (ADS)

Iervolino, Onorio; Meo, Michele

2017-04-01

Sign language is a method of communication for deaf-mute people with articulated gestures and postures of hands and fingers to represent alphabet letters or complete words. Recognizing gestures is a difficult task, due to intrapersonal and interpersonal variations in performing them. This paper investigates the use of Spiral Passive Electromagnetic Sensor (SPES) as a motion recognition tool. An instrumented glove integrated with wearable multi-SPES sensors was developed to encode data and provide a unique response for each hand gesture. The device can be used for recognition of gestures; motion control and well-defined gesture sets such as sign languages. Each specific gesture was associated to a unique sensor response. The gloves encode data regarding the gesture directly in the frequency spectrum response of the SPES. The absence of chip or complex electronic circuit make the gloves light and comfortable to wear. Results showed encouraging data to use SPES in wearable applications.

3D Printed Stretchable Tactile Sensors.

PubMed

Guo, Shuang-Zhuang; Qiu, Kaiyan; Meng, Fanben; Park, Sung Hyun; McAlpine, Michael C

2017-07-01

The development of methods for the 3D printing of multifunctional devices could impact areas ranging from wearable electronics and energy harvesting devices to smart prosthetics and human-machine interfaces. Recently, the development of stretchable electronic devices has accelerated, concomitant with advances in functional materials and fabrication processes. In particular, novel strategies have been developed to enable the intimate biointegration of wearable electronic devices with human skin in ways that bypass the mechanical and thermal restrictions of traditional microfabrication technologies. Here, a multimaterial, multiscale, and multifunctional 3D printing approach is employed to fabricate 3D tactile sensors under ambient conditions conformally onto freeform surfaces. The customized sensor is demonstrated with the capabilities of detecting and differentiating human movements, including pulse monitoring and finger motions. The custom 3D printing of functional materials and devices opens new routes for the biointegration of various sensors in wearable electronics systems, and toward advanced bionic skin applications. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Extremely Elastic Wearable Carbon Nanotube Fiber Strain Sensor for Monitoring of Human Motion.

PubMed

Ryu, Seongwoo; Lee, Phillip; Chou, Jeffrey B; Xu, Ruize; Zhao, Rong; Hart, Anastasios John; Kim, Sang-Gook

2015-06-23

The increasing demand for wearable electronic devices has made the development of highly elastic strain sensors that can monitor various physical parameters an essential factor for realizing next generation electronics. Here, we report an ultrahigh stretchable and wearable device fabricated from dry-spun carbon nanotube (CNT) fibers. Stretching the highly oriented CNT fibers grown on a flexible substrate (Ecoflex) induces a constant decrease in the conductive pathways and contact areas between nanotubes depending on the stretching distance; this enables CNT fibers to behave as highly sensitive strain sensors. Owing to its unique structure and mechanism, this device can be stretched by over 900% while retaining high sensitivity, responsiveness, and durability. Furthermore, the device with biaxially oriented CNT fiber arrays shows independent cross-sensitivity, which facilitates simultaneous measurement of strains along multiple axes. We demonstrated potential applications of the proposed device, such as strain gauge, single and multiaxial detecting motion sensors. These devices can be incorporated into various motion detecting systems where their applications are limited to their strain.

Capacitive Pressure Sensor with High Sensitivity and Fast Response to Dynamic Interaction Based on Graphene and Porous Nylon Networks.

PubMed

He, Zhongfu; Chen, Wenjun; Liang, Binghao; Liu, Changyong; Yang, Leilei; Lu, Dongwei; Mo, Zichao; Zhu, Hai; Tang, Zikang; Gui, Xuchun

2018-04-18

Flexible pressure sensors are of great importance to be applied in artificial intelligence and wearable electronics. However, assembling a simple structure, high-performance capacitive pressure sensor, especially for monitoring the flow of liquids, is still a big challenge. Here, on the basis of a sandwich-like structure, we propose a facile capacitive pressure sensor optimized by a flexible, low-cost nylon netting, showing many merits including a high response sensitivity (0.33 kPa -1 ) in a low-pressure regime (<1 kPa), an ultralow detection limit as 3.3 Pa, excellent working stability after more than 1000 cycles, and synchronous monitoring for human pulses and clicks. More important, this sensor exhibits an ultrafast response speed (<20 ms), which enables its detection for the fast variations of a small applied pressure from the morphological changing processes of a droplet falling onto the sensor. Furthermore, a capacitive pressure sensor array is fabricated for demonstrating the ability to spatial pressure distribution. Our developed pressure sensors show great prospects in practical applications such as health monitoring, flexible tactile devices, and motion detection.

Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

PubMed Central

Wang, Cheng; Wang, Xiangdong; Long, Zhou; Yuan, Jing; Qian, Yueliang; Li, Jintao

2016-01-01

Most existing wearable gait analysis methods focus on the analysis of data obtained from inertial sensors. This paper proposes a novel, low-cost, wireless and wearable gait analysis system which uses microphone sensors to collect footstep sound signals during walking. This is the first time a microphone sensor is used as a wearable gait analysis device as far as we know. Based on this system, a gait analysis algorithm for estimating the temporal parameters of gait is presented. The algorithm fully uses the fusion of two feet footstep sound signals and includes three stages: footstep detection, heel-strike event and toe-on event detection, and calculation of gait temporal parameters. Experimental results show that with a total of 240 data sequences and 1732 steps collected using three different gait data collection strategies from 15 healthy subjects, the proposed system achieves an average 0.955 F1-measure for footstep detection, an average 94.52% accuracy rate for heel-strike detection and 94.25% accuracy rate for toe-on detection. Using these detection results, nine temporal related gait parameters are calculated and these parameters are consistent with their corresponding normal gait temporal parameters and labeled data calculation results. The results verify the effectiveness of our proposed system and algorithm for temporal gait parameter estimation. PMID:27999321

A wearable fingernail chemical sensing platform: pH sensing at your fingertips.

PubMed

Kim, Jayoung; Cho, Thomas N; Valdés-Ramírez, Gabriela; Wang, Joseph

2016-04-01

This article demonstrates an example of a wearable chemical sensor based on a fingernail platform. Fingernails represent an attractive wearable platform, merging beauty products with chemical sensing, to enable monitoring of our surrounding environment. The new colorimetric pH fingernail sensor relies on coating artificial nails with a recognition layer consisted of pH indicators entrapped in a polyvinyl chloride (PVC) matrix. Such color changing fingernails offer fast and reversible response to pH changes, repeated use, and intense color change detected easily with naked eye. The PVC matrix prevents leaching out of the indicator molecules from the fingernail sensor toward such repeated use. The limited narrow working pH range of a single pH indicator has been addressed by multiplexing three different pH indicators: bromothymol blue (pH 6.0-7.6), bromocresol green (pH 3.8-5.4), and cresol red (pH 7.2-8.8), as demonstrated for analyses of real-life samples of acidic, neutral, and basic character. The new concept of an optical wearable chemical sensor on fingernail platforms can be expanded towards diverse analytes for various applications in connection to the judicious design of the recognition layer. Copyright © 2016 Elsevier B.V. All rights reserved.

Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness.

PubMed

Muza, Stephen R

2018-03-01

This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO 2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.

Wearable PPG sensor based alertness scoring system.

PubMed

Dey, Jishnu; Bhowmik, Tanmoy; Sahoo, Saswata; Tiwari, Vijay Narayan

2017-07-01

Quantifying mental alertness in today's world is important as it enables the person to adopt lifestyle changes for better work efficiency. Miniaturized sensors in wearable devices have facilitated detection/monitoring of mental alertness. Photoplethysmography (PPG) sensors through Heart Rate Variability (HRV) offer one such opportunity by providing information about one's daily alertness levels without requiring any manual interference from the user. In this paper, a smartwatch based alertness estimation system is proposed. Data collected from PPG sensor of smartwatch is processed and fed to machine learning based model to get a continuous alertness score. Utility functions are designed based on statistical analysis to give a quality score on different stages of alertness such as awake, long sleep and short duration power nap. An intelligent data collection approach is proposed in collaboration with the motion sensor in the smartwatch to reduce battery drainage. Overall, our proposed wearable based system provides a detailed analysis of alertness over a period in a systematic and optimized manner. We were able to achieve an accuracy of 80.1% for sleep/awake classification along with alertness score. This opens up the possibility for quantifying alertness levels using a single PPG sensor for better management of health related activities including sleep.

Wearable, Flexible, and Multifunctional Healthcare Device with an ISFET Chemical Sensor for Simultaneous Sweat pH and Skin Temperature Monitoring.

PubMed

Nakata, Shogo; Arie, Takayuki; Akita, Seiji; Takei, Kuniharu

2017-03-24

Real-time daily healthcare monitoring may increase the chances of predicting and diagnosing diseases in their early stages which, currently, occurs most frequently during medical check-ups. Next-generation noninvasive healthcare devices, such as flexible multifunctional sensor sheets designed to be worn on skin, are considered to be highly suitable candidates for continuous real-time health monitoring. For healthcare applications, acquiring data on the chemical state of the body, alongside physical characteristics such as body temperature and activity, are extremely important for predicting and identifying potential health conditions. To record these data, in this study, we developed a wearable, flexible sweat chemical sensor sheet for pH measurement, consisting of an ion-sensitive field-effect transistor (ISFET) integrated with a flexible temperature sensor: we intend to use this device as the foundation of a fully integrated, wearable healthcare patch in the future. After characterizing the performance, mechanical flexibility, and stability of the sensor, real-time measurements of sweat pH and skin temperature are successfully conducted through skin contact. This flexible integrated device has the potential to be developed into a chemical sensor for sweat for applications in healthcare and sports.

Application of data fusion techniques and technologies for wearable health monitoring.

PubMed

King, Rachel C; Villeneuve, Emma; White, Ruth J; Sherratt, R Simon; Holderbaum, William; Harwin, William S

2017-04-01

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market. Copyright © 2017. Published by Elsevier Ltd.

Wireless wearable range-of-motion sensor system for upper and lower extremity joints: a validation study.

PubMed

Kumar, Yogaprakash; Yen, Shih-Cheng; Tay, Arthur; Lee, Wangwei; Gao, Fan; Zhao, Ziyi; Li, Jingze; Hon, Benjamin; Tian-Ma Xu, Tim; Cheong, Angela; Koh, Karen; Ng, Yee-Sien; Chew, Effie; Koh, Gerald

2015-02-01

Range-of-motion (ROM) assessment is a critical assessment tool during the rehabilitation process. The conventional approach uses the goniometer which remains the most reliable instrument but it is usually time-consuming and subject to both intra- and inter-therapist measurement errors. An automated wireless wearable sensor system for the measurement of ROM has previously been developed by the current authors. Presented is the correlation and accuracy of the automated wireless wearable sensor system against a goniometer in measuring ROM in the major joints of upper (UEs) and lower extremities (LEs) in 19 healthy subjects and 20 newly disabled inpatients through intra (same) subject comparison of ROM assessments between the sensor system against goniometer measurements by physical therapists. In healthy subjects, ROM measurements using the new sensor system were highly correlated with goniometry, with 95% of differences < 20° and 10° for most movements in major joints of UE and LE, respectively. Among inpatients undergoing rehabilitation, ROM measurements using the new sensor system were also highly correlated with goniometry, with 95% of the differences being < 20° and 25° for most movements in the major joints of UE and LE, respectively.

A review on architectures and communications technologies for wearable health-monitoring systems.

PubMed

Custodio, Víctor; Herrera, Francisco J; López, Gregorio; Moreno, José Ignacio

2012-10-16

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in "LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments", published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs.

PubMed

González-Tablas, Ana I; Tapiador, Juan E

2016-05-11

We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user's security preferences implicitly captured by policies already in place.

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs

PubMed Central

González-Tablas, Ana I.; Tapiador, Juan E.

2016-01-01

We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user’s security preferences implicitly captured by policies already in place. PMID:27187385

A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems

PubMed Central

Custodio, Víctor; Herrera, Francisco J.; López, Gregorio; Moreno, José Ignacio

2012-01-01

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications. PMID:23202028

Estimation of Center of Mass Trajectory using Wearable Sensors during Golf Swing.

PubMed

Najafi, Bijan; Lee-Eng, Jacqueline; Wrobel, James S; Goebel, Ruben

2015-06-01

This study suggests a wearable sensor technology to estimate center of mass (CoM) trajectory during a golf swing. Groups of 3, 4, and 18 participants were recruited, respectively, for the purpose of three validation studies. Study 1 examined the accuracy of the system to estimate a 3D body segment angle compared to a camera-based motion analyzer (Vicon®). Study 2 assessed the accuracy of three simplified CoM trajectory models. Finally, Study 3 assessed the accuracy of the proposed CoM model during multiple golf swings. A relatively high agreement was observed between wearable sensors and the reference (Vicon®) for angle measurement (r > 0.99, random error <1.2° (1.5%) for anterior-posterior; <0.9° (2%) for medial-lateral; and <3.6° (2.5%) for internal-external direction). The two-link model yielded a better agreement with the reference system compared to one-link model (r > 0.93 v. r = 0.52, respectively). On the same note, the proposed two-link model estimated CoM trajectory during golf swing with relatively good accuracy (r > 0.9, A-P random error <1cm (7.7%) and <2cm (10.4%) for M-L). The proposed system appears to accurately quantify the kinematics of CoM trajectory as a surrogate of dynamic postural control during an athlete's movement and its portability, makes it feasible to fit the competitive environment without restricting surface type. Key pointsThis study demonstrates that wearable technology based on inertial sensors are accurate to estimate center of mass trajectory in complex athletic task (e.g., golf swing)This study suggests that two-link model of human body provides optimum tradeoff between accuracy and minimum number of sensor module for estimation of center of mass trajectory in particular during fast movements.Wearable technologies based on inertial sensors are viable option for assessing dynamic postural control in complex task outside of gait laboratory and constraints of cameras, surface, and base of support.

A flexible wearable sensor for knee flexion assessment during gait.

PubMed

Papi, Enrica; Bo, Yen Nee; McGregor, Alison H

2018-05-01

Gait analysis plays an important role in the diagnosis and management of patients with movement disorders but it is usually performed within a laboratory. Recently interest has shifted towards the possibility of conducting gait assessments in everyday environments thus facilitating long-term monitoring. This is possible by using wearable technologies rather than laboratory based equipment. This study aims to validate a novel wearable sensor system's ability to measure peak knee sagittal angles during gait. The proposed system comprises a flexible conductive polymer unit interfaced with a wireless acquisition node attached over the knee on a pair of leggings. Sixteen healthy volunteers participated to two gait assessments on separate occasions. Data was simultaneously collected from the novel sensor and a gold standard 10 camera motion capture system. The relationship between sensor signal and reference knee flexion angles was defined for each subject to allow the transformation of sensor voltage outputs to angular measures (degrees). The knee peak flexion angle from the sensor and reference system were compared by means of root mean square error (RMSE), absolute error, Bland-Altman plots and intra-class correlation coefficients (ICCs) to assess test-retest reliability. Comparisons of knee peak flexion angles calculated from the sensor and gold standard yielded an absolute error of 0.35(±2.9°) and RMSE of 1.2(±0.4)°. Good agreement was found between the two systems with the majority of data lying within the limits of agreement. The sensor demonstrated high test-retest reliability (ICCs>0.8). These results show the ability of the sensor to monitor knee peak sagittal angles with small margins of error and in agreement with the gold standard system. The sensor has potential to be used in clinical settings as a discreet, unobtrusive wearable device allowing for long-term gait analysis. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Wearable Fall Detector using Integrated Sensors and Energy Devices

NASA Astrophysics Data System (ADS)

Jung, Sungmook; Hong, Seungki; Kim, Jaemin; Lee, Sangkyu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

2015-11-01

Wearable devices have attracted great attentions as next-generation electronic devices. For the comfortable, portable, and easy-to-use system platform in wearable electronics, a key requirement is to replace conventional bulky and rigid energy devices into thin and deformable ones accompanying the capability of long-term energy supply. Here, we demonstrate a wearable fall detection system composed of a wristband-type deformable triboelectric generator and lithium ion battery in conjunction with integrated sensors, controllers, and wireless units. A stretchable conductive nylon is used as electrodes of the triboelectric generator and the interconnection between battery cells. Ethoxylated polyethylenimine, coated on the surface of the conductive nylon electrode, tunes the work function of a triboelectric generator and maximizes its performance. The electrical energy harvested from the triboelectric generator through human body motions continuously recharges the stretchable battery and prolongs hours of its use. The integrated energy supply system runs the 3-axis accelerometer and related electronics that record human body motions and send the data wirelessly. Upon the unexpected fall occurring, a custom-made software discriminates the fall signal and an emergency alert is immediately sent to an external mobile device. This wearable fall detection system would provide new opportunities in the mobile electronics and wearable healthcare.

Wearable Fall Detector using Integrated Sensors and Energy Devices.

PubMed

Jung, Sungmook; Hong, Seungki; Kim, Jaemin; Lee, Sangkyu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

2015-11-24

Wearable devices have attracted great attentions as next-generation electronic devices. For the comfortable, portable, and easy-to-use system platform in wearable electronics, a key requirement is to replace conventional bulky and rigid energy devices into thin and deformable ones accompanying the capability of long-term energy supply. Here, we demonstrate a wearable fall detection system composed of a wristband-type deformable triboelectric generator and lithium ion battery in conjunction with integrated sensors, controllers, and wireless units. A stretchable conductive nylon is used as electrodes of the triboelectric generator and the interconnection between battery cells. Ethoxylated polyethylenimine, coated on the surface of the conductive nylon electrode, tunes the work function of a triboelectric generator and maximizes its performance. The electrical energy harvested from the triboelectric generator through human body motions continuously recharges the stretchable battery and prolongs hours of its use. The integrated energy supply system runs the 3-axis accelerometer and related electronics that record human body motions and send the data wirelessly. Upon the unexpected fall occurring, a custom-made software discriminates the fall signal and an emergency alert is immediately sent to an external mobile device. This wearable fall detection system would provide new opportunities in the mobile electronics and wearable healthcare.

Energy harvesting for human wearable and implantable bio-sensors.

PubMed

Mitcheson, Paul D

2010-01-01

There are clear trade-offs between functionality, battery lifetime and battery volume for wearable and implantable wireless-biosensors which energy harvesting devices may be able to overcome. Reliable energy harvesting has now become a reality for machine condition monitoring and is finding applications in chemical process plants, refineries and water treatment works. However, practical miniature devices that can harvest sufficient energy from the human body to power a wireless bio-sensor are still in their infancy. This paper reviews the options for human energy harvesting in order to determine power availability for harvester-powered body sensor networks. The main competing technologies for energy harvesting from the human body are inertial kinetic energy harvesting devices and thermoelectric devices. These devices are advantageous to some other types as they can be hermetically sealed. In this paper the fundamental limit to the power output of these devices is compared as a function of generator volume when attached to a human whilst walking and running. It is shown that the kinetic energy devices have the highest fundamental power limits in both cases. However, when a comparison is made between the devices using device effectivenesses figures from previously demonstrated prototypes presented in the literature, the thermal device is competitive with the kinetic energy harvesting device when the subject is running and achieves the highest power density when the subject is walking.

MEDIC: medical embedded device for individualized care.

PubMed

Wu, Winston H; Bui, Alex A T; Batalin, Maxim A; Au, Lawrence K; Binney, Jonathan D; Kaiser, William J

2008-02-01

Presented work highlights the development and initial validation of a medical embedded device for individualized care (MEDIC), which is based on a novel software architecture, enabling sensor management and disease prediction capabilities, and commercially available microelectronic components, sensors and conventional personal digital assistant (PDA) (or a cell phone). In this paper, we present a general architecture for a wearable sensor system that can be customized to an individual patient's needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics. A prototype of the system has been developed based on a standard PDA and wireless sensor nodes equipped with commercially available Bluetooth radio components, permitting real-time streaming of high-bandwidth data from various physiological and contextual sensors. We also present the results of abnormal gait diagnosis using the complete system from our evaluation, and illustrate how the wearable system and its operation can be remotely configured and managed by either enterprise systems or medical personnel at centralized locations. By using commercially available hardware components and software architecture presented in this paper, the MEDIC system can be rapidly configured, providing medical researchers with broadband sensor data from remote patients and platform access to best adapt operation for diagnostic operation objectives.

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis.

PubMed

Gao, Wei; Emaminejad, Sam; Nyein, Hnin Yin Yin; Challa, Samyuktha; Chen, Kevin; Peck, Austin; Fahad, Hossain M; Ota, Hiroki; Shiraki, Hiroshi; Kiriya, Daisuke; Lien, Der-Hsien; Brooks, George A; Davis, Ronald W; Javey, Ali

2016-01-28

Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

NASA Astrophysics Data System (ADS)

Gao, Wei; Emaminejad, Sam; Nyein, Hnin Yin Yin; Challa, Samyuktha; Chen, Kevin; Peck, Austin; Fahad, Hossain M.; Ota, Hiroki; Shiraki, Hiroshi; Kiriya, Daisuke; Lien, Der-Hsien; Brooks, George A.; Davis, Ronald W.; Javey, Ali

2016-01-01

Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers.

PubMed

Aziz, Omar; Park, Edward J; Mori, Greg; Robinovitch, Stephen N

2014-01-01

Falls are the number one cause of injury in older adults. Lack of objective evidence on the cause and circumstances of falls is often a barrier to effective prevention strategies. Previous studies have established the ability of wearable miniature inertial sensors (accelerometers and gyroscopes) to automatically detect falls, for the purpose of delivering medical assistance. In the current study, we extend the applications of this technology, by developing and evaluating the accuracy of wearable sensor systems for determining the cause of falls. Twelve young adults participated in experimental trials involving falls due to seven causes: slips, trips, fainting, and incorrect shifting/transfer of body weight while sitting down, standing up from sitting, reaching and turning. Features (means and variances) of acceleration data acquired from four tri-axial accelerometers during the falling trials were input to a linear discriminant analysis technique. Data from an array of three sensors (left ankle+right ankle+sternum) provided at least 83% sensitivity and 89% specificity in classifying falls due to slips, trips, and incorrect shift of body weight during sitting, reaching and turning. Classification of falls due to fainting and incorrect shift during rising was less successful across all sensor combinations. Furthermore, similar classification accuracy was observed with data from wearable sensors and a video-based motion analysis system. These results establish a basis for the development of sensor-based fall monitoring systems that provide information on the cause and circumstances of falls, to direct fall prevention strategies at a patient or population level. Copyright © 2013 Elsevier B.V. All rights reserved.

A wireless potentiostat for mobile chemical sensing and biosensing.

PubMed

Steinberg, Matthew D; Kassal, Petar; Kereković, Irena; Steinberg, Ivana Murković

2015-10-01

Wireless chemical sensors are used as analytical devices in homeland defence, home-based healthcare, food logistics and more generally for the Sensor Internet of Things (SIoT). Presented here is a battery-powered and highly portable credit-card size potentiostat that is suitable for performing mobile and wearable amperometric electrochemical measurements with seamless wireless data transfer to mobile computing devices. The mobile electrochemical analytical system has been evaluated in the laboratory with a model redox system - the reduction of hexacyanoferrate(III) - and also with commercially available enzymatic blood-glucose test-strips. The potentiostat communicates wirelessly with mobile devices such as tablets or Smartphones by near-field communication (NFC) or with personal computers by radio-frequency identification (RFID), and thus provides a solution to the 'missing link' in connectivity that often exists between low-cost mobile and wearable chemical sensors and ubiquitous mobile computing products. The mobile potentiostat has been evaluated in the laboratory with a set of proof-of-concept experiments, and its analytical performance compared with a commercial laboratory potentiostat (R(2)=0.9999). These first experimental results demonstrate the functionality of the wireless potentiostat and suggest that the device could be suitable for wearable and point-of-sample analytical measurements. We conclude that the wireless potentiostat could contribute significantly to the advancement of mobile chemical sensor research and adoption, in particular for wearable sensors in healthcare and sport physiology, for wound monitoring and in mobile point-of-sample diagnostics as well as more generally as a part of the Sensor Internet of Things. Copyright © 2015 Elsevier B.V. All rights reserved.

Wearable sensors for patient-specific boundary shape estimation to improve the forward model for electrical impedance tomography (EIT) of neonatal lung function.

PubMed

Khor, Joo Moy; Tizzard, Andrew; Demosthenous, Andreas; Bayford, Richard

2014-06-01

Electrical impedance tomography (EIT) could be significantly advantageous to continuous monitoring of lung development in newborn and, in particular, preterm infants as it is non-invasive and safe to use within the intensive care unit. It has been demonstrated that accurate boundary form of the forward model is important to minimize artefacts in reconstructed electrical impedance images. This paper presents the outcomes of initial investigations for acquiring patient-specific thorax boundary information using a network of flexible sensors that imposes no restrictions on the patient's normal breathing and movements. The investigations include: (1) description of the basis of the reconstruction algorithms, (2) tests to determine a minimum number of bend sensors, (3) validation of two approaches to reconstruction and (4) an example of a commercially available bend sensor and its performance. Simulation results using ideal sensors show that, in the worst case, a total shape error of less than 6% with respect to its total perimeter can be achieved.

The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review

PubMed Central

Fong, Daniel Tik-Pui; Chan, Yue-Yan

2010-01-01

Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed. PMID:22163542

The use of wearable inertial motion sensors in human lower limb biomechanics studies: a systematic review.

PubMed

Fong, Daniel Tik-Pui; Chan, Yue-Yan

2010-01-01

Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed.

3D printing of wearable fractal-based sensor systems for neurocardiology and healthcare

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2017-04-01

Neurocardiology is the pathophysiological interplay of nervous and cardiovascular systems. The communication between the heart and brain has revealed various methodologies in healthcare that could be investigated to study the heart-brain interactions and other cardiovascular and neurological diseases. A textile based wearable nanosensor system in the form of e-bra, e-shirt, e-headband, e-brief, underwear etc, was presented in this SPIE conferences earlier for noninvasive recording of EEG and EKG, and showing the correlation between the brain and heart signals. In this paper, the technology is expanded further using fractal based geometries using 3D printing system for low cost and flexible wearable sensor system for healthcare.

Low-Cost Embedded Oximeter

NASA Astrophysics Data System (ADS)

Laghrouche, M.; Haddab, S.; Lotmani, S.; Mekdoud, K.; Ameur, S.

2010-01-01

Nowadays, many medical devices have been developed for the purposes of diagnosing and treatment. Wearable sensors and systems have evolved to the point that they can be considered ready for clinical application. The use of wearable monitoring devices that allow continuous or intermittent monitoring of physiological signals is critical for the advancement of both the diagnosis as well as treatment of diseases. Patient vital sign monitoring within hospitals requires the use of noninvasive sensors that are hardwired to bedside monitors. This paper describes the initial bench testing of a wireless wearable pulse oximeter. Arterial oxygen saturation in the patient's blood signal was measured with an optical sensor, and then converted to digital data using a microcontroller system. The digital data were then sent to a receiver where it is in 433 MHz FM/FSK transmitter. At the receiver, the digital data were reconverted to analog signal to be monitored and recorded on the PC.

Development of gait segmentation methods for wearable foot pressure sensors.

PubMed

Crea, S; De Rossi, S M M; Donati, M; Reberšek, P; Novak, D; Vitiello, N; Lenzi, T; Podobnik, J; Munih, M; Carrozza, M C

2012-01-01

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Throughput, latency and cost comparisons of microcontroller-based implementations of wireless sensor network (WSN) in high jump sports

NASA Astrophysics Data System (ADS)

Ahmad, Afandi; Roslan, Muhammad Faris; Amira, Abbes

2017-09-01

In high jump sports, approach take-off speed and force during the take-off are two (2) main important parts to gain maximum jump. To measure both parameters, wireless sensor network (WSN) that contains microcontroller and sensor are needed to describe the results of speed and force for jumpers. Most of the microcontroller exhibit transmission issues in terms of throughput, latency and cost. Thus, this study presents the comparison of wireless microcontrollers in terms of throughput, latency and cost, and the microcontroller that have best performances and cost will be implemented in high jump wearable device. In the experiments, three (3) parts have been integrated - input, process and output. Force (for ankle) and global positioning system (GPS) sensor (for body waist) acts as an input for data transmission. These data were then being processed by both microcontrollers, ESP8266 and Arduino Yun Mini to transmit the data from sensors to the server (host-PC) via message queuing telemetry transport (MQTT) protocol. The server acts as receiver and the results was calculated from the MQTT log files. At the end, results obtained have shown ESP8266 microcontroller had been chosen since it achieved high throughput, low latency and 11 times cheaper in term of prices compared to Arduino Yun Mini microcontroller.

Evaluation of a 433 MHz band body sensor network for biomedical applications.

PubMed

Kim, Saim; Brendle, Christian; Lee, Hyun-Young; Walter, Marian; Gloeggler, Sigrid; Krueger, Stefan; Leonhardt, Steffen

2013-01-14

Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth or WiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%).

Smart wearable systems: current status and future challenges.

PubMed

Chan, Marie; Estève, Daniel; Fourniols, Jean-Yves; Escriba, Christophe; Campo, Eric

2012-11-01

Extensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research. Herein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered. We describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS. We conclude by identifying the future challenges facing SWS for HM. Copyright © 2012 Elsevier B.V. All rights reserved.

Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges.

PubMed

Lymberis, A; Paradiso, R

2008-01-01

Smart fabrics and interactive textiles (SFIT) are fibrous structures that are capable of sensing, actuating, generating/storing power and/or communicating. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection & safety, and healthy lifestyle gained strong interest during the last 10 years. Under the Information and Communication Programme of the European Commission, a cluster of R&D projects dealing with smart fabrics and interactive textile wearable systems regroup activities along two different and complementary approaches i.e. 'application pull' and 'technology push'. This includes projects aiming at personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. The integration part of the technologies into a real SFIT product is at present stage on the threshold of prototyping and testing. Several issues, technical as well user-centred, societal and business, remain to be solved. The paper presents on going major R&D activities, identifies gaps and discuss key challenges for the future.

Smart Vest: wearable multi-parameter remote physiological monitoring system.

PubMed

Pandian, P S; Mohanavelu, K; Safeer, K P; Kotresh, T M; Shakunthala, D T; Gopal, Parvati; Padaki, V C

2008-05-01

The wearable physiological monitoring system is a washable shirt, which uses an array of sensors connected to a central processing unit with firmware for continuously monitoring physiological signals. The data collected can be correlated to produce an overall picture of the wearer's health. In this paper, we discuss the wearable physiological monitoring system called 'Smart Vest'. The Smart Vest consists of a comfortable to wear vest with sensors integrated for monitoring physiological parameters, wearable data acquisition and processing hardware and remote monitoring station. The wearable data acquisition system is designed using microcontroller and interfaced with wireless communication and global positioning system (GPS) modules. The physiological signals monitored are electrocardiogram (ECG), photoplethysmogram (PPG), body temperature, blood pressure, galvanic skin response (GSR) and heart rate. The acquired physiological signals are sampled at 250samples/s, digitized at 12-bit resolution and transmitted wireless to a remote physiological monitoring station along with the geo-location of the wearer. The paper describes a prototype Smart Vest system used for remote monitoring of physiological parameters and the clinical validation of the data are also presented.

Improving spatial resolution in skin-contact thermography: comparison between a spline based and linear interpolation.

PubMed

Giansanti, Daniele

2008-07-01

A wearable device for skin-contact thermography [Giansanti D, Maccioni G. Development and testing of a wearable integrated thermometer sensor for skin contact thermography. Med Eng Phys 2006 [ahead of print

Field Programmable Gate Array (FPGA) Respiratory Monitoring System Using a Flow Microsensor and an Accelerometer

NASA Astrophysics Data System (ADS)

Mellal, Idir; Laghrouche, Mourad; Bui, Hung Tien

2017-04-01

This paper describes a non-invasive system for respiratory monitoring using a Micro Electro Mechanical Systems (MEMS) flow sensor and an IMU (Inertial Measurement Unit) accelerometer. The designed system is intended to be wearable and used in a hospital or at home to assist people with respiratory disorders. To ensure the accuracy of our system, we proposed a calibration method based on ANN (Artificial Neural Network) to compensate the temperature drift of the silicon flow sensor. The sigmoid activation functions used in the ANN model were computed with the CORDIC (COordinate Rotation DIgital Computer) algorithm. This algorithm was also used to estimate the tilt angle in body position. The design was implemented on reconfigurable platform FPGA.

Flexible and wearable 3D graphene sensor with 141 KHz frequency signal response capability

NASA Astrophysics Data System (ADS)

Xu, R.; Zhang, H.; Cai, Y.; Ruan, J.; Qu, K.; Liu, E.; Ni, X.; Lu, M.; Dong, X.

2017-09-01

We developed a flexible force sensor consisting of 3D graphene foam (GF) encapsulated in flexible polydimethylsiloxane (PDMS). Because the 3D GF/PDMS sensor is based on the transformation of an electronic band structure aroused by static mechanical strain or KHz vibration, it can detect frequency signals by both tuning fork tests and piezoelectric ceramic transducer tests, which showed a clear linear response from audio frequencies, including frequencies up to 141 KHz in the ultrasound range. Because of their excellent response with a wide bandwidth, the 3D GF/PDMS sensors are attractive for interactive wearable devices or artificial prosthetics capable of perceiving seismic waves, ultrasonic waves, shock waves, and transient pressures.

Wearable strain sensors based on thin graphite films for human activity monitoring

NASA Astrophysics Data System (ADS)

Saito, Takanari; Kihara, Yusuke; Shirakashi, Jun-ichi

2017-12-01

Wearable health-monitoring devices have attracted increasing attention in disease diagnosis and health assessment. In many cases, such devices have been prepared by complicated multistep procedures which result in the waste of materials and require expensive facilities. In this study, we focused on pyrolytic graphite sheet (PGS), which is a low-cost, simple, and flexible material, used as wearable devices for monitoring human activity. We investigated wearable devices based on PGSs for the observation of elbow and finger motions. The thin graphite films were fabricated by cutting small films from PGSs. The wearable devices were then made from the thin graphite films assembled on a commercially available rubber glove. The human motions could be observed using the wearable devices. Therefore, these results suggested that the wearable devices based on thin graphite films may broaden their application in cost-effective wearable electronics for the observation of human activity.

Smart sensors enable smart air conditioning control.

PubMed

Cheng, Chin-Chi; Lee, Dasheng

2014-06-24

In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants' information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans' intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It's also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.

Engineering the Charge Transport of Ag Nanocrystals for Highly Accurate, Wearable Temperature Sensors through All-Solution Processes.

PubMed

Joh, Hyungmok; Lee, Seung-Wook; Seong, Mingi; Lee, Woo Seok; Oh, Soong Ju

2017-06-01

All-nanocrystal (NC)-based and all-solution-processed wearable resistance temperature detectors (RTDs) are introduced. The charge transport mechanisms of Ag NC thin films are engineered through various ligand treatments to design high performance RTDs. Highly conductive Ag NC thin films exhibiting metallic transport behavior with high positive temperature coefficients of resistance (TCRs) are achieved through tetrabutylammonium bromide treatment. Ag NC thin films showing hopping transport with high negative TCRs are created through organic ligand treatment. All-solution-based, one-step photolithography techniques that integrate two distinct opposite-sign TCR Ag NC thin films into an ultrathin single device are developed to decouple the mechanical effects such as human motion. The unconventional materials design and strategy enables highly accurate, sensitive, wearable and motion-free RTDs, demonstrated by experiments on moving or curved objects such as human skin, and simulation results based on charge transport analysis. This strategy provides a low cost and simple method to design wearable multifunctional sensors with high sensitivity which could be utilized in various fields such as biointegrated sensors or electronic skin. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Smart Sensors Enable Smart Air Conditioning Control

PubMed Central

Cheng, Chin-Chi; Lee, Dasheng

2014-01-01

In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants' information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans' intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It's also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection. PMID:24961213

Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion

PubMed Central

Gaidhani, Apoorva; Moon, Kee S.; Ozturk, Yusuf; Lee, Sung Q.; Youm, Woosub

2017-01-01

Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit) can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the “Sagittal” plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data. PMID:29258214

Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

PubMed Central

Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng

2015-01-01

Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path. PMID:25961384

A wearable biochemical sensor for monitoring alcohol consumption lifestyle through Ethyl glucuronide (EtG) detection in human sweat.

PubMed

Selvam, Anjan Panneer; Muthukumar, Sriram; Kamakoti, Vikramshankar; Prasad, Shalini

2016-03-21

We demonstrate for the first time a wearable biochemical sensor for monitoring alcohol consumption through the detection and quantification of a metabolite of ethanol, ethyl glucuronide (EtG). We designed and fabricated two co-planar sensors with gold and zinc oxide as sensing electrodes. We also designed a LED based reporting for the presence of EtG in the human sweat samples. The sensor functions on affinity based immunoassay principles whereby monoclonal antibodies for EtG were immobilized on the electrodes using thiol based chemistry. Detection of EtG from human sweat was achieved through chemiresistive sensing mechanism. In this method, an AC voltage was applied across the two coplanar electrodes and the impedance across the sensor electrodes was measured and calibrated for physiologically relevant doses of EtG in human sweat. EtG detection over a dose concentration of 0.001-100 μg/L was demonstrated on both glass and polyimide substrates. Detection sensitivity was lower at 1 μg/L with gold electrodes as compared to ZnO, which had detection sensitivity of 0.001 μg/L. Based on the detection range the wearable sensor has the ability to detect alcohol consumption of up to 11 standard drinks in the US over a period of 4 to 9 hours.

Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review.

PubMed

Ma, Christina Zong-Hao; Wong, Duo Wai-Chi; Lam, Wing Kai; Wan, Anson Hong-Ping; Lee, Winson Chiu-Chun

2016-03-25

Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors.

Wearable Resistive Pressure Sensor Based on Highly Flexible Carbon Composite Conductors with Irregular Surface Morphology.

PubMed

Kim, Kang-Hyun; Hong, Soon Kyu; Jang, Nam-Su; Ha, Sung-Hun; Lee, Hyung Woo; Kim, Jong-Man

2017-05-24

Wearable pressure sensors are crucial building blocks for potential applications in real-time health monitoring, artificial electronic skins, and human-to-machine interfaces. Here we present a highly sensitive, simple-architectured wearable resistive pressure sensor based on highly compliant yet robust carbon composite conductors made of a vertically aligned carbon nanotube (VACNT) forest embedded in a polydimethylsiloxane (PDMS) matrix with irregular surface morphology. A roughened surface of the VACNT/PDMS composite conductor is simply formed using a sandblasted silicon master in a low-cost and potentially scalable manner and plays an important role in improving the sensitivity of resistive pressure sensor. After assembling two of the roughened composite conductors, our sensor shows considerable pressure sensitivity of ∼0.3 kPa -1 up to 0.7 kPa as well as stable steady-state responses under various pressures, a wide detectable range of up to 5 kPa before saturation, a relatively fast response time of ∼162 ms, and good reproducibility over 5000 cycles of pressure loading/unloading. The fabricated pressure sensor can be used to detect a wide range of human motions ranging from subtle blood pulses to dynamic joint movements, and it can also be used to map spatial pressure distribution in a multipixel platform (in a 4 × 4 pixel array).

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

DOE PAGES

Gao, Wei; Emaminejad, Sam; Nyein, Hnin Yin Yin; ...

2016-01-27

We report that wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health. Sampling human sweat, which is rich in physiological information13, could enable non-invasive monitoring. Previously reported sweat-based and other noninvasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state14–18. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanicallymore » flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Lastly, our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plasticbased sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.« less

Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review

PubMed Central

Ma, Christina Zong-Hao; Wong, Duo Wai-Chi; Lam, Wing Kai; Wan, Anson Hong-Ping; Lee, Winson Chiu-Chun

2016-01-01

Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors. PMID:27023558

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Wei; Emaminejad, Sam; Nyein, Hnin Yin Yin

We report that wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health. Sampling human sweat, which is rich in physiological information13, could enable non-invasive monitoring. Previously reported sweat-based and other noninvasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state14–18. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanicallymore » flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Lastly, our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plasticbased sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.« less

Tattoo-based potentiometric ion-selective sensors for epidermal pH monitoring.

PubMed

Bandodkar, Amay J; Hung, Vinci W S; Jia, Wenzhao; Valdés-Ramírez, Gabriela; Windmiller, Joshua R; Martinez, Alexandra G; Ramírez, Julian; Chan, Garrett; Kerman, Kagan; Wang, Joseph

2013-01-07

This article presents the fabrication and characterization of novel tattoo-based solid-contact ion-selective electrodes (ISEs) for non-invasive potentiometric monitoring of epidermal pH levels. The new fabrication approach combines commercially available temporary transfer tattoo paper with conventional screen printing and solid-contact polymer ISE methodologies. The resulting tattoo-based potentiometric sensors exhibit rapid and sensitive response to a wide range of pH changes with no carry-over effects. Furthermore, the tattoo ISE sensors endure repetitive mechanical deformation, which is a key requirement of wearable and epidermal sensors. The flexible and conformal nature of the tattoo sensors enable them to be mounted on nearly any exposed skin surface for real-time pH monitoring of the human perspiration, as illustrated from the response during a strenuous physical activity. The resulting tattoo-based ISE sensors offer considerable promise as wearable potentiometric sensors suitable for diverse applications.

Flexible tension sensor based on poly(l-lactic acid) film with coaxial structure

NASA Astrophysics Data System (ADS)

Yoshida, Mitsunobu; Onishi, Katsuki; Tanimoto, Kazuhiro; Nishikawa, Shigeo

2017-10-01

We have developed a tension sensor with a coaxial structure using a narrow slit ribbon made of a uniaxially stretched poly(l-lactic acid) (PLLA) film for application to a wearable device. The tension sensor is produced as follows. We used tinsel wire as the center conductor of the sensor. The tinsel wire consists of a yarn of synthetic fibers arranged at the center, with a spirally wound rolled copper foil ribbon on the side surface. Next, slit ribbons obtained from a uniaxially oriented film of PLLA are wound helically on the side surface of the center conductor in the direction of a left-handed screw, at an angle of 45° to the central axis. The rolled copper foil is used as an outer conductor and covers the yarn without a gap. The prototype of the fabricated tension sensor has good flexibility, since the sensor is in the form of a filament and consists of a highly flexible material. For the 1 mm tension sensor, it was found that for a tension of 1 N, a charge of 14 pC was output. It was also found that the sensor maintained its room-temperature sensitivity up to 60 °C. Compared with an existing coaxial line sensor using poly(vinylidene fluoride) (PVDF), the sensor using PLLA does not exhibit pyroelectricity, meaning that no undesirable voltage is generated when in contact with body heat, which is a significant advantage as wearable sensors. The result has demonstrated the potential application of the PLLA film to wearable devices for detecting heartbeat and respiration.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors.

PubMed

Sedighi Maman, Zahra; Alamdar Yazdi, Mohammad Ali; Cavuoto, Lora A; Megahed, Fadel M

2017-11-01

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Activity Monitoring and Heart Rate Variability as Indicators of Fall Risk: Proof-of-Concept for Application of Wearable Sensors in the Acute Care Setting.

PubMed

Razjouyan, Javad; Grewal, Gurtej Singh; Rishel, Cindy; Parthasarathy, Sairam; Mohler, Jane; Najafi, Bijan

2017-07-01

Growing concern for falls in acute care settings could be addressed with objective evaluation of fall risk. The current proof-of-concept study evaluated the feasibility of using a chest-worn sensor during hospitalization to determine fall risk. Physical activity and heart rate variability (HRV) of 31 volunteers admitted to a 29-bed adult inpatient unit were recorded using a single chest-worn sensor. Sensor data during the first 24-hour recording were analyzed. Participants were stratified using the Hendrich II fall risk assessment into high and low fall risk groups. Univariate analysis revealed age, daytime activity, nighttime side lying posture, and HRV were significantly different between groups. Results suggest feasibility of wearable technology to consciously monitor physical activity, sleep postures, and HRV as potential markers of fall risk in the acute care setting. Further study is warranted to confirm the results and examine the efficacy of the proposed wearable technology to manage falls in hospitals. [Journal of Gerontological Nursing, 43(7), 53-62.]. Copyright 2017, SLACK Incorporated.

Wearable sensor-based objective assessment of motor symptoms in Parkinson's disease.

PubMed

Ossig, Christiana; Antonini, Angelo; Buhmann, Carsten; Classen, Joseph; Csoti, Ilona; Falkenburger, Björn; Schwarz, Michael; Winkler, Jürgen; Storch, Alexander

2016-01-01

Effective management and development of new treatment strategies of motor symptoms in Parkinson's disease (PD) largely depend on clinical rating instruments like the Unified PD rating scale (UPDRS) and the modified abnormal involuntary movement scale (mAIMS). Regarding inter-rater variability and continuous monitoring, clinical rating scales have various limitations. Patient-administered questionnaires such as the PD home diary to assess motor stages and fluctuations in late-stage PD are frequently used in clinical routine and as clinical trial endpoints, but diary/questionnaire are tiring, and recall bias impacts on data quality, particularly in patients with cognitive dysfunction or depression. Consequently, there is a strong need for continuous and objective monitoring of motor symptoms in PD for improving therapeutic regimen and for usage in clinical trials. Recent advances in battery technology, movement sensors such as gyroscopes, accelerometers and information technology boosted the field of objective measurement of movement in everyday life and medicine using wearable sensors allowing continuous (long-term) monitoring. This systematic review summarizes the current wearable sensor-based devices to objectively assess the various motor symptoms of PD.

Wearable Fall Detector using Integrated Sensors and Energy Devices

PubMed Central

Jung, Sungmook; Hong, Seungki; Kim, Jaemin; Lee, Sangkyu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

2015-01-01

Wearable devices have attracted great attentions as next-generation electronic devices. For the comfortable, portable, and easy-to-use system platform in wearable electronics, a key requirement is to replace conventional bulky and rigid energy devices into thin and deformable ones accompanying the capability of long-term energy supply. Here, we demonstrate a wearable fall detection system composed of a wristband-type deformable triboelectric generator and lithium ion battery in conjunction with integrated sensors, controllers, and wireless units. A stretchable conductive nylon is used as electrodes of the triboelectric generator and the interconnection between battery cells. Ethoxylated polyethylenimine, coated on the surface of the conductive nylon electrode, tunes the work function of a triboelectric generator and maximizes its performance. The electrical energy harvested from the triboelectric generator through human body motions continuously recharges the stretchable battery and prolongs hours of its use. The integrated energy supply system runs the 3-axis accelerometer and related electronics that record human body motions and send the data wirelessly. Upon the unexpected fall occurring, a custom-made software discriminates the fall signal and an emergency alert is immediately sent to an external mobile device. This wearable fall detection system would provide new opportunities in the mobile electronics and wearable healthcare. PMID:26597423

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

PubMed Central

Nyein, Hnin Yin Yin; Challa, Samyuktha; Chen, Kevin; Peck, Austin; Fahad, Hossain M.; Ota, Hiroki; Shiraki, Hiroshi; Kiriya, Daisuke; Lien, Der-Hsien; Brooks, George A.; Davis, Ronald W.; Javey, Ali

2016-01-01

Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health1–12. Sampling human sweat, which is rich in physiological information13, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state14–18. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications. PMID:26819044

Carbon Nanotube-Based Ion Selective Sensors for Wearable Applications.

PubMed

Roy, Soumyendu; David-Pur, Moshe; Hanein, Yael

2017-10-11

Wearable electronics offer new opportunities in a wide range of applications, especially sweat analysis using skin sensors. A fundamental challenge in these applications is the formation of sensitive and stable electrodes. In this article we report the development of a wearable sensor based on carbon nanotube (CNT) electrode arrays for sweat sensing. Solid-state ion selective electrodes (ISEs), sensitive to Na + ions, were prepared by drop coating plasticized poly(vinyl chloride) (PVC) doped with ionophore and ion exchanger on CNT electrodes. The ion selective membrane (ISM) filled the intertubular spaces of the highly porous CNT film and formed an attachment that was stronger than that achieved with flat Au, Pt, or carbon electrodes. Concentration of the ISM solution used influenced the attachment to the CNT film, the ISM surface morphology, and the overall performance of the sensor. Sensitivity of 56 ± 3 mV/decade to Na + ions was achieved. Optimized solid-state reference electrodes (REs), suitable for wearable applications, were prepared by coating CNT electrodes with colloidal dispersion of Ag/AgCl, agarose hydrogel with 0.5 M NaCl, and a passivation layer of PVC doped with NaCl. The CNT-based REs had low sensitivity (-1.7 ± 1.2 mV/decade) toward the NaCl solution and high repeatability and were superior to bare Ag/AgCl, metals, carbon, and CNT films, reported previously as REs. CNT-based ISEs were calibrated against CNT-based REs, and the short-term stability of the system was tested. We demonstrate that CNT-based devices implemented on a flexible support are a very attractive platform for future wearable technology devices.

Wearable knee health rehabilitation assessment using acoustical emissions

NASA Astrophysics Data System (ADS)

Teague, Caitlin N.; Hersek, Sinan; Conant, Jordan L.; Gilliland, Scott M.; Inan, Omer T.

2017-02-01

We have developed a novel, wearable sensing system based on miniature piezoelectric contact microphones for measuring the acoustical emissions from the knee during movement. The system consists of two contact microphones, positioned on the medial and lateral sides of the patella, connected to custom, analog pre-amplifier circuits and a microcontroller for digitization and data storage on a secure digital card. Tn addition to the acoustical sensing, the system includes two integrated inertial measurement sensors including accelerometer and gyroscope modalities to enable joint angle calculations; these sensors, with digital outputs, are connected directly to the same microcontroller. The system provides low noise, accurate joint acoustical emission and angle measurements in a wearable form factor and has several hours of battery life.

Wearable Wireless Telemetry System for Implantable Bio-MEMS Sensors

NASA Technical Reports Server (NTRS)

Simons, Rainee N.; Miranda, Felix A.; Wilson, Jeffrey D.; Simons, Renita E.

2006-01-01

In this paper, a telemetry and contact-less powering system consisting of an implantable bio-MEMS sensor with a miniature printed square spiral chip antenna and an external wearable garment with printed loop antenna is investigated. The wearable garment pick-up antenna and the implantable chip antenna are in close proximity to each other and hence couple inductively through their near-fields and behave as the primary and the secondary circuits of a transformer, respectively. The numerical and experimental results are graphically presented, and include the design parameter values as a function of the geometry, the relative RF magnetic near-field intensity as a function of the distance and angle, and the current density on the strip conductors, for the implantable chip antenna.

A wireless energy transfer platform, integrated at the bedside.

PubMed

De Clercq, Hans; Puers, Robert

2013-01-01

This paper presents the design of a wireless energy transfer platform, integrated at the bedside. The system contains a matrix of identical inductive power transmitters, which are optimised to provide power to a wearable sensor network, with the purpose of wirelessly recording vital signals over an extended period of time. The magnetic link, operates at a transfer frequency of 6.78MHz and is able to transfer a power of 3.3mW to the remote side at an inter-coil distance of 100mm. The total efficiency of the power link is 26%. Moreover, the platform is able to dynamically determine the position of freely moving sensor nodes and selectively induce a magnetic field in the area where the sensor nodes are positioned. As a result, the patient will not be subjected to unnecessary radiation and the specific absorption rate standards are met more easily.

Cardiac Care Assistance using Self Configured Sensor Network—a Remote Patient Monitoring System

NASA Astrophysics Data System (ADS)

Sarma Dhulipala, V. R.; Kanagachidambaresan, G. R.

2014-04-01

Pervasive health care systems are used to monitor patients remotely without disturbing the normal day-to-day activities in real-time. Wearable physiological sensors required to monitor various significant ecological parameters of the patients are connected to Body Central Unit (BCU). Body Sensor Network (BSN) updates data in real-time and are designed to transmit alerts against abnormalities which enables quick response by medical units in case of an emergency. BSN helps monitoring patient without any need for attention to the subject. BSN helps in reducing the stress and strain caused by hospital environment. In this paper, mathematical models for heartbeat signal, electro cardio graph (ECG) signal and pulse rate are introduced. These signals are compared and their RMS difference-fast Fourier transforms (PRD-FFT) are processed. In the context of cardiac arrest, alert messages of these parameters and first aid for post-surgical operations has been suggested.

Wearable vital parameters monitoring system

NASA Astrophysics Data System (ADS)

Caramaliu, Radu Vadim; Vasile, Alexandru; Bacis, Irina

2015-02-01

The system we propose monitors body temperature, heart rate and beside this, it tracks if the person who wears it suffers a faint. It uses a digital temperature sensor, a pulse sensor and a gravitational acceleration sensor to monitor the eventual faint or small heights free falls. The system continuously tracks the GPS position when available and stores the last valid data. So, when measuring abnormal vital parameters the module will send an SMS, using the GSM cellular network , with the person's social security number, the last valid GPS position for that person, the heart rate, the body temperature and, where applicable, a valid fall alert or non-valid fall alert. Even though such systems exist, they contain only faint detection or heart rate detection. Usually there is a strong correlation between low/high heart rate and an eventual faint. Combining both features into one system results in a more reliable detection device.

Sheath-Core Graphite/Silk Fiber Made by Dry-Meyer-Rod-Coating for Wearable Strain Sensors.

PubMed

Zhang, Mingchao; Wang, Chunya; Wang, Qi; Jian, Muqiang; Zhang, Yingying

2016-08-17

Recent years have witnessed the explosive development of flexible strain sensors. Nanomaterials have been widely utilized to fabricate flexible strain sensors, because of their high flexibility and electrical conductivity. However, the fabrication processes for nanomaterials and the subsequent strain sensors are generally complicated and are manufactured at high cost. In this work, we developed a facile dry-Meyer-rod-coating process to fabricate sheath-core-structured single-fiber strain sensors using ultrafine graphite flakes as the sheath and silk fibers as the core by virtue of their flexibility, high production, and low cost. The fabricated strain sensor exhibits a high sensitivity with a gauge factor of 14.5 within wide workable strain range up to 15%, and outstanding stability (up to 3000 cycles). The single-fiber-based strain sensors could be attached to a human body to detect joint motions or easily integrated into the multidirectional strain sensor for monitoring multiaxial strain, showing great potential applications as wearable strain sensors.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

PubMed Central

Banos, Oresti; Toth, Mate Attila; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2014-01-01

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements. PMID:24915181

Intelligent Medical Garments with Graphene-Functionalized Smart-Cloth ECG Sensors.

PubMed

Yapici, Murat Kaya; Alkhidir, Tamador Elboshra

2017-04-16

Biopotential signals are recorded mostly by using sticky, pre-gelled electrodes, which are not ideal for wearable, point-of-care monitoring where the usability of the personalized medical device depends critically on the level of comfort and wearability of the electrodes. We report a fully-wearable medical garment for mobile monitoring of cardiac biopotentials from the wrists or the neck with minimum restriction to regular clothing habits. The wearable prototype is based on elastic bands with graphene functionalized, textile electrodes and battery-powered, low-cost electronics for signal acquisition and wireless transmission. Comparison of the electrocardiogram (ECG) recordings obtained from the wearable prototype against conventional wet electrodes indicate excellent conformity and spectral coherence among the two signals.

Self-sensing of dielectric elastomer actuator enhanced by artificial neural network

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng

2017-09-01

Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have promising usage in future’s soft actuators and sensors, such as soft robotics, energy harvesters, and wearable sensors. In this paper, a stripe DE actuator with integrated sensing capability is designed, fabricated, and characterized. Since the strip actuator can be approximated as a compliant capacitor, it is possible to detect the actuator’s displacement by analyzing the actuator’s impedance change. An integrated sensing scheme that adds a high frequency probing signal into actuation signal is developed. Electrical impedance changes in the probing signal are extracted by fast Fourier transform algorithm, and nonlinear data fitting methods involving artificial neural network are implemented to detect the actuator’s displacement. A series of experiments show that by improving data processing and analyzing methods, the integrated sensing method can achieve error level of lower than 1%.

Estimation of Center of Mass Trajectory using Wearable Sensors during Golf Swing

PubMed Central

Najafi, Bijan; Lee-Eng, Jacqueline; Wrobel, James S.; Goebel, Ruben

2015-01-01

This study suggests a wearable sensor technology to estimate center of mass (CoM) trajectory during a golf swing. Groups of 3, 4, and 18 participants were recruited, respectively, for the purpose of three validation studies. Study 1 examined the accuracy of the system to estimate a 3D body segment angle compared to a camera-based motion analyzer (Vicon®). Study 2 assessed the accuracy of three simplified CoM trajectory models. Finally, Study 3 assessed the accuracy of the proposed CoM model during multiple golf swings. A relatively high agreement was observed between wearable sensors and the reference (Vicon®) for angle measurement (r > 0.99, random error <1.2° (1.5%) for anterior-posterior; <0.9° (2%) for medial-lateral; and <3.6° (2.5%) for internal-external direction). The two-link model yielded a better agreement with the reference system compared to one-link model (r > 0.93 v. r = 0.52, respectively). On the same note, the proposed two-link model estimated CoM trajectory during golf swing with relatively good accuracy (r > 0.9, A-P random error <1cm (7.7%) and <2cm (10.4%) for M-L). The proposed system appears to accurately quantify the kinematics of CoM trajectory as a surrogate of dynamic postural control during an athlete’s movement and its portability, makes it feasible to fit the competitive environment without restricting surface type. Key points This study demonstrates that wearable technology based on inertial sensors are accurate to estimate center of mass trajectory in complex athletic task (e.g., golf swing) This study suggests that two-link model of human body provides optimum tradeoff between accuracy and minimum number of sensor module for estimation of center of mass trajectory in particular during fast movements. Wearable technologies based on inertial sensors are viable option for assessing dynamic postural control in complex task outside of gait laboratory and constraints of cameras, surface, and base of support. PMID:25983585

Hand-arm vibration exposure monitoring with wearable sensor module.

PubMed

Austad, Hanne O; Røed, Morten H; Liverud, Anders E; Dalgard, Steffen; Seeberg, Trine M

2013-01-01

Vibration exposure is a serious risk within work physiology for several work groups. Combined with cold artic climate, the risk for permanent harm is even higher. Equipment that can monitor the vibration exposure and warn the user when at risk will provide a safer work environment for these work groups. This study evaluates whether data from a wearable wireless multi-parameter sensor module can be used to estimate vibration exposure and exposure time. This work has been focused on the characterization of the response from the accelerometer in the sensor module and the optimal location of the module in the hand-arm configuration.

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics.

PubMed

Gonzalez-Solino, Carla; Lorenzo, Mirella Di

2018-01-29

With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs) hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors.

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics

PubMed Central

Gonzalez-Solino, Carla; Lorenzo, Mirella Di

2018-01-01

With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs) hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors. PMID:29382147

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.

PubMed

Nait Aicha, Ahmed; Englebienne, Gwenn; van Schooten, Kimberley S; Pijnappels, Mirjam; Kröse, Ben

2018-05-22

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

PubMed Central

Englebienne, Gwenn; Pijnappels, Mirjam

2018-01-01

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. PMID:29786659

Large-Area All-Textile Pressure Sensors for Monitoring Human Motion and Physiological Signals.

PubMed

Liu, Mengmeng; Pu, Xiong; Jiang, Chunyan; Liu, Ting; Huang, Xin; Chen, Libo; Du, Chunhua; Sun, Jiangman; Hu, Weiguo; Wang, Zhong Lin

2017-11-01

Wearable pressure sensors, which can perceive and respond to environmental stimuli, are essential components of smart textiles. Here, large-area all-textile-based pressure-sensor arrays are successfully realized on common fabric substrates. The textile sensor unit achieves high sensitivity (14.4 kPa -1 ), low detection limit (2 Pa), fast response (≈24 ms), low power consumption (<6 µW), and mechanical stability under harsh deformations. Thanks to these merits, the textile sensor is demonstrated to be able to recognize finger movement, hand gestures, acoustic vibrations, and real-time pulse wave. Furthermore, large-area sensor arrays are successfully fabricated on one textile substrate to spatially map tactile stimuli and can be directly incorporated into a fabric garment for stylish designs without sacrifice of comfort, suggesting great potential in smart textiles or wearable electronics. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Intelligent monitoring system of bedridden elderly

NASA Astrophysics Data System (ADS)

Dong, Rue Shao; Tanaka, Motohiro; Ushijima, Miki; Ishimatsu, Takakazu

2005-12-01

In this paper we propose a system to detect physical behavior of the elderly under bedridden status. This system is used to prevent those elderly from falling down and being wounded. Basic idea of our approach is to measure the body movements of the elderly using the acceleration sensor. Based on the data measured, dangerous actions of the elderly are extracted and warning signals to the caseworkers are generated via wireless signals. A feature of the system is that the senor part is compactly assembled as a wearable unit. Another feature of the system is that the system adopts a simplified wireless network system. Due to the network capability the system can monitor physical movements of multi-patients. Applicability of the system is now being examined at hospitals.

Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty

PubMed Central

Chiang, Chih-Yen; Chen, Kun-Hui; Liu, Kai-Chun; Hsu, Steen Jun-Ping; Chan, Chia-Tai

2017-01-01

Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements. PMID:28241434

Fabrication of Ultra-Thin Printed Organic TFT CMOS Logic Circuits Optimized for Low-Voltage Wearable Sensor Applications.

PubMed

Takeda, Yasunori; Hayasaka, Kazuma; Shiwaku, Rei; Yokosawa, Koji; Shiba, Takeo; Mamada, Masashi; Kumaki, Daisuke; Fukuda, Kenjiro; Tokito, Shizuo

2016-05-09

Ultrathin electronic circuits that can be manufactured by using conventional printing technologies are key elements necessary to realize wearable health sensors and next-generation flexible electronic devices. Due to their low level of power consumption, complementary (CMOS) circuits using both types of semiconductors can be easily employed in wireless devices. Here, we describe ultrathin CMOS logic circuits, for which not only the source/drain electrodes but also the semiconductor layers were printed. Both p-type and n-type organic thin film transistor devices were employed in a D-flip flop circuit in the newly developed stacked structure and exhibited excellent electrical characteristics, including good carrier mobilities of 0.34 and 0.21 cm(2) V(-1) sec(-1), and threshold voltages of nearly 0 V with low operating voltages. These printed organic CMOS D-flip flop circuits exhibit operating frequencies of 75 Hz and demonstrate great potential for flexible and printed electronics technology, particularly for wearable sensor applications with wireless connectivity.

Fabrication of Ultra-Thin Printed Organic TFT CMOS Logic Circuits Optimized for Low-Voltage Wearable Sensor Applications

PubMed Central

Takeda, Yasunori; Hayasaka, Kazuma; Shiwaku, Rei; Yokosawa, Koji; Shiba, Takeo; Mamada, Masashi; Kumaki, Daisuke; Fukuda, Kenjiro; Tokito, Shizuo

2016-01-01

Ultrathin electronic circuits that can be manufactured by using conventional printing technologies are key elements necessary to realize wearable health sensors and next-generation flexible electronic devices. Due to their low level of power consumption, complementary (CMOS) circuits using both types of semiconductors can be easily employed in wireless devices. Here, we describe ultrathin CMOS logic circuits, for which not only the source/drain electrodes but also the semiconductor layers were printed. Both p-type and n-type organic thin film transistor devices were employed in a D-flip flop circuit in the newly developed stacked structure and exhibited excellent electrical characteristics, including good carrier mobilities of 0.34 and 0.21 cm2 V−1 sec−1, and threshold voltages of nearly 0 V with low operating voltages. These printed organic CMOS D-flip flop circuits exhibit operating frequencies of 75 Hz and demonstrate great potential for flexible and printed electronics technology, particularly for wearable sensor applications with wireless connectivity. PMID:27157914

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors.

PubMed

Phinyomark, Angkoon; N Khushaba, Rami; Scheme, Erik

2018-05-18

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly ( p.

Improving the Capture and Re-Use of Data with Wearable Computers

NASA Technical Reports Server (NTRS)

Pfarr, Barbara; Fating, Curtis C.; Green, Daniel; Powers, Edward I. (Technical Monitor)

2001-01-01

At the Goddard Space Flight Center, members of the Real-Time Software Engineering Branch are developing a wearable, wireless, voice-activated computer for use in a wide range of crosscutting space applications that would benefit from having instant Internet, network, and computer access with complete mobility and hands-free operations. These applications can be applied across many fields and disciplines including spacecraft fabrication, integration and testing (including environmental testing), and astronaut on-orbit control and monitoring of experiments with ground based experimenters. To satisfy the needs of NASA customers, this wearable computer needs to be connected to a wireless network, to transmit and receive real-time video over the network, and to receive updated documents via the Internet or NASA servers. The voice-activated computer, with a unique vocabulary, will allow the users to access documentation in a hands free environment and interact in real-time with remote users. We will discuss wearable computer development, hardware and software issues, wireless network limitations, video/audio solutions and difficulties in language development.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

PubMed

Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Studies on a wearable, electronic, transdermal alcohol sensor.

PubMed

Swift, R M; Martin, C S; Swette, L; LaConti, A; Kackley, N

1992-08-01

The measurement of alcohol consumption over long time periods is important for monitoring treatment outcome and for research applications. Giner, Inc. has developed a wearable device that senses ethanol vapor at the surface of the skin, using an electrochemical cell that produces a continuous current signal proportional to ethanol concentration. A thermistor monitors continuous contact of the sensor with the skin, and a data-acquisition/logic circuit stores days of data recorded at 2- to 5-min intervals. Testing of this novel ethanol sensor/recorder was conducted on nonalcoholic human subjects consuming known quantities of ethanol and on intoxicated alcoholic subjects. The transdermal sensor signal closely follows the pattern of the blood alcohol concentration curve, although with a delay. This paper describes the concept of electrochemical ethanol measurement and presents some of the clinical data collected in support of the sensor/recorder development.

Potentiometric sensors using cotton yarns, carbon nanotubes and polymeric membranes.

PubMed

Guinovart, Tomàs; Parrilla, Marc; Crespo, Gastón A; Rius, F Xavier; Andrade, Francisco J

2013-09-21

A simple and generalized approach to build electrochemical sensors for wearable devices is presented. Commercial cotton yarns are first turned into electrical conductors through a simple dyeing process using a carbon nanotube ink. These conductive yarns are then partially coated with a suitable polymeric membrane to build ion-selective electrodes. Potentiometric measurements using these yarn-potentiometric sensors are demonstrated. Examples of yarns that can sense pH, K(+) and NH4(+) are presented. In all cases, these sensing yarns show limits of detection and linear ranges that are similar to those obtained with lab-made solid-state ion-selective electrodes. Through the immobilization of these sensors in a band-aid, it is shown that this approach could be easily implemented in a wearable device. Factors affecting the performance of the sensors and future potential applications are discussed.

Novel approach to ambulatory assessment of human segmental orientation on a wearable sensor system.

PubMed

Liu, Kun; Liu, Tao; Shibata, Kyoko; Inoue, Yoshio; Zheng, Rencheng

2009-12-11

A new method using a double-sensor difference based algorithm for analyzing human segment rotational angles in two directions for segmental orientation analysis in the three-dimensional (3D) space was presented. A wearable sensor system based only on triaxial accelerometers was developed to obtain the pitch and yaw angles of thigh segment with an accelerometer approximating translational acceleration of the hip joint and two accelerometers measuring the actual accelerations on the thigh. To evaluate the method, the system was first tested on a 2 degrees of freedom mechanical arm assembled out of rigid segments and encoders. Then, to estimate the human segmental orientation, the wearable sensor system was tested on the thighs of eight volunteer subjects, who walked in a straight forward line in the work space of an optical motion analysis system at three self-selected speeds: slow, normal and fast. In the experiment, the subject was assumed to walk in a straight forward way with very little trunk sway, skin artifacts and no significant internal/external rotation of the leg. The root mean square (RMS) errors of the thigh segment orientation measurement were between 2.4 degrees and 4.9 degrees during normal gait that had a 45 degrees flexion/extension range of motion. Measurement error was observed to increase with increasing walking speed probably because of the result of increased trunk sway, axial rotation and skin artifacts. The results show that, without integration and switching between different sensors, using only one kind of sensor, the wearable sensor system is suitable for ambulatory analysis of normal gait orientation of thigh and shank in two directions of the segment-fixed local coordinate system in 3D space. It can then be applied to assess spatio-temporal gait parameters and monitoring the gait function of patients in clinical settings.

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model.

PubMed

Rose, Michael; Curtze, Carolin; O'Sullivan, Joseph; El-Gohary, Mahmoud; Crawford, Dennis; Friess, Darin; Brady, Jacqueline M

2017-12-01

To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy. Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice. For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups. Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy. Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?

PubMed

Burridge, Jane H; Lee, Alan Chong W; Turk, Ruth; Stokes, Maria; Whitall, Jill; Vaidyanathan, Ravi; Clatworthy, Phil; Hughes, Ann-Marie; Meagher, Claire; Franco, Enrico; Yardley, Lucy

2017-07-01

Stroke, predominantly a condition of older age, is a major cause of acquired disability in the global population and puts an increasing burden on health care resources. Clear evidence for the importance of intensity of therapy in optimizing functional outcomes is found in animal models, supported by neuroimaging and behavioral research, and strengthened by recent meta-analyses from multiple clinical trials. However, providing intensive therapy using conventional treatment paradigms is expensive and sometimes not feasible because of social and environmental factors. This article addresses the need for cost-effective increased intensity of practice and suggests potential benefits of telehealth (TH) as an innovative model of care in physical therapy. We provide an overview of TH and present evidence that a web-supported program, used in conjunction with constraint-induced therapy (CIT), can increase intensity and adherence to a rehabilitation regimen. The design and feasibility testing of this web-based program, "LifeCIT," is presented. We describe how wearable sensors can monitor activity and provide feedback to patients and therapists. The methodology for the development of a wearable device with embedded inertial and mechanomyographic sensors, algorithms to classify functional movement, and a graphical user interface to present meaningful data to patients to support a home exercise program is explained. We propose that wearable sensor technologies and TH programs have the potential to provide most-effective, intensive, home-based stroke rehabilitation.

Optimization and evaluation of the human fall detection system

NASA Astrophysics Data System (ADS)

Alzoubi, Hadeel; Ramzan, Naeem; Shahriar, Hasan; Alzubi, Raid; Gibson, Ryan; Amira, Abbes

2016-10-01

Falls are the most critical health problem for elderly people, which are often, cause significant injuries. To tackle a serious risk that made by the fall, we develop an automatic wearable fall detection system utilizing two devices (mobile phone and wireless sensor) based on three axes accelerometer signals. The goal of this study is to find an effective machine learning method that distinguish falls from activities of daily living (ADL) using only a single triaxial accelerometer. In addition, comparing the performance results for wearable sensor and mobile device data .The proposed model detects the fall by using seven different classifiers and the significant performance is demonstrated using accuracy, recall, precision and F-measure. Our model obtained accuracy over 99% on wearable device data and over 97% on mobile phone data.

Self-Healable Sensors Based Nanoparticles for Detecting Physiological Markers via Skin and Breath: Toward Disease Prevention via Wearable Devices.

PubMed

Jin, Han; Huynh, Tan-Phat; Haick, Hossam

2016-07-13

Flexible and wearable electronic sensors are useful for the early diagnosis and monitoring of an individual's health state. Sampling of volatile organic compounds (VOCs) derived from human breath/skin or monitoring abrupt changes in heart-beat/breath rate should allow noninvasive monitoring of disease states at an early stage. Nevertheless, for many reported wearable sensing devices, interaction with the human body leads incidentally to unavoidable scratches and/or mechanical cuts and bring about malfunction of these devices. We now offer proof-of-concept of nanoparticle-based flexible sensor arrays with fascinating self-healing abilities. By integrating a self-healable polymer substrate with 5 kinds of functionalized gold nanoparticle films, a sensor array gives a fast self-healing (<3 h) and attractive healing efficiency in both the substrate and sensing films. The proposed platform was used in sensing pressure variation and 11 kinds of VOCs. The sensor array had satisfactory sensitivity, a low detection limit, and promising discrimination features in monitoring both of VOCs and pressure variation, even after full healing. These results presage a new type of smart sensing device, with a desirable performance in the possible detection and/or clinical application for a number of different purposes.

A wearable biochemical sensor for monitoring alcohol consumption lifestyle through Ethyl glucuronide (EtG) detection in human sweat

PubMed Central

Panneer Selvam, Anjan; Muthukumar, Sriram; Kamakoti, Vikramshankar; Prasad, Shalini

2016-01-01

We demonstrate for the first time a wearable biochemical sensor for monitoring alcohol consumption through the detection and quantification of a metabolite of ethanol, ethyl glucuronide (EtG). We designed and fabricated two co-planar sensors with gold and zinc oxide as sensing electrodes. We also designed a LED based reporting for the presence of EtG in the human sweat samples. The sensor functions on affinity based immunoassay principles whereby monoclonal antibodies for EtG were immobilized on the electrodes using thiol based chemistry. Detection of EtG from human sweat was achieved through chemiresistive sensing mechanism. In this method, an AC voltage was applied across the two coplanar electrodes and the impedance across the sensor electrodes was measured and calibrated for physiologically relevant doses of EtG in human sweat. EtG detection over a dose concentration of 0.001–100 μg/L was demonstrated on both glass and polyimide substrates. Detection sensitivity was lower at 1 μg/L with gold electrodes as compared to ZnO, which had detection sensitivity of 0.001 μg/L. Based on the detection range the wearable sensor has the ability to detect alcohol consumption of up to 11 standard drinks in the US over a period of 4 to 9 hours. PMID:26996103

A flexible transcutaneous oxygen sensor using polymer membranes.

PubMed

Kudo, Hiroyuki; Iguchi, Shigehito; Yamada, Takua; Kawase, Tatsuya; Saito, Hirokazu; Otsuka, Kimio; Mitsubayashi, Kohji

2007-02-01

A wearable and flexible oxygen sensor for transcutaneous blood gas monitoring was fabricated and tested. The sensor has a laminar film-like structure, which was fabricated by pouching KCl electrolyte solution by both non-permeable (metal weldable) sheet and gas-permeable membrane with Pt- and Ag/AgCl-electrodes patterned using microfabrication process. The electrolyte solution was fixed only by heat-sealing the edges of the weldable membranes without any chemical adhesives. The wearable oxygen sensor (thickness: 84 mum) was applied to the electrochemical measurement with a constant potential of -600 mV vs. Ag/AgCl, thus obtaining the calibration range to dissolved oxygen (DO) from 0.0 to 7.0 mg/l with a correlation coefficient of 0.998 and the quick response time (53.4 s to 90% of a steady-state current), which operate similarly to a commercially available oxygen electrode. The sensor was also utilized to transcutaneous oxygen monitoring for healthy human subject. The sensing region of the wearable oxygen sensor was attached onto the forearm-skin surface of the subject inhaling various concentrations of oxygen. As a result of physiological application, the output current was varied from -6.2 microA to -7.8 microA within 2 min when the concentration of inhaling oxygen was changed from atmospheric air to 60% oxygen. Thus, the transcutaneous oxygen was successfully monitored without any inconveniences such as skin inflammation, etc.

A wearable biochemical sensor for monitoring alcohol consumption lifestyle through Ethyl glucuronide (EtG) detection in human sweat

NASA Astrophysics Data System (ADS)

Panneer Selvam, Anjan; Muthukumar, Sriram; Kamakoti, Vikramshankar; Prasad, Shalini

2016-03-01

We demonstrate for the first time a wearable biochemical sensor for monitoring alcohol consumption through the detection and quantification of a metabolite of ethanol, ethyl glucuronide (EtG). We designed and fabricated two co-planar sensors with gold and zinc oxide as sensing electrodes. We also designed a LED based reporting for the presence of EtG in the human sweat samples. The sensor functions on affinity based immunoassay principles whereby monoclonal antibodies for EtG were immobilized on the electrodes using thiol based chemistry. Detection of EtG from human sweat was achieved through chemiresistive sensing mechanism. In this method, an AC voltage was applied across the two coplanar electrodes and the impedance across the sensor electrodes was measured and calibrated for physiologically relevant doses of EtG in human sweat. EtG detection over a dose concentration of 0.001-100 μg/L was demonstrated on both glass and polyimide substrates. Detection sensitivity was lower at 1 μg/L with gold electrodes as compared to ZnO, which had detection sensitivity of 0.001 μg/L. Based on the detection range the wearable sensor has the ability to detect alcohol consumption of up to 11 standard drinks in the US over a period of 4 to 9 hours.

New generation of wearable goniometers for motion capture systems

PubMed Central

2014-01-01

Background Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient’s home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented. Methods KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed. Results The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations. Conclusions Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented. PMID:24725669

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

PubMed

Beltrame, Thomas; Amelard, Robert; Wong, Alexander; Hughson, Richard L

2018-02-01

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o 2 ) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o 2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o 2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC HIP ), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o 2 . The aerobic system analysis was based on the frequency-domain analysis of ACC HIP and predicted V̇o 2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o 2 data during μADL correlated with the temporal characteristics of measured V̇o 2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Implementation of a wireless sensor network for heart rate monitoring in a senior center.

PubMed

Huang, Jyh-How; Su, Tzu-Yao; Raknim, Paweeya; Lan, Kun-Chan

2015-06-01

Wearable sensor systems are widely used to monitor vital sign in hospitals and in recent years have also been used at home. In this article we present a system that includes a ring probe, sensor, radio, and receiver, designed for use as a long-term heart rate monitoring system in a senior center. The primary contribution of this article is successfully implementing a cheap, large-scale wireless heart rate monitoring system that is stable and comfortable to use 24 h a day. We developed new finger ring sensors for comfortable continuous wearing experience and used dynamic power adjustment on the ring so the sensor can detect pulses at different strength levels. Our system has been deployed in a senior center since May 2012, and 63 seniors have used this system in this period. During the 54-h system observation period, 10 alarms were set off. Eight of them were due to abnormal heart rate, and two of them were due to loose probes. The monitoring system runs stably with the senior center's existing WiFi network, and achieves 99.48% system availability. The managers and caregivers use our system as a reliable warning system for clinical deterioration. The results of the year-long deployment show that the wireless group heart rate monitoring system developed in this work is viable for use within a designated area.

Lead zirconate titanate nanowire textile nanogenerator for wearable energy-harvesting and self-powered devices.

PubMed

Wu, Weiwei; Bai, Suo; Yuan, Miaomiao; Qin, Yong; Wang, Zhong Lin; Jing, Tao

2012-07-24

Wearable nanogenerators are of vital importance to portable energy-harvesting and personal electronics. Here we report a method to synthesize a lead zirconate titanate textile in which nanowires are parallel with each other and a procedure to make it into flexible and wearable nanogenerators. The nanogenerator can generate 6 V output voltage and 45 nA output current, which are large enough to power a liquid crystal display and a UV sensor.

Adaptive cancellation of motion artifact in wearable biosensors.

PubMed

Yousefi, Rasoul; Nourani, Mehrdad; Panahi, Issa

2012-01-01

The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

The Feasibility of Wearables in an Enterprise Environment and Their Impact on IT Security

NASA Technical Reports Server (NTRS)

Scotti, Vincent, Jr.

2015-01-01

This paper is intended to explore the usability and feasibility of wearables in an enterprise environment and their impact on IT Security. In this day and age, with the advent of the Internet of Things, we must explore all the new technology emerging from the minds of the new inventors. This means exploring the use of wearables in regards to their benefits, limitations, and the new challenges they pose to securing computer networks in the Federal environment. We will explore the design of the wearables, the interfaces needed to connect them, and what it will take to connect personal devices in the Federal enterprise network environment. We will provide an overview of the wearable design, concerns of ensuring the confidentiality, integrity, and availability of information and the challenges faced by those doing so. We will also review the implications and limitations of the policies governing wearable technology and the physical efforts to enforce them.

Intelligent Medical Garments with Graphene-Functionalized Smart-Cloth ECG Sensors

PubMed Central

Yapici, Murat Kaya; Alkhidir, Tamador Elboshra

2017-01-01

Biopotential signals are recorded mostly by using sticky, pre-gelled electrodes, which are not ideal for wearable, point-of-care monitoring where the usability of the personalized medical device depends critically on the level of comfort and wearability of the electrodes. We report a fully-wearable medical garment for mobile monitoring of cardiac biopotentials from the wrists or the neck with minimum restriction to regular clothing habits. The wearable prototype is based on elastic bands with graphene functionalized, textile electrodes and battery-powered, low-cost electronics for signal acquisition and wireless transmission. Comparison of the electrocardiogram (ECG) recordings obtained from the wearable prototype against conventional wet electrodes indicate excellent conformity and spectral coherence among the two signals. PMID:28420158

Integration of biochemical sensors into wearable biomaterial platforms

NASA Astrophysics Data System (ADS)

Jandhyala, Sidhartha; Walper, Scott A.; Cargill, Allison A.; Ozual, Abigail; Daniele, Michael A.

2016-05-01

With rapidly inflating healthcare costs, a limited supply of physicians and an alarming surge in lifestyle diseases, radical changes must be made to improve preventative medicine and ensure a sustainable healthcare system. A compelling solution is to equip the population with wearable health monitors to continuously record representative and actionable physiological data. Herein, we present a preliminary design and evaluation of a biochemical sensor node enabled by a substrate comprised of a nanocellulose thin-film that conforms to the skin and carries a printed sensor element. The nanocellulose layer ensures conformal and biocompatible contact with the skin, while a printed layer provides a high surface-area electrode. While the recognition/transduction element can be exchanged for many different sensing motifs, we utilize the general structure of an electrochemical glucose sensor.

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

PubMed Central

Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

2013-01-01

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems. PMID:24351646

Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges.

PubMed

Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

2013-12-17

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

PubMed Central

Casson, Alexander J.

2015-01-01

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via gmC circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans. PMID:26694414

An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

PubMed

Casson, Alexander J

2015-12-17

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.

A sneak peek into digital innovations and wearable sensors for cardiac monitoring.

PubMed

Michard, Frederic

2017-04-01

Many mobile phone or tablet applications have been designed to control cardiovascular risk factors (obesity, smoking, sedentary lifestyle, diabetes and hypertension) or to optimize treatment adherence. Some have been shown to be useful but the long-term benefits remain to be demonstrated. Digital stethoscopes make easier the interpretation of abnormal heart sounds, and the development of pocket-sized echo machines may quickly and significantly expand the use of ultrasounds. Daily home monitoring of pulmonary artery pressures with wireless implantable sensors has been shown to be associated with a significant decrease in hospital readmissions for heart failure. There are more and more non-invasive, wireless, and wearable sensors designed to monitor heart rate, heart rate variability, respiratory rate, arterial oxygen saturation, and thoracic fluid content. They have the potential to change the way we monitor and treat patients with cardiovascular diseases in the hospital and beyond. Some may have the ability to improve quality of care, decrease the number of medical visits and hospitalization, and ultimately health care costs. Validation and outcome studies are needed to clarify, among the growing number of digital innovations and wearable sensors, which tools have real clinical value.

Capacitive wearable tactile sensor based on smart textile substrate with carbon black /silicone rubber composite dielectric

NASA Astrophysics Data System (ADS)

Guo, Xiaohui; Huang, Ying; Cai, Xia; Liu, Caixia; Liu, Ping

2016-04-01

To achieve the wearable comfort of electronic skin (e-skin), a capacitive sensor printed on a flexible textile substrate with a carbon black (CB)/silicone rubber (SR) composite dielectric was demonstrated in this paper. Organo-silicone conductive silver adhesive serves as a flexible electrodes/shielding layer. The structure design, sensing mechanism and the influence of the conductive filler content and temperature variations on the sensor performance were investigated. The proposed device can effectively enhance the flexibility and comfort of wearing the device asthe sensing element has achieved a sensitivity of 0.02536%/KPa, a hysteresis error of 5.6%, and a dynamic response time of ~89 ms at the range of 0-700 KPa. The drift induced by temperature variations has been calibrated by presenting the temperature compensation model. The research on the time-space distribution of plantar pressure information and the experiment of the manipulator soft-grasping were implemented with the introduced device, and the experimental results indicate that the capacitive flexible textile tactile sensor has good stability and tactile perception capacity. This study provides a good candidate for wearable artificial skin.

Evaluating physical function and activity in the elderly patient using wearable motion sensors.

PubMed

Grimm, Bernd; Bolink, Stijn

2016-05-01

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the 'micro-level' of activity such as step frequency or sit-stand duration.Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation. Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112-120. DOI: 10.1302/2058-5241.1.160022.

Evaluating physical function and activity in the elderly patient using wearable motion sensors

PubMed Central

Grimm, Bernd; Bolink, Stijn

2016-01-01

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand. Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures. In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts. In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration. Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation. Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022. PMID:28461937

Totally Connected Healthcare with TV White Spaces.

PubMed

Katzis, Konstantinos; Jones, Richard W; Despotou, Georgios

2017-01-01

Recent technological advances in electronics, wireless communications and low cost medical sensors generated a plethora of Wearable Medical Devices (WMDs), which are capable of generating considerably large amounts of new, unstructured real-time data. This contribution outlines how this data can be propagated to a healthcare system through the internet, using long distance Radio Access Networks (RANs) and proposes a novel communication system architecture employing White Space Devices (WSD) to provide seamless connectivity to its users. Initial findings indicate that the proposed communication system can facilitate broadband services over a large geographical area taking advantage of the freely available TV White Spaces (TVWS).

Nonenzymatic Wearable Sensor for Electrochemical Analysis of Perspiration Glucose.

PubMed

Zhu, Xiaofei; Ju, Yinhui; Chen, Jian; Liu, Deye; Liu, Hong

2018-05-25

We report a nonenzymatic wearable sensor for electrochemical analysis of perspiration glucose. Multipotential steps are applied on a Au electrode, including a high negative pretreatment potential step for proton reduction which produces a localized alkaline condition, a moderate potential step for electrocatalytic oxidation of glucose under the alkaline condition, and a positive potential step to clean and reactivate the electrode surface for the next detection. Fluorocarbon-based materials were coated on the Au electrode for improving the selectivity and robustness of the sensor. A fully integrated wristband is developed for continuous real-time monitoring of perspiration glucose during physical activities, and uploading the test result to a smartphone app via Bluetooth.

A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption.

PubMed

Baig, Mirza Mansoor; GholamHosseini, Hamid; Moqeem, Aasia A; Mirza, Farhaan; Lindén, Maria

2017-07-01

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

Flexible heartbeat sensor for wearable device.

PubMed

Kwak, Yeon Hwa; Kim, Wonhyo; Park, Kwang Bum; Kim, Kunnyun; Seo, Sungkyu

2017-08-15

We demonstrate a flexible strain-gauge sensor and its use in a wearable application for heart rate detection. This polymer-based strain-gauge sensor was fabricated using a double-sided fabrication method with polymer and metal, i.e., polyimide and nickel-chrome. The fabrication process for this strain-gauge sensor is compatible with the conventional flexible printed circuit board (FPCB) processes facilitating its commercialization. The fabricated sensor showed a linear relation for an applied normal force of more than 930 kPa, with a minimum detectable force of 6.25Pa. This sensor can also linearly detect a bending radius from 5mm to 100mm. It is a thin, flexible, compact, and inexpensive (for mass production) heart rate detection sensor that is highly sensitive compared to the established optical photoplethysmography (PPG) sensors. It can detect not only the timing of heart pulsation, but also the amplitude or shape of the pulse signal. The proposed strain-gauge sensor can be applicable to various applications for smart devices requiring heartbeat detection. Copyright © 2017 Elsevier B.V. All rights reserved.

Sensor-Data Fusion for Multi-Person Indoor Location Estimation.

PubMed

Mohebbi, Parisa; Stroulia, Eleni; Nikolaidis, Ioanis

2017-10-18

We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other "wearable" device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors' coverage of the monitored space and the quality of the location estimates.

Wearable Stretch Sensors for Motion Measurement of the Wrist Joint Based on Dielectric Elastomers.

PubMed

Huang, Bo; Li, Mingyu; Mei, Tao; McCoul, David; Qin, Shihao; Zhao, Zhanfeng; Zhao, Jianwen

2017-11-23

Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness, light weight, and compliance. In this paper, DESs were applied to measure all component motions of the wrist joints. Five sensors were mounted to different positions on the wrist, and each one is for one component motion. To find the best position to mount the sensors, the distribution of the muscles is analyzed. Even so, the component motions and the deformation of the sensors are coupled; therefore, a decoupling method was developed. By the decoupling algorithm, all component motions can be measured with a precision of 5°, which meets the requirements of general motion capture systems.

Solvent-free fabrication of biodegradable hot-film flow sensor for noninvasive respiratory monitoring

NASA Astrophysics Data System (ADS)

Dinh, Toan; Phan, Hoang-Phuong; Nguyen, Tuan-Khoa; Qamar, Afzaal; Woodfield, Peter; Zhu, Yong; Nguyen, Nam-Trung; Viet Dao, Dzung

2017-06-01

In this paper, we report on a low-cost, environment-friendly and wearable thermal flow sensor, which can be manufactured in-house using pencil graphite as a sensing hot film and biodegradable printing paper as a substrate, without using any toxic solvents or cleanroom facilities. The hot film flow sensor offers excellent performance such as high signal-to-noise ratio (≥slant 40 for an air flow velocity of 1 m s-1), high sensitivity to airflow (53.7 mV(m s-1)-0.8) and outstanding long-term stability (almost no drift in 24 h). The sensor can be comfortably affixed to the philtrum of patients and measures human respiration in realtime. The results indicate that the wearable thermal flow sensors fabricated by this solvent-free and user-friendly method could be employed in human respiratory monitoring.

Respiratory rate detection using a wearable electromagnetic generator.

PubMed

Padasdao, Bryson; Boric-Lubecke, Olga

2011-01-01

Wearable health and fitness monitoring systems are a promising new way of collecting physiological data without inconveniencing patients. Human energy harvesting may be used to power wearable sensors. In this paper, we explore this zero-net energy biosensor concept through sensing and harvesting of respiratory effort. An off the shelf servo motor operation in reverse was used to successfully obtain respiratory rate, while also demonstrating significant harvested power. These are the first reported respiratory rate sensing results using electromagnetic generators.

Sweat test for cystic fibrosis: Wearable sweat sensor vs. standard laboratory test.

PubMed

Choi, Dong-Hoon; Thaxton, Abigail; Jeong, In Cheol; Kim, Kain; Sosnay, Patrick R; Cutting, Garry R; Searson, Peter C

2018-03-23

Sweat chloride testing for diagnosis of cystic fibrosis (CF) involves sweat induction, collection and handling, and measurement in an analytical lab. We have developed a wearable sensor with an integrated salt bridge for real-time measurement of sweat chloride concentration. Here, in a proof-of-concept study, we compare the performance of the sensor to current clinical practice in CF patients and healthy subjects. Sweat was induced on both forearms of 10 individuals with CF and 10 healthy subjects using pilocarpine iontophoresis. A Macroduct sweat collection device was attached to one arm and sweat was collected for 30 min and then sent for laboratory analysis. A sensor was attached to the other arm and the chloride ion concentration monitored in real time for 30 min using a Bluetooth transceiver and smart phone app. Stable sweat chloride measurements were obtained within 15 min following sweat induction using the wearable sensor. We define the detection time as the time at which the standard deviation of the real-time chloride ion concentration remained below 2 mEq/L for 5 min. The sweat volume for sensor measurements at the detection time was 13.1 ± 11.4 μL (SD), in many cases lower than the minimum sweat volume of 15 μL for conventional testing. The mean difference between sweat chloride concentrations measured by the sensor and the conventional laboratory practice was 6.2 ± 9.5 mEq/L (SD), close to the arm-to-arm variation of about 3 mEq/L. The Pearson correlation coefficient between the two measurements was 0.97 highlighting the excellent agreement between the two methods. A wearable sensor can be used to make real-time measurements of sweat chloride within 15 min following sweat induction, requiring a small sweat volume, and with excellent agreement to standard methods. Copyright © 2018 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Wearable Sensors for Chemical & Biological Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ozanich, Richard M.

2017-08-31

One of PNNL’s strengths is the ability to conduct comprehensive technology foraging and objective assessments of various technology areas. The following examples highlight leading research by others in the area of chemical and biological (chem/bio) detection that could be further developed into a robust, highly integrated wearables to aid preparedness, response and recovery.

Interoperability and security in wireless body area network infrastructures.

PubMed

Warren, Steve; Lebak, Jeffrey; Yao, Jianchu; Creekmore, Jonathan; Milenkovic, Aleksandar; Jovanov, Emil

2005-01-01

Wireless body area networks (WBANs) and their supporting information infrastructures offer unprecedented opportunities to monitor state of health without constraining the activities of a wearer. These mobile point-of-care systems are now realizable due to the convergence of technologies such as low-power wireless communication standards, plug-and-play device buses, off-the-shelf development kits for low-power microcontrollers, handheld computers, electronic medical records, and the Internet. To increase acceptance of personal monitoring technology while lowering equipment cost, advances must be made in interoperability (at both the system and device levels) and security. This paper presents an overview of WBAN infrastructure work in these areas currently underway in the Medical Component Design Laboratory at Kansas State University (KSU) and at the University of Alabama in Huntsville (UAH). KSU efforts include the development of wearable health status monitoring systems that utilize ISO/IEEE 11073, Bluetooth, Health Level 7, and OpenEMed. WBAN efforts at UAH include the development of wearable activity and health monitors that incorporate ZigBee-compliant wireless sensor platforms with hardware-level encryption and the TinyOS development environment. WBAN infrastructures are complex, requiring many functional support elements. To realize these infrastructures through collaborative efforts, organizations such as KSU and UAH must define and utilize standard interfaces, nomenclature, and security approaches.

Scalable Production of Graphene-Based Wearable E-Textiles.

PubMed

Karim, Nazmul; Afroj, Shaila; Tan, Sirui; He, Pei; Fernando, Anura; Carr, Chris; Novoselov, Kostya S

2017-12-26

Graphene-based wearable e-textiles are considered to be promising due to their advantages over traditional metal-based technology. However, the manufacturing process is complex and currently not suitable for industrial scale application. Here we report a simple, scalable, and cost-effective method of producing graphene-based wearable e-textiles through the chemical reduction of graphene oxide (GO) to make stable reduced graphene oxide (rGO) dispersion which can then be applied to the textile fabric using a simple pad-dry technique. This application method allows the potential manufacture of conductive graphene e-textiles at commercial production rates of ∼150 m/min. The graphene e-textile materials produced are durable and washable with acceptable softness/hand feel. The rGO coating enhanced the tensile strength of cotton fabric and also the flexibility due to the increase in strain% at maximum load. We demonstrate the potential application of these graphene e-textiles for wearable electronics with activity monitoring sensor. This could potentially lead to a multifunctional single graphene e-textile garment that can act both as sensors and flexible heating elements powered by the energy stored in graphene textile supercapacitors.

Ultrathin Quantum Dot Display Integrated with Wearable Electronics.

PubMed

Kim, Jaemin; Shim, Hyung Joon; Yang, Jiwoong; Choi, Moon Kee; Kim, Dong Chan; Kim, Junhee; Hyeon, Taeghwan; Kim, Dae-Hyeong

2017-10-01

An ultrathin skin-attachable display is a critical component for an information output port in next-generation wearable electronics. In this regard, quantum dot (QD) light-emitting diodes (QLEDs) offer unique and attractive characteristics for future displays, including high color purity with narrow bandwidths, high electroluminescence (EL) brightness at low operating voltages, and easy processability. Here, ultrathin QLED displays that utilize a passive matrix to address individual pixels are reported. The ultrathin thickness (≈5.5 µm) of the QLED display enables its conformal contact with the wearer's skin and prevents its failure under vigorous mechanical deformation. QDs with relatively thick shells are employed to improve EL characteristics (brightness up to 44 719 cd m -2 at 9 V, which is the record highest among wearable LEDs reported to date) by suppressing the nonradiative recombination. Various patterns, including letters, numbers, and symbols can be successfully visualized on the skin-mounted QLED display. Furthermore, the combination of the ultrathin QLED display with flexible driving circuits and wearable sensors results in a fully integrated QLED display that can directly show sensor data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Business model design for a wearable biofeedback system.

PubMed

Hidefjäll, Patrik; Titkova, Dina

2015-01-01

Wearable sensor technologies used to track daily activities have become successful in the consumer market. In order for wearable sensor technology to offer added value in the more challenging areas of stress-rehab care and occupational health stress-related biofeedback parameters need to be monitored and more elaborate business models are needed. To identify probable success factors for a wearable biofeedback system (Affective Health) in the two mentioned market segments in a Swedish setting, we conducted literature studies and interviews with relevant representatives. Data were collected and used first to describe the two market segments and then to define likely feasible business model designs, according to the Business Model Canvas framework. Needs of stakeholders were identified as inputs to business model design. Value propositions, a key building block of a business model, were defined for each segment. The value proposition for occupational health was defined as "A tool that can both identify employees at risk of stress-related disorders and reinforce healthy sustainable behavior" and for healthcare as: "Providing therapists with objective data about the patient's emotional state and motivating patients to better engage in the treatment process".

Review of Recent Metamaterial Microfluidic Sensors

PubMed Central

Salim, Ahmed

2018-01-01

Metamaterial elements/arrays exhibit a sensitive response to fluids yet with a small footprint, therefore, they have been an attractive choice to realize various sensing devices when integrated with microfluidic technology. Micro-channels made from inexpensive biocompatible materials avoid any contamination from environment and require only microliter–nanoliter sample for sensing. Simple design, easy fabrication process, light weight prototype, and instant measurements are advantages as compared to conventional (optical, electrochemical and biological) sensing systems. Inkjet-printed flexible sensors find their utilization in rapidly growing wearable electronics and health-monitoring flexible devices. Adequate sensitivity and repeatability of these low profile microfluidic sensors make them a potential candidate for point-of-care testing which novice patients can use reliably. Aside from degraded sensitivity and lack of selectivity in all practical microwave chemical sensors, they require an instrument, such as vector network analyzer for measurements and not readily available as a self-sustained portable sensor. This review article presents state-of-the-art metamaterial inspired microfluidic bio/chemical sensors (passive devices ranging from gigahertz to terahertz range) with an emphasis on metamaterial sensing circuit and microfluidic detection. We also highlight challenges and strategies to cope these issues which set future directions. PMID:29342953

Review of Recent Metamaterial Microfluidic Sensors.

PubMed

Salim, Ahmed; Lim, Sungjoon

2018-01-15

Metamaterial elements/arrays exhibit a sensitive response to fluids yet with a small footprint, therefore, they have been an attractive choice to realize various sensing devices when integrated with microfluidic technology. Micro-channels made from inexpensive biocompatible materials avoid any contamination from environment and require only microliter-nanoliter sample for sensing. Simple design, easy fabrication process, light weight prototype, and instant measurements are advantages as compared to conventional (optical, electrochemical and biological) sensing systems. Inkjet-printed flexible sensors find their utilization in rapidly growing wearable electronics and health-monitoring flexible devices. Adequate sensitivity and repeatability of these low profile microfluidic sensors make them a potential candidate for point-of-care testing which novice patients can use reliably. Aside from degraded sensitivity and lack of selectivity in all practical microwave chemical sensors, they require an instrument, such as vector network analyzer for measurements and not readily available as a self-sustained portable sensor. This review article presents state-of-the-art metamaterial inspired microfluidic bio/chemical sensors (passive devices ranging from gigahertz to terahertz range) with an emphasis on metamaterial sensing circuit and microfluidic detection. We also highlight challenges and strategies to cope these issues which set future directions.

An early illness recognition framework using a temporal Smith Waterman algorithm and NLP.

PubMed

Hajihashemi, Zahra; Popescu, Mihail

2013-01-01

In this paper we propose a framework for detecting health patterns based on non-wearable sensor sequence similarity and natural language processing (NLP). In TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor networks together with a nursing electronic health record (EHR) system to provide early illness recognition. The proposed framework utilizes sensor sequence similarity and NLP on EHR nursing comments to automatically notify the physician when health problems are detected. The reported methodology is inspired by genomic sequence annotation using similarity algorithms such as Smith Waterman (SW). Similarly, for each sensor sequence, we associate health concepts extracted from the nursing notes using Metamap, a NLP tool provided by Unified Medical Language System (UMLS). Since sensor sequences, unlike genomics ones, have an associated time dimension we propose a temporal variant of SW (TSW) to account for time. The main challenges presented by our framework are finding the most suitable time sequence similarity and aggregation of the retrieved UMLS concepts. On a pilot dataset from three Tiger Place residents, with a total of 1685 sensor days and 626 nursing records, we obtained an average precision of 0.64 and a recall of 0.37.

Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

PubMed

Zhao, Hang; Bai, Jinbo

2015-05-13

The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

Material approaches to stretchable strain sensors.

PubMed

Park, Jaeyoon; You, Insang; Shin, Sangbaie; Jeong, Unyong

2015-04-27

With the recent progress made in wearable electronics, devices now require high flexibility and stretchability up to large strain levels (typically larger than 30 % strain). Wearable strain sensors or deformable strain sensors have been gaining increasing research interest because of the rapid development of electronic skins and robotics and because of their biomedical applications. Conventional brittle strain sensors made of metals and piezoresistors are not applicable for such stretchable sensors. This Review summarizes recent advances in stretchable sensors and focuses on material aspects for high stretchability and sensitivity. It begins with a brief introduction to the Wheatstone bridge circuit of conventional resistive strain sensors. Then, studies on the manipulation of materials are reviewed, including waved structural approaches for making metals and semiconductors stretchable, the use of liquid metals, and conductive filler/elastomer composites by using percolation among the fillers. For capacitive strain sensors, the constant conductivity of the electrode is a key factor in obtaining reliable sensors. Possible approaches to developing capacitive strain sensors are presented. This Review concludes with a discussion on the major challenges and perspectives related to stretchable strain sensors. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Compression in wearable sensor nodes: impacts of node topology.

PubMed

Imtiaz, Syed Anas; Casson, Alexander J; Rodriguez-Villegas, Esther

2014-04-01

Wearable sensor nodes monitoring the human body must operate autonomously for very long periods of time. Online and low-power data compression embedded within the sensor node is therefore essential to minimize data storage/transmission overheads. This paper presents a low-power MSP430 compressive sensing implementation for providing such compression, focusing particularly on the impact of the sensor node architecture on the compression performance. Compression power performance is compared for four different sensor nodes incorporating different strategies for wireless transmission/on-sensor-node local storage of data. The results demonstrate that the compressive sensing used must be designed differently depending on the underlying node topology, and that the compression strategy should not be guided only by signal processing considerations. We also provide a practical overview of state-of-the-art sensor node topologies. Wireless transmission of data is often preferred as it offers increased flexibility during use, but in general at the cost of increased power consumption. We demonstrate that wireless sensor nodes can highly benefit from the use of compressive sensing and now can achieve power consumptions comparable to, or better than, the use of local memory.

A Triple-Mode Flexible E-Skin Sensor Interface for Multi-Purpose Wearable Applications

PubMed Central

Kim, Sung-Woo; Lee, Youngoh; Park, Jonghwa; Kim, Seungmok; Chae, Heeyoung; Ko, Hyunhyub

2017-01-01

This study presents a flexible wireless electronic skin (e-skin) sensor system that includes a multi-functional sensor device, a triple-mode reconfigurable readout integrated circuit (ROIC), and a mobile monitoring interface. The e-skin device’s multi-functionality is achieved by an interlocked micro-dome array structure that uses a polyvinylidene fluoride and reduced graphene oxide (PVDF/RGO) composite material that is inspired by the structure and functions of the human fingertip. For multi-functional implementation, the proposed triple-mode ROIC is reconfigured to support piezoelectric, piezoresistance, and pyroelectric interfaces through single-type e-skin sensor devices. A flexible system prototype was developed and experimentally verified to provide various wireless wearable sensing functions—including pulse wave, voice, chewing/swallowing, breathing, knee movements, and temperature—while their real-time sensed data are displayed on a smartphone. PMID:29286312

Wearable Sensors in Huntington Disease: A Pilot Study.

PubMed

Andrzejewski, Kelly L; Dowling, Ariel V; Stamler, David; Felong, Timothy J; Harris, Denzil A; Wong, Cynthia; Cai, Hang; Reilmann, Ralf; Little, Max A; Gwin, Joseph T; Biglan, Kevin M; Dorsey, E Ray

2016-06-18

The Unified Huntington's Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments. To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home. Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A second chest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen's d values. Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (time between consecutive steps) was increased in Huntington disease (p < 0.0001; Cohen's d = 2.61) compared to controls. At home with additional observations, significant differences were observed in seven additional gait measures. The gait of individuals with higher total motor scores (50 or more) differed significantly from those with lower total motor scores (below 50) on multiple measures at home. In this pilot study, the use of wearable sensors in clinic and at home was feasible and demonstrated gait differences between controls, participants with Huntington disease, and participants with Huntington disease grouped by motor impairment.

Assessment of Lower Limb Prosthesis through Wearable Sensors and Thermography

PubMed Central

Cutti, Andrea Giovanni; Perego, Paolo; Fusca, Marcello C.; Sacchetti, Rinaldo; Andreoni, Giuseppe

2014-01-01

This study aimed to explore the application of infrared thermography in combination with ambulatory wearable monitoring of temperature and relative humidity, to assess the residual limb-to-liner interface in lower-limb prosthesis users. Five male traumatic transtibial amputees were involved, who reported no problems or discomfort while wearing the prosthesis. A thermal imaging camera was used to measure superficial thermal distribution maps of the stump. A wearable system for recording the temperature and relative humidity in up to four anatomical points was developed, tested in vitro and integrated with the measurement set. The parallel application of an infrared camera and wearable sensors provided complementary information. Four main Regions of Interest were identified on the stump (inferior patella, lateral/medial epicondyles, tibial tuberosity), with good inter-subject repeatability. An average increase of 20% in hot areas (P < 0.05) is shown after walking compared to resting conditions. The sensors inside the cuff did not provoke any discomfort during recordings and provide an inside of the thermal exchanges while walking and recording the temperature increase (a regime value is ∼+1.1 ± 0.7 °C) and a more significant one (∼+4.1 ± 2.3%) in humidity because of the sweat produced. This study has also begun the development of a reference data set for optimal socket/liner-stump construction. PMID:24618782

Stretchable, Twisted Conductive Microtubules for Wearable Computing, Robotics, Electronics, and Healthcare.

PubMed

Do, Thanh Nho; Visell, Yon

2017-05-11

Stretchable and flexible multifunctional electronic components, including sensors and actuators, have received increasing attention in robotics, electronics, wearable, and healthcare applications. Despite advances, it has remained challenging to design analogs of many electronic components to be highly stretchable, to be efficient to fabricate, and to provide control over electronic performance. Here, we describe highly elastic sensors and interconnects formed from thin, twisted conductive microtubules. These devices consist of twisted assemblies of thin, highly stretchable (>400%) elastomer tubules filled with liquid conductor (eutectic gallium indium, EGaIn), and fabricated using a simple roller coating process. As we demonstrate, these devices can operate as multimodal sensors for strain, rotation, contact force, or contact location. We also show that, through twisting, it is possible to control their mechanical performance and electronic sensitivity. In extensive experiments, we have evaluated the capabilities of these devices, and have prototyped an array of applications in several domains of stretchable and wearable electronics. These devices provide a novel, low cost solution for high performance stretchable electronics with broad applications in industry, healthcare, and consumer electronics, to emerging product categories of high potential economic and societal significance.

Bioimpedance-Based Wearable Measurement Instrumentation for Studying the Autonomic Nerve System Response to Stressful Working Conditions

NASA Astrophysics Data System (ADS)

Ferreira, J.; Álvarez, L.; Buendía, R.; Ayllón, D.; Llerena, C.; Gil-Pita, R.; Seoane, F.

2013-04-01

The assessment of mental stress on workers under hard and stressful conditions is critical to identify which workers are not ready to undertake a mission that might put in risk their own life and the life of others. The ATREC project aims to enable Real Time Assessment of Mental Stress of the Spanish Armed Forces during military activities. Integrating sensors with garments and using wearable measurement devices, the following physiological measurements were recorded: heart and respiration rate, skin galvanic response as well as peripheral temperature. The measuring garments are the following: a sensorized glove, an upper-arm strap and a repositionable textrode chest strap system with 6 textrodes. The implemented textile-enabled instrumentation contains: one skin galvanometer, two temperature sensors, for skin and environmental, and an Impedance Cardiographer/Pneumographer containing a 1 channel ECG amplifier to record cardiogenic biopotentials. The implemented wearable systems operated accordingly to the specifications and are ready to be used for the mental stress experiments that will be executed in the coming phases of the project in healthy volunteers.

A Novel Method for Fabricating Wearable, Piezoresistive, and Pressure Sensors Based on Modified-Graphite/Polyurethane Composite Films

PubMed Central

He, Yin; Li, Wei; Yang, Guilin; Liu, Hao; Lu, Junyu; Zheng, Tongtong; Li, Xiaojiu

2017-01-01

A wearable, low-cost, highly repeatable piezoresistive sensor was fabricated by the synthesis of modified-graphite and polyurethane (PU) composites and polydimethylsiloxane (PDMS). Graphite sheets functionalized by using a silane coupling agent (KH550) were distributed in PU/N,N-dimethylformamide (DMF) solution, which were then molded to modified-graphite/PU (MG/PU) composite films. Experimental results show that with increasing modified-graphite content, the tensile strength of the MG/PU films first increased and then decreased, and the elongation at break of the composite films showed a decreasing trend. The electrical conductivity of the composite films can be influenced by filler modification and concentration, and the percolation threshold of MG/PU was 28.03 wt %. Under liner uniaxial compression, the 30 wt % MG/PU composite films exhibited 0.274 kPa−1 piezoresistive sensitivity within the range of low pressure, and possessed better stability and hysteresis. The flexible MG/PU composite piezoresistive sensors have great potential for body motion, wearable devices for human healthcare, and garment pressure testing. PMID:28773047

A Shoe-Embedded Piezoelectric Energy Harvester for Wearable Sensors

PubMed Central

Zhao, Jingjing; You, Zheng

2014-01-01

Harvesting mechanical energy from human motion is an attractive approach for obtaining clean and sustainable electric energy to power wearable sensors, which are widely used for health monitoring, activity recognition, gait analysis and so on. This paper studies a piezoelectric energy harvester for the parasitic mechanical energy in shoes originated from human motion. The harvester is based on a specially designed sandwich structure with a thin thickness, which makes it readily compatible with a shoe. Besides, consideration is given to both high performance and excellent durability. The harvester provides an average output power of 1 mW during a walk at a frequency of roughly 1 Hz. Furthermore, a direct current (DC) power supply is built through integrating the harvester with a power management circuit. The DC power supply is tested by driving a simulated wireless transmitter, which can be activated once every 2–3 steps with an active period lasting 5 ms and a mean power of 50 mW. This work demonstrates the feasibility of applying piezoelectric energy harvesters to power wearable sensors. PMID:25019634

Two-Dimensional Atomic-Layered Alloy Junctions for High-Performance Wearable Chemical Sensor.

PubMed

Cho, Byungjin; Kim, Ah Ra; Kim, Dong Jae; Chung, Hee-Suk; Choi, Sun Young; Kwon, Jung-Dae; Park, Sang Won; Kim, Yonghun; Lee, Byoung Hun; Lee, Kyu Hwan; Kim, Dong-Ho; Nam, Jaewook; Hahm, Myung Gwan

2016-08-03

We first report that two-dimensional (2D) metal (NbSe2)-semiconductor (WSe2)-based flexible, wearable, and launderable gas sensors can be prepared through simple one-step chemical vapor deposition of prepatterned WO3 and Nb2O5. Compared to a control device with a Au/WSe2 junction, gas-sensing performance of the 2D NbSe2/WSe2 device was significantly enhanced, which might have resulted from the formation of a NbxW1-xSe2 transition alloy junction lowering the Schottky barrier height. This would make it easier to collect charges of channels induced by molecule adsorption, improving gas response characteristics toward chemical species including NO2 and NH3. 2D NbSe2/WSe2 devices on a flexible substrate provide gas-sensing properties with excellent durability under harsh bending. Furthermore, the device stitched on a T-shirt still performed well even after conventional cleaning with a laundry machine, enabling wearable and launderable chemical sensors. These results could pave a road toward futuristic gas-sensing platforms based on only 2D materials.

Nanoalloy Printed and Pulse-Laser Sintered Flexible Sensor Devices with Enhanced Stability and Materials Compatibility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Wei; Rovore, Thomas; Weerawarne, Darshana

2015-06-02

While conformal and wearable devices have become one of the most desired formats for printable electronics, it is challenging to establish a scalable process that produces stable conductive patterns but also uses substrates compatible with widely available wearable materials. Here, we describe findings of an investigation of a nanoalloy ink printed and pulsed laser sintered conductive patterns as flexible functional devices with enhanced stability and materials compatibility. While nanoparticle inks are desired for printable electronics, almost all existing nanoparticle inks are based on single-metal component, which, as an electronic element, is limited by its inherent stabilities of the metal suchmore » as propensity of metal oxidation and mobility of metal ions, especially in sintering processes. The work here has demonstrated the first example in exploiting plasmonic coupling of nanoalloys and pulsed-laser energy with controllable thermal penetration. The experimental and theoretical results have revealed clear correlation between the pulsed laser parameters and the nanoalloy structural characteristics. The superior performance of the resulting flexible sensor device, upon imparting nanostructured sensing materials, for detecting volatile organic compounds has significant implications to developing stable and wearable sensors for monitoring environmental pollutants and breath biomarkers. This simple “nanoalloy printing 'laser sintering' nanostructure printing” process is entirely general to many different sensor devices and nanostructured sensing materials, enabling the ability to easily construct sophisticated sensor array.« less

Nanoalloy Printed and Pulse-Laser Sintered Flexible Sensor Devices with Enhanced Stability and Materials Compatibility.

PubMed

Zhao, Wei; Rovere, Thomas; Weerawarne, Darshana; Osterhoudt, Gavin; Kang, Ning; Joseph, Pharrah; Luo, Jin; Shim, Bonggu; Poliks, Mark; Zhong, Chuan-Jian

2015-06-23

While conformal and wearable devices have become one of the most desired formats for printable electronics, it is challenging to establish a scalable process that produces stable conductive patterns but also uses substrates compatible with widely available wearable materials. Here, we describe findings of an investigation of a nanoalloy ink printed and pulsed-laser sintered conductive patterns as flexible functional devices with enhanced stability and materials compatibility. While nanoparticle inks are desired for printable electronics, almost all existing nanoparticle inks are based on single-metal component, which, as an electronic element, is limited by its inherent stabilities of the metal such as propensity of metal oxidation and mobility of metal ions, especially in sintering processes. The work here has demonstrated the first example in exploiting plasmonic coupling of nanoalloys and pulsed-laser energy with controllable thermal penetration. The experimental and theoretical results have revealed clear correlation between the pulsed laser parameters and the nanoalloy structural characteristics. The superior performance of the resulting flexible sensor device, upon imparting nanostructured sensing materials, for detecting volatile organic compounds has significant implications to developing stable and wearable sensors for monitoring environmental pollutants and breath biomarkers. This simple "nanoalloy printing-laser sintering-nanostructure printing" process is entirely general to many different sensor devices and nanostructured sensing materials, enabling the ability to easily construct sophisticated sensor array.

Textile materials for the design of wearable antennas: a survey.

PubMed

Salvado, Rita; Loss, Caroline; Gonçalves, Ricardo; Pinho, Pedro

2012-11-15

In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented.

Textile Materials for the Design of Wearable Antennas: A Survey

PubMed Central

Salvado, Rita; Loss, Caroline; Gonçalves, Ricardo; Pinho, Pedro

2012-01-01

In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented. PMID:23202235

Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications

PubMed Central

Vallejo, Mónica; Recas, Joaquín; del Valle, Pablo García; Ayala, José L.

2013-01-01

The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead. PMID:23752565

Localization and Tracking of Implantable Biomedical Sensors

PubMed Central

Umay, Ilknur; Fidan, Barış; Barshan, Billur

2017-01-01

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems. PMID:28335384

Highly stretchable strain sensor based on polyurethane substrate using hydrogen bond-assisted laminated structure for monitoring of tiny human motions

NASA Astrophysics Data System (ADS)

Huang, Ying; Zhao, Yunong; Wang, Yang; Guo, Xiaohui; Zhang, Yangyang; Liu, Ping; Liu, Caixia; Zhang, Yugang

2018-03-01

Strain sensors used as flexible and wearable electronic devices have improved prospects in the fields of artificial skin, robotics, human-machine interfaces, and healthcare. This work introduces a highly stretchable fiber-based strain sensor with a laminated structure made up of a graphene nanoplatelet layer and a carbon black/single-walled carbon nanotube synergetic conductive network layer. An ultrathin, flexible, and elastic two-layer polyurethane (PU) yarn substrate was successively deposited by a novel chemical bonding-based layered dip-coating process. These strain sensors demonstrated high stretchability (˜350%), little hysteresis, and long-term durability (over 2400 cycles) due to the favorable tensile properties of the PU substrate. The linearity of the strain sensor could reach an adjusted R-squared of 0.990 at 100% strain, which is better than most of the recently reported strain sensors. Meanwhile, the strain sensor exhibited good sensibility, rapid response, and a lower detection limit. The lower detection limit benefited from the hydrogen bond-assisted laminated structure and continuous conductive path. Finally, a series of experiments were carried out based on the special features of the PU strain sensor to show its capacity of detecting and monitoring tiny human motions.

Low Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic Respiratory Disease

PubMed Central

Dieffenderfer, James; Goodell, Henry; Mills, Steven; McKnight, Michael; Yao, Shanshan; Lin, Feiyan; Beppler, Eric; Bent, Brinnae; Lee, Bongmook; Misra, Veena; Zhu, Yong; Oralkan, Omer; Strohmaier, Jason; Muth, John; Peden, David; Bozkurt, Alper

2016-01-01

We present our efforts towards enabling a wearable sensor system that allows for the correlation of individual environmental exposures to physiologic and subsequent adverse health responses. This system will permit a better understanding of the impact of increased ozone levels and other pollutants on chronic asthma conditions. We discuss the inefficiency of existing commercial off-the-shelf components to achieve continuous monitoring and our system-level and nano-enabled efforts towards improving the wearability and power consumption. Our system consists of a wristband, a chest patch, and a handheld spirometer. We describe our preliminary efforts to achieve a sub-milliwatt system ultimately powered by the energy harvested from thermal radiation and motion of the body with the primary contributions being an ultra-low power ozone sensor, an volatile organic compounds sensor, spirometer, and the integration of these and other sensors in a multimodal sensing platform. The measured environmental parameters include ambient ozone concentration, temperature, and relative humidity. Our array of sensors also assesses heart rate via photoplethysmography and electrocardiography, respiratory rate via photoplethysmography, skin impedance, three-axis acceleration, wheezing via a microphone, and expiratory airflow. The sensors on the wristband, chest patch, and spirometer consume 0.83, 0.96, and 0.01 milliwatts respectively. The data from each sensor is continually streamed to a peripheral data aggregation device and is subsequently transferred to a dedicated server for cloud storage. Future work includes reducing the power consumption of the system-on-chip including radio to reduce the entirety of each described system in the sub-milliwatt range. PMID:27249840

Low-Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic Respiratory Disease.

PubMed

Dieffenderfer, James; Goodell, Henry; Mills, Steven; McKnight, Michael; Yao, Shanshan; Lin, Feiyan; Beppler, Eric; Bent, Brinnae; Lee, Bongmook; Misra, Veena; Zhu, Yong; Oralkan, Omer; Strohmaier, Jason; Muth, John; Peden, David; Bozkurt, Alper

2016-09-01

We present our efforts toward enabling a wearable sensor system that allows for the correlation of individual environmental exposures with physiologic and subsequent adverse health responses. This system will permit a better understanding of the impact of increased ozone levels and other pollutants on chronic asthma conditions. We discuss the inefficiency of existing commercial off-the-shelf components to achieve continuous monitoring and our system-level and nano-enabled efforts toward improving the wearability and power consumption. Our system consists of a wristband, a chest patch, and a handheld spirometer. We describe our preliminary efforts to achieve a submilliwatt system ultimately powered by the energy harvested from thermal radiation and motion of the body with the primary contributions being an ultralow-power ozone sensor, an volatile organic compounds sensor, spirometer, and the integration of these and other sensors in a multimodal sensing platform. The measured environmental parameters include ambient ozone concentration, temperature, and relative humidity. Our array of sensors also assesses heart rate via photoplethysmography and electrocardiography, respiratory rate via photoplethysmography, skin impedance, three-axis acceleration, wheezing via a microphone, and expiratory airflow. The sensors on the wristband, chest patch, and spirometer consume 0.83, 0.96, and 0.01 mW, respectively. The data from each sensor are continually streamed to a peripheral data aggregation device and are subsequently transferred to a dedicated server for cloud storage. Future work includes reducing the power consumption of the system-on-chip including radio to reduce the entirety of each described system in the submilliwatt range.

Influence of the UV radiation on the screen-printed pH-sensitive layers based on graphene and ruthenium dioxide

NASA Astrophysics Data System (ADS)

Pepłowski, A.; Grudziński, D.; Raczyński, T.; Wróblewski, G.; Janczak, D.; Jakubowska, M.

2017-08-01

Electrodes for measuring pH of the solution were fabricated by the means of screen-printing technology. Potentiometric sensors' layers comprised of composite with polymer matrix and graphene nanoplatelets/ruthenium (IV) oxide nanopowder as functional phase. Transceivers were printed on the elastic PMMA foil. Regarding potential application of the sensors in the wearable devices, dynamic response of the electrodes to changing ultraviolet radiation levels was assessed, since RuO2 is reported to be UV-sensitive. Observed changes of the electrodes' potential were of sub-millivolt magnitude, being comparable to simultaneously observed signal drift. Given this stability under varying UV conditions and previously verified good flexibility, fabricated sensors meet the requirements for wearable applications.

OLED microdisplays in near-to-eye applications: challenges and solutions

NASA Astrophysics Data System (ADS)

Vogel, Uwe; Richter, Bernd; Wartenberg, Philipp; König, Peter; Hild, Olaf R.; Fehse, Karsten; Schober, Matthias; Bodenstein, Elisabeth; Beyer, Beatrice

2017-06-01

Wearable augmented-reality (AR) has already started to be used productively mainly in manufacturing industry and logistics. Next step will be to move wearable AR from "professionals to citizens" by enabling networked, everywhere augmented-reality (in-/outdoor localisation, scene recognition, cloud access,…) which is non-intrusive, exhibits intuitive user-interaction, anytime safe and secure use, and considers personal privacy protection (user's and others). Various hardware improvements (e.g., low-power, seamless interactivity, small form factor, ergonomics,…), as well as connectivity and network integration will become vital for consumer adoption. Smart-Glasses (i.e., near-to-eye (NTE) displays) have evolved as major devices for wearable AR, that hold potential to become adopted by consumers soon. Tiny microdisplays are a key component of smart-glasses, e.g., creating images from organic light emitting diodes (OLED), that have become popular in mobile phone displays. All microdisplay technologies on the market comprise an image-creating pixel modulation, but only the emissive ones (for example, OLED and LED) feature the image and light source in a single device, and therefore do not require an external light source. This minimizes system size and power consumption, while providing exceptional contrast and color space. These advantages make OLED microdisplays a perfect fit for near-eye applications. Low-power active-matrix circuitry CMOS backplane architecture, embedded sensors, emission spectra outside the visible and high-resolution sub-pixel micro-patterning address some of the application challenges (e.g., long battery life, sun-light readability, user interaction modes) and enable advanced features for OLED microdisplays in near-to-eye displays, e.g., upcoming connected augmented-reality smart glasses. This report is to analyze the challenges in addressing those features and discuss solutions.

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

PubMed Central

Haugen Mikalsen, Martin; Woldaregay, Ashenafi Zebene; Muzny, Miroslav; Hartvigsen, Gunnar; Hopstock, Laila Arnesdatter; Grimsgaard, Sameline

2018-01-01

Background New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated. PMID:29567635

Sensing textile seam-line for wearable multimodal physiological monitoring.

PubMed

McKnight, M; Agcayazi, T; Kausche, H; Ghosh, T; Bozkurt, A

2016-08-01

This paper investigates a novel multimodal sensing method by forming seam-lines of conductive textile fibers into commercially available fabrics. The proposed ultra-low cost micro-electro-mechanical sensor would provide, wearable, flexible, textile based biopotential signal recording, wetness detection and tactile sensing simultaneously. Three types of fibers are evaluated for their array-based sensing capability, including a 3D printed conductive fiber, a multiwall carbon nanotube based fiber, and a commercially available stainless steel conductive thread. The sensors were shown to have a correlation between capacitance and pressure; impedance and wetness; and recorded potential and ECG waveforms.

Implementation study of wearable sensors for activity recognition systems.

PubMed

Rezaie, Hamed; Ghassemian, Mona

2015-08-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely 'stream-based', 'feature-based' and 'threshold-based' scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

A wearable device for emotional recognition using facial expression and physiological response.

PubMed

Jangho Kwon; Da-Hye Kim; Wanjoo Park; Laehyun Kim

2016-08-01

This paper introduces a glasses-typed wearable system to detect user's emotions using facial expression and physiological responses. The system is designed to acquire facial expression through a built-in camera and physiological responses such as photoplethysmogram (PPG) and electrodermal activity (EDA) in unobtrusive way. We used video clips for induced emotions to test the system suitability in the experiment. The results showed a few meaningful properties that associate emotions with facial expressions and physiological responses captured by the developed wearable device. We expect that this wearable system with a built-in camera and physiological sensors may be a good solution to monitor user's emotional state in daily life.

A Framework for Learning Analytics Using Commodity Wearable Devices

PubMed Central

Lu, Yu; Zhang, Sen; Zhang, Zhiqiang; Xiao, Wendong; Yu, Shengquan

2017-01-01

We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55). PMID:28613236

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection.

PubMed

Ergeneci, Mert; Gokcesu, Kaan; Ertan, Erhan; Kosmas, Panagiotis

2018-02-01

Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with accuracy in the detection of the contractions.

A Framework for Learning Analytics Using Commodity Wearable Devices.

PubMed

Lu, Yu; Zhang, Sen; Zhang, Zhiqiang; Xiao, Wendong; Yu, Shengquan

2017-06-14

We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner's physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55).

Multifunctional wearable devices for diagnosis and therapy of movement disorders.

PubMed

Son, Donghee; Lee, Jongha; Qiao, Shutao; Ghaffari, Roozbeh; Kim, Jaemin; Lee, Ji Eun; Song, Changyeong; Kim, Seok Joo; Lee, Dong Jun; Jun, Samuel Woojoo; Yang, Shixuan; Park, Minjoon; Shin, Jiho; Do, Kyungsik; Lee, Mincheol; Kang, Kwanghun; Hwang, Cheol Seong; Lu, Nanshu; Hyeon, Taeghwan; Kim, Dae-Hyeong

2014-05-01

Wearable systems that monitor muscle activity, store data and deliver feedback therapy are the next frontier in personalized medicine and healthcare. However, technical challenges, such as the fabrication of high-performance, energy-efficient sensors and memory modules that are in intimate mechanical contact with soft tissues, in conjunction with controlled delivery of therapeutic agents, limit the wide-scale adoption of such systems. Here, we describe materials, mechanics and designs for multifunctional, wearable-on-the-skin systems that address these challenges via monolithic integration of nanomembranes fabricated with a top-down approach, nanoparticles assembled by bottom-up methods, and stretchable electronics on a tissue-like polymeric substrate. Representative examples of such systems include physiological sensors, non-volatile memory and drug-release actuators. Quantitative analyses of the electronics, mechanics, heat-transfer and drug-diffusion characteristics validate the operation of individual components, thereby enabling system-level multifunctionalities.

Wearable carbon nanotube based dry-electrodes for electrophysiological sensors

NASA Astrophysics Data System (ADS)

Kang, Byeong-Cheol; Ha, Tae-Jun

2018-05-01

In this paper, we demonstrate all-solution-processed carbon nanotube (CNT) dry-electrodes for the detection of electrophysiological signals such as electrocardiograms (ECG) and electromyograms (EMG). The key parameters of P, Q, R, S, and T peaks are successfully extracted by such CNT based dry-electrodes, which is comparable with conventional silver/chloride (Ag/AgCl) wet-electrodes with a conducting gel film for the ECG recording. Furthermore, the sensing performance of CNT based dry-electrodes is secured during the bending test of 200 cycles, which is essential for wearable electrophysiological sensors in a non-invasive method on human skin. We also investigate the application of wearable CNT based dry-electrodes directly attached to the human skins such as forearm for sensing the electrophysiological signals. The accurate and rapid sensing response can be achieved by CNT based dry-electrodes to supervise the health condition affected by excessive physical movements during the real-time measurements.

Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.

PubMed

Natarajan, Annamalai; Angarita, Gustavo; Gaiser, Edward; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin M

2016-09-01

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

Personalized biomedical devices & systems for healthcare applications

NASA Astrophysics Data System (ADS)

Chen, I.-Ming; Phee, Soo Jay; Luo, Zhiqiang; Lim, Chee Kian

2011-03-01

With the advancement in micro- and nanotechnology, electromechanical components and systems are getting smaller and smaller and gradually can be applied to the human as portable, mobile and even wearable devices. Healthcare industry have started to benefit from this technology trend by providing more and more miniature biomedical devices for personalized medical treatments in order to obtain better and more accurate outcome. This article introduces some recent development in non-intrusive and intrusive biomedical devices resulted from the advancement of niche miniature sensors and actuators, namely, wearable biomedical sensors, wearable haptic devices, and ingestible medical capsules. The development of these devices requires carful integration of knowledge and people from many different disciplines like medicine, electronics, mechanics, and design. Furthermore, designing affordable devices and systems to benefit all mankind is a great challenge ahead. The multi-disciplinary nature of the R&D effort in this area provides a new perspective for the future mechanical engineers.

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

PubMed Central

Villalonga, Claudia; Damas, Miguel

2014-01-01

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices. PMID:25295301

PhysioDroid: combining wearable health sensors and mobile devices for a ubiquitous, continuous, and personal monitoring.

PubMed

Banos, Oresti; Villalonga, Claudia; Damas, Miguel; Gloesekoetter, Peter; Pomares, Hector; Rojas, Ignacio

2014-01-01

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

Wearable motion sensors to continuously measure real-world physical activities.

PubMed

Dobkin, Bruce H

2013-12-01

Rehabilitation for sensorimotor impairments aims to improve daily activities, walking, exercise, and motor skills. Monitoring of practice and measuring outcomes, however, is usually restricted to laboratory-based procedures and self-reports. Mobile health devices may reverse these confounders of daily care and research trials. Wearable, wireless motion sensor data, analyzed by activity pattern-recognition algorithms, can describe the type, quantity, and quality of mobility-related activities in the community. Data transmission from the sensors to a cell phone and the Internet enable continuous monitoring. Remote access to laboratory quality data about walking speed, duration and distance, gait asymmetry and smoothness of movements, as well as cycling, exercise, and skills practice, opens new opportunities to engage patients in progressive, personalized therapies with feedback about the performance. Clinical trial designs will be able to include remote verification of the integrity of complex physical interventions and compliance with practice, as well as capture repeated, ecologically sound, ratio scale outcome measures. Given the progressively falling cost of miniaturized wearable gyroscopes, accelerometers, and other physiologic sensors, as well as inexpensive data transmission, sensing systems may become as ubiquitous as cell phones for healthcare. Neurorehabilitation can develop these mobile health platforms for daily care and clinical trials to improve exercise and fitness, skills learning, and physical functioning.

Sensorized Garments and Textrode-Enabled Measurement Instrumentation for Ambulatory Assessment of the Autonomic Nervous System Response in the ATREC Project

PubMed Central

Seoane, Fernando; Ferreira, Javier; Alvarez, Lorena; Buendia, Ruben; Ayllón, David; Llerena, Cosme; Gil-Pita, Roberto

2013-01-01

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers. PMID:23857264

Sensorized garments and textrode-enabled measurement instrumentation for ambulatory assessment of the autonomic nervous system response in the ATREC project.

PubMed

Seoane, Fernando; Ferreira, Javier; Alvarez, Lorena; Buendia, Ruben; Ayllón, David; Llerena, Cosme; Gil-Pita, Roberto

2013-07-12

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.

Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults.

PubMed

Schwenk, Michael; Grewal, Gurtej S; Honarvar, Bahareh; Schwenk, Stefanie; Mohler, Jane; Khalsa, Dharma S; Najafi, Bijan

2014-12-13

Wearable sensor technology can accurately measure body motion and provide incentive feedback during exercising. The aim of this pilot study was to evaluate the effectiveness and user experience of a balance training program in older adults integrating data from wearable sensors into a human-computer interface designed for interactive training. Senior living community residents (mean age 84.6) with confirmed fall risk were randomized to an intervention (IG, n = 17) or control group (CG, n = 16). The IG underwent 4 weeks (twice a week) of balance training including weight shifting and virtual obstacle crossing tasks with visual/auditory real-time joint movement feedback using wearable sensors. The CG received no intervention. Outcome measures included changes in center of mass (CoM) sway, ankle and hip joint sway measured during eyes open (EO) and eyes closed (EC) balance test at baseline and post-intervention. Ankle-hip postural coordination was quantified by a reciprocal compensatory index (RCI). Physical performance was quantified by the Alternate-Step-Test (AST), Timed-up-and-go (TUG), and gait assessment. User experience was measured by a standardized questionnaire. After the intervention sway of CoM, hip, and ankle were reduced in the IG compared to the CG during both EO and EC condition (p = .007-.042). Improvement was obtained for AST (p = .037), TUG (p = .024), fast gait speed (p = . 010), but not normal gait speed (p = .264). Effect sizes were moderate for all outcomes. RCI did not change significantly. Users expressed a positive training experience including fun, safety, and helpfulness of sensor-feedback. Results of this proof-of-concept study suggest that older adults at risk of falling can benefit from the balance training program. Study findings may help to inform future exercise interventions integrating wearable sensors for guided game-based training in home- and community environments. Future studies should evaluate the added value of the proposed sensor-based training paradigm compared to traditional balance training programs and commercial exergames. http://www.clinicaltrials.govNCT02043834.

Energy Harvesting Based Body Area Networks for Smart Health.

PubMed

Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

2017-07-10

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

Energy Harvesting Based Body Area Networks for Smart Health

PubMed Central

Hao, Yixue; Peng, Limei; Alamri, Atif

2017-01-01

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive. PMID:28698501

Sleep Quality Prediction From Wearable Data Using Deep Learning.

PubMed

Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-11-04

The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. ©Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.11.2016.

Sleep Quality Prediction From Wearable Data Using Deep Learning

PubMed Central

Sathyanarayana, Aarti; Joty, Shafiq; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-01-01

Background The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463). Conclusions Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. PMID:27815231

Development of Fabric-Based Chemical Gas Sensors for Use as Wearable Electronic Noses

PubMed Central

Seesaard, Thara; Lorwongtragool, Panida; Kerdcharoen, Teerakiat

2015-01-01

Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP)/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons. PMID:25602265

Development of fabric-based chemical gas sensors for use as wearable electronic noses.

PubMed

Seesaard, Thara; Lorwongtragool, Panida; Kerdcharoen, Teerakiat

2015-01-16

Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP)/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

PubMed

Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

2017-05-27

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

PubMed Central

Xi, Xugang; Tang, Minyan; Miran, Seyed M.; Luo, Zhizeng

2017-01-01

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection. PMID:28555016

Hollow-Structured Graphene-Silicone-Composite-Based Piezoresistive Sensors: Decoupled Property Tuning and Bending Reliability.

PubMed

Luo, Ningqi; Huang, Yan; Liu, Jing; Chen, Shih-Chi; Wong, Ching Ping; Zhao, Ni

2017-10-01

A versatile flexible piezoresistive sensor should maintain high sensitivity in a wide linear range, and provide a stable and repeatable pressure reading under bending. These properties are often difficult to achieve simultaneously with conventional filler-matrix composite active materials, as tuning of one material component often results in change of multiple sensor properties. Here, a material strategy is developed to realize a 3D graphene-poly(dimethylsiloxane) hollow structure, where the electrical conductivity and mechanical elasticity of the composite can be tuned separately by varying the graphene layer number and the poly(dimethylsiloxane) composition ratio, respectively. As a result, the sensor sensitivity and linear range can be easily improved through a decoupled tuning process, reaching a sensitivity of 15.9 kPa -1 in a 60 kPa linear region, and the sensor also exhibits fast response (1.2 ms rising time) and high stability. Furthermore, by optimizing the density of the graphene percolation network and thickness of the composite, the stability and repeatability of the sensor output under bending are improved, achieving a measurement error below 6% under bending radius variations from -25 to +25 mm. Finally, the potential applications of these sensors in wearable medical devices and robotic vision are explored. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics

PubMed Central

Kim, Joohee; Kim, Minji; Lee, Mi-Sun; Kim, Kukjoo; Ji, Sangyoon; Kim, Yun-Tae; Park, Jihun; Na, Kyungmin; Bae, Kwi-Hyun; Kyun Kim, Hong; Bien, Franklin; Young Lee, Chang; Park, Jang-Ung

2017-01-01

Wearable contact lenses which can monitor physiological parameters have attracted substantial interests due to the capability of direct detection of biomarkers contained in body fluids. However, previously reported contact lens sensors can only monitor a single analyte at a time. Furthermore, such ocular contact lenses generally obstruct the field of vision of the subject. Here, we developed a multifunctional contact lens sensor that alleviates some of these limitations since it was developed on an actual ocular contact lens. It was also designed to monitor glucose within tears, as well as intraocular pressure using the resistance and capacitance of the electronic device. Furthermore, in-vivo and in-vitro tests using a live rabbit and bovine eyeball demonstrated its reliable operation. Our developed contact lens sensor can measure the glucose level in tear fluid and intraocular pressure simultaneously but yet independently based on different electrical responses. PMID:28447604

Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics.

PubMed

Kim, Joohee; Kim, Minji; Lee, Mi-Sun; Kim, Kukjoo; Ji, Sangyoon; Kim, Yun-Tae; Park, Jihun; Na, Kyungmin; Bae, Kwi-Hyun; Kyun Kim, Hong; Bien, Franklin; Young Lee, Chang; Park, Jang-Ung

2017-04-27

Wearable contact lenses which can monitor physiological parameters have attracted substantial interests due to the capability of direct detection of biomarkers contained in body fluids. However, previously reported contact lens sensors can only monitor a single analyte at a time. Furthermore, such ocular contact lenses generally obstruct the field of vision of the subject. Here, we developed a multifunctional contact lens sensor that alleviates some of these limitations since it was developed on an actual ocular contact lens. It was also designed to monitor glucose within tears, as well as intraocular pressure using the resistance and capacitance of the electronic device. Furthermore, in-vivo and in-vitro tests using a live rabbit and bovine eyeball demonstrated its reliable operation. Our developed contact lens sensor can measure the glucose level in tear fluid and intraocular pressure simultaneously but yet independently based on different electrical responses.

Roll-to-Roll Gravure Printed Electrochemical Sensors for Wearable and Medical Devices.

PubMed

Bariya, Mallika; Shahpar, Ziba; Park, Hyejin; Sun, Junfeng; Jung, Younsu; Gao, Wei; Nyein, Hnin Yin Yin; Liaw, Tiffany Sun; Tai, Li-Chia; Ngo, Quynh P; Chao, Minghan; Zhao, Yingbo; Hettick, Mark; Cho, Gyoujin; Javey, Ali

2018-06-25

As recent developments in noninvasive biosensors spearhead the thrust toward personalized health and fitness monitoring, there is a need for high throughput, cost-effective fabrication of flexible sensing components. Toward this goal, we present roll-to-roll (R2R) gravure printed electrodes that are robust under a range of electrochemical sensing applications. We use inks and electrode morphologies designed for electrochemical and mechanical stability, achieving devices with uniform redox kinetics printed on 150 m flexible substrate rolls. We show that these electrodes can be functionalized into consistently high performing sensors for detecting ions, metabolites, heavy metals, and other small molecules in noninvasively accessed biofluids, including sensors for real-time, in situ perspiration monitoring during exercise. This development of robust and versatile R2R gravure printed electrodes represents a key translational step in enabling large-scale, low-cost fabrication of disposable wearable sensors for personalized health monitoring applications.

Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics

NASA Astrophysics Data System (ADS)

Kim, Joohee; Kim, Minji; Lee, Mi-Sun; Kim, Kukjoo; Ji, Sangyoon; Kim, Yun-Tae; Park, Jihun; Na, Kyungmin; Bae, Kwi-Hyun; Kyun Kim, Hong; Bien, Franklin; Young Lee, Chang; Park, Jang-Ung

2017-04-01

Wearable contact lenses which can monitor physiological parameters have attracted substantial interests due to the capability of direct detection of biomarkers contained in body fluids. However, previously reported contact lens sensors can only monitor a single analyte at a time. Furthermore, such ocular contact lenses generally obstruct the field of vision of the subject. Here, we developed a multifunctional contact lens sensor that alleviates some of these limitations since it was developed on an actual ocular contact lens. It was also designed to monitor glucose within tears, as well as intraocular pressure using the resistance and capacitance of the electronic device. Furthermore, in-vivo and in-vitro tests using a live rabbit and bovine eyeball demonstrated its reliable operation. Our developed contact lens sensor can measure the glucose level in tear fluid and intraocular pressure simultaneously but yet independently based on different electrical responses.

Recent progress of flexible and wearable strain sensors for human-motion monitoring

NASA Astrophysics Data System (ADS)

Ge, Gang; Huang, Wei; Shao, Jinjun; Dong, Xiaochen

2018-01-01

With the rapid development of human artificial intelligence and the inevitably expanding markets, the past two decades have witnessed an urgent demand for the flexible and wearable devices, especially the flexible strain sensors. Flexible strain sensors, incorporated the merits of stretchability, high sensitivity and skin-mountable, are emerging as an extremely charming domain in virtue of their promising applications in artificial intelligent realms, human-machine systems and health-care devices. In this review, we concentrate on the transduction mechanisms, building blocks of flexible physical sensors, subsequently property optimization in terms of device structures and sensing materials in the direction of practical applications. Perspectives on the existing challenges are also highlighted in the end. Project supported by the NNSF of China (Nos. 61525402, 61604071), the Key University Science Research Project of Jiangsu Province (No. 15KJA430006), and the Natural Science Foundation of Jiangsu Province (No. BK20161012).

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.

PubMed

Tsinganos, Panagiotis; Skodras, Athanassios

2018-02-14

In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.

Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study

PubMed Central

Shida-Tokeshi, Joanne; Lane, Christianne J.; Trujillo-Priego, Ivan A.; Deng, Weiyang; Vanderbilt, Douglas L.; Loeb, Gerald E.; Smith, Beth A.

2018-01-01

Background: Advances in wearable sensor technology now allow us to quantify the number, type and kinematic characteristics of bouts of infant arm movement made across a full day in the natural environment. Our aim here was to determine whether the amount and kinematic characteristics of arm movements made across the day in the natural environment were related to developmental status in infants with typical development as they learned to reach for objects using their arms. Methods: We used wearable sensors to measure arm movement across days and months as infants developed arm reaching skills. In total, 22 infants with typical development participated, aged between 38 and 203 days. Of the participants, 2 infants were measured once and the other 20 infants were measured once per month for 3 to 6 visits. The Bayley Scales of Infant Development was used to measure developmental level. Results: Our main findings were: 1) infant arm movement characteristics as measured by full-day wearable sensor data were related to Bayley motor, cognitive and language scores, indicating a relationship between daily movement characteristics and developmental status; 2) infants who moved more had larger increases in language and cognitive scores across visits; and 3) larger changes in movement characteristics across visits were related to higher motor scores. Conclusions: This was a preliminary, exploratory, small study of the potential importance of infant arm movement characteristics as measured by full-day wearable sensor data. Our results support full-day arm movement activity as an area of interest for future study as a biomarker of neurodevelopmental status and as a target for early intervention. PMID:29708221

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

PubMed Central

Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao

2016-01-01

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm. PMID:26985896

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems.

PubMed

Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao

2016-03-12

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.

All-Organic High-Performance Piezoelectric Nanogenerator with Multilayer Assembled Electrospun Nanofiber Mats for Self-Powered Multifunctional Sensors.

PubMed

Maity, Kuntal; Mandal, Dipankar

2018-05-30

Rapid development of wearable electronics, piezoelectric nanogenerator (PNG), has been paid a special attention because of its sustainable and accessible energy generation. In this context, we present a simple yet highly efficient design strategy to enhance the output performance of an all-organic PNG (OPNG) based on multilayer assembled electrospun poly(vinylidene fluoride) (PVDF) nanofiber (NF) mats where vapor-phase polymerized poly(3,4-ethylenedioxythiophene)-coated PVDF NFs are assembled as electrodes and neat PVDF NFs are utilized as an active component. In addition to the multilayer assembly, electrode compatibility and durability remain a challenging task to mitigate the primary requirements of wearable electronics. A multilayer networked three-dimensional structure integrated with a compatible electrode thereby provides enhanced output voltage and current (e.g., open-circuit voltage, V oc ≈ 48 V, and short-circuit current, I sc ≈ 6 μA, upon 8.3 kPa of the applied stress amplitude) with superior piezoelectric energy conversion efficiency of 66% compared to the single-mat device. Besides, OPNG also shows ultrasensitivity toward human movements such as foot strikes and walking. The weight measurement mapping is critically explored by principal component analysis that may have enormous applications in medical diagnosis to smart packaging industries. More importantly, fatigue test under continuous mechanical impact (over 6 months) shows great promise as a robust wearable mechanical energy harvester.

One-Step Fabrication of Stretchable Copper Nanowire Conductors by a Fast Photonic Sintering Technique and Its Application in Wearable Devices.

PubMed

Ding, Su; Jiu, Jinting; Gao, Yue; Tian, Yanhong; Araki, Teppei; Sugahara, Tohru; Nagao, Shijo; Nogi, Masaya; Koga, Hirotaka; Suganuma, Katsuaki; Uchida, Hiroshi

2016-03-09

Copper nanowire (CuNW) conductors have been considered to have a promising perspective in the area of stretchable electronics due to the low price and high conductivity. However, the fabrication of CuNW conductors suffers from harsh conditions, such as high temperature, reducing atmosphere, and time-consuming transfer step. Here, a simple and rapid one-step photonic sintering technique was developed to fabricate stretchable CuNW conductors on polyurethane (PU) at room temperature in air environment. It was observed that CuNWs were instantaneously deoxidized, welded and simultaneously embedded into the soft surface of PU through the one-step photonic sintering technique, after which highly conductive network and strong adhesion between CuNWs and PU substrates were achieved. The CuNW/PU conductor with sheet resistance of 22.1 Ohm/sq and transmittance of 78% was achieved by the one-step photonic sintering technique within only 20 μs in air. Besides, the CuNW/PU conductor could remain a low sheet resistance even after 1000 cycles of stretching/releasing under 10% strain. Two flexible electronic devices, wearable sensor and glove-shaped heater, were fabricated using the stretchable CuNW/PU conductor, demonstrating that our CuNW/PU conductor could be integrated into various wearable electronic devices for applications in food, clothes, and medical supplies fields.

OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring.

PubMed

Albright, Ryan K; Goska, Benjamin J; Hagen, Tory M; Chi, Mike Y; Cauwenberghs, G; Chiang, Patrick Y

2011-01-01

A wearable, multi-modal sensor is presented that can non-invasively monitor a patient's activity level and heart function concurrently for more than a week. The 4 in(2) sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin [1]. Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity for long-term observation into a patient's physical activity and subtle longitudinal changes.

Highly ordered nanowire arrays on plastic substrates for ultrasensitive flexible chemical sensors.

PubMed

McAlpine, Michael C; Ahmad, Habib; Wang, Dunwei; Heath, James R

2007-05-01

The development of a robust method for integrating high-performance semiconductors on flexible plastics could enable exciting avenues in fundamental research and novel applications. One area of vital relevance is chemical and biological sensing, which if implemented on biocompatible substrates, could yield breakthroughs in implantable or wearable monitoring systems. Semiconducting nanowires (and nanotubes) are particularly sensitive chemical sensors because of their high surface-to-volume ratios. Here, we present a scalable and parallel process for transferring hundreds of pre-aligned silicon nanowires onto plastic to yield highly ordered films for low-power sensor chips. The nanowires are excellent field-effect transistors, and, as sensors, exhibit parts-per-billion sensitivity to NO2, a hazardous pollutant. We also use SiO2 surface chemistries to construct a 'nano-electronic nose' library, which can distinguish acetone and hexane vapours via distributed responses. The excellent sensing performance coupled with bendable plastic could open up opportunities in portable, wearable or even implantable sensors.

Embroidered electrochemical sensors on gauze for rapid quantification of wound biomarkers.

PubMed

Liu, Xiyuan; Lillehoj, Peter B

2017-12-15

Electrochemical sensors are an attractive platform for analytical measurements due to their high sensitivity, portability and fast response time. These attributes also make electrochemical sensors well suited for wearable applications which require excellent flexibility and durability. Towards this end, we have developed a robust electrochemical sensor on gauze via a unique embroidery fabrication process for quantitative measurements of wound biomarkers. For proof of principle, this biosensor was used to detect uric acid, a biomarker for wound severity and healing, in simulated wound fluid which exhibits high specificity, good linearly from 0 to 800µM, and excellent reproducibility. Continuous sensing of uric acid was also performed using this biosensor which reveals that it can generate consistent and accurate measurements for up to 7h. Experiments to evaluate the robustness of the embroidered gauze sensor demonstrate that it offers excellent resilience against mechanical stress and deformation, making it a promising wearable platform for assessing and monitoring wound status in situ. Copyright © 2017 Elsevier B.V. All rights reserved.

Highly ordered nanowire arrays on plastic substrates for ultrasensitive flexible chemical sensors

NASA Astrophysics Data System (ADS)

McAlpine, Michael C.; Ahmad, Habib; Wang, Dunwei; Heath, James R.

2007-05-01

The development of a robust method for integrating high-performance semiconductors on flexible plastics could enable exciting avenues in fundamental research and novel applications. One area of vital relevance is chemical and biological sensing, which if implemented on biocompatible substrates, could yield breakthroughs in implantable or wearable monitoring systems. Semiconducting nanowires (and nanotubes) are particularly sensitive chemical sensors because of their high surface-to-volume ratios. Here, we present a scalable and parallel process for transferring hundreds of pre-aligned silicon nanowires onto plastic to yield highly ordered films for low-power sensor chips. The nanowires are excellent field-effect transistors, and, as sensors, exhibit parts-per-billion sensitivity to NO2, a hazardous pollutant. We also use SiO2 surface chemistries to construct a `nano-electronic nose' library, which can distinguish acetone and hexane vapours via distributed responses. The excellent sensing performance coupled with bendable plastic could open up opportunities in portable, wearable or even implantable sensors.

Highly ordered nanowire arrays on plastic substrates for ultrasensitive flexible chemical sensors

PubMed Central

McAlpine, Michael C.; Ahmad, Habib; Wang, Dunwei; Heath, James R.

2013-01-01

The development of a robust method for integrating high-performance semiconductors on flexible plastics could enable exciting avenues in fundamental research and novel applications. One area of vital relevance is chemical and biological sensing, which if implemented on biocompatible substrates, could yield breakthroughs in implantable or wearable monitoring systems. Semiconducting nanowires (and nanotubes) are particularly sensitive chemical sensors because of their high surface-to-volume ratios. Here, we present a scalable and parallel process for transferring hundreds of pre-aligned silicon nanowires onto plastic to yield highly ordered films for low-power sensor chips. The nanowires are excellent field-effect transistors, and, as sensors, exhibit parts-per-billion sensitivity to NO2, a hazardous pollutant. We also use SiO2 surface chemistries to construct a ‘nano-electronic nose’ library, which can distinguish acetone and hexane vapours via distributed responses. The excellent sensing performance coupled with bendable plastic could open up opportunities in portable, wearable or even implantable sensors. PMID:17450146

Wearable sensors objectively measure gait parameters in Parkinson’s disease

PubMed Central

Marxreiter, Franz; Gossler, Julia; Kohl, Zacharias; Reinfelder, Samuel; Gassner, Heiko; Aminian, Kamiar; Eskofier, Bjoern M.; Winkler, Jürgen; Klucken, Jochen

2017-01-01

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care. PMID:29020012

Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials.

PubMed

House, Samuel; Connell, Sean; Milligan, Ian; Austin, Daniel; Hayes, Tamara L; Chiang, Patrick

2011-01-01

We describe a low-cost wearable system that tracks the location of individuals indoors using commonly available inertial navigation sensors fused with radio frequency identification (RFID) tags placed around the smart environment. While conventional pedestrian dead reckoning (PDR) calculated with an inertial measurement unit (IMU) is susceptible to sensor drift inaccuracies, the proposed wearable prototype fuses the drift-sensitive IMU with a RFID tag reader. Passive RFID tags placed throughout the smart-building then act as fiducial markers that update the physical locations of each user, thereby correcting positional errors and sensor inaccuracy. Experimental measurements taken for a 55 m × 20 m 2D floor space indicate an over 1200% improvement in average error rate of the proposed RFID-fused system over dead reckoning alone.

Bio-assembled, piezoelectric prawn shell made self-powered wearable sensor for non-invasive physiological signal monitoring

NASA Astrophysics Data System (ADS)

Ghosh, Sujoy Kumar; Mandal, Dipankar

2017-03-01

A human interactive self-powered wearable sensor is designed using waste by-product prawn shells. The structural origin of intrinsic piezoelectric characteristics of bio-assembled chitin nanofibers has been investigated. It allows the prawn shell to make a tactile sensor that performs also as a highly durable mechanical energy harvester/nanogenerator. The feasibility and fundamental physics of self-powered consumer electronics even from human perception is highlighted by prawn shells made nanogenerator (PSNG). High fidelity and non-invasive monitoring of vital signs, such as radial artery pulse wave and coughing actions, may lead to the potential use of PSNG for early intervention. It is presumed that PSNG has enormous future aspects in real-time as well as remote health care assessment.

Wearable sensors in intelligent clothing for measuring human body temperature based on optical fiber Bragg grating.

PubMed

Li, Hongqiang; Yang, Haijing; Li, Enbang; Liu, Zhihui; Wei, Kejia

2012-05-21

Measuring body temperature is considerably important to physiological studies as well as clinical investigations. In recent years, numerous observations have been reported and various methods of measurement have been employed. The present paper introduces a novel wearable sensor in intelligent clothing for human body temperature measurement. The objective is the integration of optical fiber Bragg grating (FBG)-based sensors into functional textiles to extend the capabilities of wearable solutions for body temperature monitoring. In addition, the temperature sensitivity is 150 pm/°C, which is almost 15 times higher than that of a bare FBG. This study combines large and small pipes during fabrication to implant FBG sensors into the fabric. The law of energy conservation of the human body is considered in determining heat transfer between the body and its clothing. The mathematical model of heat transmission between the body and clothed FBG sensors is studied, and the steady-state thermal analysis is presented. The simulation results show the capability of the material to correct the actual body temperature. Based on the skin temperature obtained by the weighted average method, this paper presents the five points weighted coefficients model using both sides of the chest, armpits, and the upper back for the intelligent clothing. The weighted coefficients of 0.0826 for the left chest, 0.3706 for the left armpit, 0.3706 for the right armpit, 0.0936 for the upper back, and 0.0826 for the right chest were obtained using Cramer's Rule. Using the weighting coefficient, the deviation of the experimental result was ± 0.18 °C, which favors the use for clinical armpit temperature monitoring. Moreover, in special cases when several FBG sensors are broken, the weighted coefficients of the other sensors could be changed to obtain accurate body temperature.

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

PubMed

Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

2017-01-01

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

Scalable Production of Graphene-Based Wearable E-Textiles

PubMed Central

2017-01-01

Graphene-based wearable e-textiles are considered to be promising due to their advantages over traditional metal-based technology. However, the manufacturing process is complex and currently not suitable for industrial scale application. Here we report a simple, scalable, and cost-effective method of producing graphene-based wearable e-textiles through the chemical reduction of graphene oxide (GO) to make stable reduced graphene oxide (rGO) dispersion which can then be applied to the textile fabric using a simple pad-dry technique. This application method allows the potential manufacture of conductive graphene e-textiles at commercial production rates of ∼150 m/min. The graphene e-textile materials produced are durable and washable with acceptable softness/hand feel. The rGO coating enhanced the tensile strength of cotton fabric and also the flexibility due to the increase in strain% at maximum load. We demonstrate the potential application of these graphene e-textiles for wearable electronics with activity monitoring sensor. This could potentially lead to a multifunctional single graphene e-textile garment that can act both as sensors and flexible heating elements powered by the energy stored in graphene textile supercapacitors. PMID:29185706

Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application.

PubMed

Zhou, Qifan; Zhang, Hai; Lari, Zahra; Liu, Zhenbo; El-Sheimy, Naser

2016-10-21

Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments.

Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application

PubMed Central

Zhou, Qifan; Zhang, Hai; Lari, Zahra; Liu, Zhenbo; El-Sheimy, Naser

2016-01-01

Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments. PMID:27775673

Advances in wearable technology for rehabilitation.

PubMed

Bonato, Paolo

2009-01-01

Assessing the impact of rehabilitation interventions on the real life of individuals is a key element of the decision-making process required to choose a rehabilitation strategy. In the past, therapists and physicians inferred the effectiveness of a given rehabilitation approach from observations performed in a clinical setting and self-reports by patients. Recent developments in wearable technology have provided tools to complement the information gathered by rehabilitation personnel via patient's direct observation and via interviews and questionnaires. A new generation of wearable sensors and systems has emerged that allows clinicians to gather measures in the home and community settings that capture patients' activity level and exercise compliance, the effectiveness of pharmacological interventions, and the ability of patients to perform efficiently specific motor tasks. Available unobtrusive sensors allow clinical personnel to monitor patients' movement and physiological data such as heart rate, respiratory rate, and oxygen saturation. Cell phone technology and the widespread access to the Internet provide means to implement systems designed to remotely monitor patients' status and optimize interventions based on individual responses to different rehabilitation approaches. This chapter summarizes recent advances in the field of wearable technology and presents examples of application of this technology in rehabilitation.

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care.

PubMed

Bayo-Monton, Jose-Luis; Martinez-Millana, Antonio; Han, Weisi; Fernandez-Llatas, Carlos; Sun, Yan; Traver, Vicente

2018-06-06

Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ( p < < 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.

Highly Sensitive Multifilament Fiber Strain Sensors with Ultrabroad Sensing Range for Textile Electronics.

PubMed

Lee, Jaehong; Shin, Sera; Lee, Sanggeun; Song, Jaekang; Kang, Subin; Han, Heetak; Kim, SeulGee; Kim, Seunghoe; Seo, Jungmok; Kim, DaeEun; Lee, Taeyoon

2018-05-22

Highly stretchable fiber strain sensors are one of the most important components for various applications in wearable electronics, electronic textiles, and biomedical electronics. Herein, we present a facile approach for fabricating highly stretchable and sensitive fiber strain sensors by embedding Ag nanoparticles into a stretchable fiber with a multifilament structure. The multifilament structure and Ag-rich shells of the fiber strain sensor enable the sensor to simultaneously achieve both a high sensitivity and largely wide sensing range despite its simple fabrication process and components. The fiber strain sensor simultaneously exhibits ultrahigh gauge factors (∼9.3 × 10 5 and ∼659 in the first stretching and subsequent stretching, respectively), a very broad strain-sensing range (450 and 200% for the first and subsequent stretching, respectively), and high durability for more than 10 000 stretching cycles. The fiber strain sensors can also be readily integrated into a glove to control a hand robot and effectively applied to monitor the large volume expansion of a balloon and a pig bladder for an artificial bladder system, thereby demonstrating the potential of the fiber strain sensors as candidates for electronic textiles, wearable electronics, and biomedical engineering.

Towards a wearable sensor system for continuous occupational cold stress assessment

PubMed Central

AUSTAD, Hanne; WIGGEN, Øystein; FÆREVIK, Hilde; SEEBERG, Trine M.

2018-01-01

This study investigated the usefulness of continuous sensor data for improving occupational cold stress assessment. Eleven volunteer male subjects completed a 90–120-min protocol in cold environments, consisting of rest, moderate and hard work. Biomedical data were measured using a smart jacket with integrated temperature, humidity and activity sensors, in addition to a custom-made sensor belt worn around the chest. Other relevant sensor data were measured using commercially available sensors. The study aimed to improve decision support for workers in cold climates, by taking advantage of the information provided by data from the rapidly growing market of wearable sensors. Important findings were that the subjective thermal sensation did not correspond to the measured absolute skin temperature and that large differences were observed in both metabolic energy production and skin temperatures under identical exposure conditions. Temperature, humidity, activity and heart rate were found to be relevant parameters for cold stress assessment, and the locations of the sensors in the prototype jacket were adequate. The study reveals the need for cold stress assessment and indicates that a generalised approached is not sufficient to assess the stress on an individual level. PMID:29353859

Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study.

PubMed

Lin, Li-Fong; Lin, Yi-Jia; Lin, Zi-Hao; Chuang, Li-Yun; Hsu, Wei-Chun; Lin, Yuan-Hsiang

2017-06-19

Wearable devices based on inertial measurement units through wireless sensor networks have many applications such as real-time motion monitoring and functional outcome assessment of stroke rehabilitation. However, additional investigations are warranted to validate their clinical value, particularly in detecting the synergy patterns of movements after stroke. To explore the feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke and to compare the intervention effects (e.g., neurological recovery, active range of motion, and deviation angle) with those in a control group. A single-blind, randomized-controlled pilot study. Rehabilitation ward. A total of 18 patients with chronic stroke were randomly distributed into a device group and control group. Both groups received conventional rehabilitation; nevertheless, the device group was additionally subjected to 15 daily sessions at least three times a week for 5 weeks. The outcome measures included the upper extremity subscores of the Fugl-Meyer assessment, active range of motion, and deviation angle. These measurements were performed pre- and post-treatment. All five Fugl-Meyer assessment subscores improved in both the device and control groups after intervention; in particular, the "shoulder/elbow/forearm" subscore (p = 0.02, 0.03) and "total score" (p = 0.03, 0.03) substantially improved. The active range of motion of shoulder flexion and abduction substantially improved at pre-post treatment in both the device (p = 0.02, 0.03) and control (p = 0.02, 0.03) groups. The deviation angle of shoulder external rotation during shoulder abduction substantially improved in the device group (p = 0.02), but not in the control group. The designed wearable devices are practical and efficient for use in chronic patients with stroke. Wearable devices are expected to be useful for future internet-of-things rehabilitation clinical trials at home and in long-term care institutions.

A Wearable System for Gait Training in Subjects with Parkinson's Disease

PubMed Central

Casamassima, Filippo; Ferrari, Alberto; Milosevic, Bojan; Ginis, Pieter; Farella, Elisabetta; Rocchi, Laura

2014-01-01

In this paper, a system for gait training and rehabilitation for Parkinson's disease (PD) patients in a daily life setting is presented. It is based on a wearable architecture aimed at the provision of real-time auditory feedback. Recent studies have, in fact, shown that PD patients can receive benefit from a motor therapy based on auditory cueing and feedback, as happens in traditional rehabilitation contexts with verbal instructions given by clinical operators. To this extent, a system based on a wireless body sensor network and a smartphone has been developed. The system enables real-time extraction of gait spatio-temporal features and their comparison with a patient's reference walking parameters captured in the lab under clinical operator supervision. Feedback is returned to the user in form of vocal messages, encouraging the user to keep her/his walking behavior or to correct it. This paper describes the overall concept, the proposed usage scenario and the parameters estimated for the gait analysis. It also presents, in detail, the hardware-software architecture of the system and the evaluation of system reliability by testing it on a few subjects. PMID:24686731

SenseMyHeart: A cloud service and API for wearable heart monitors.

PubMed

Pinto Silva, P M; Silva Cunha, J P

2015-01-01

In the era of ubiquitous computing, the growing adoption of wearable systems and body sensor networks is trailing the path for new research and software for cardiovascular intensity, energy expenditure and stress and fatigue detection through cardiovascular monitoring. Several systems have received clinical-certification and provide huge amounts of reliable heart-related data in a continuous basis. PhysioNet provides equally reliable open-source software tools for ECG processing and analysis that can be combined with these devices. However, this software remains difficult to use in a mobile environment and for researchers unfamiliar with Linux-based systems. In the present paper we present an approach that aims at tackling these limitations by developing a cloud service that provides an API for a PhysioNet-based pipeline for ECG processing and Heart Rate Variability measurement. We describe the proposed solution, along with its advantages and tradeoffs. We also present some client tools (windows and Android) and several projects where the developed cloud service has been used successfully as a standard for Heart Rate and Heart Rate Variability studies in different scenarios.

Live Social Semantics

NASA Astrophysics Data System (ADS)

Alani, Harith; Szomszor, Martin; Cattuto, Ciro; van den Broeck, Wouter; Correndo, Gianluca; Barrat, Alain

Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment.

Lightweight, Wearable, Metal Rubber Sensor

NASA Technical Reports Server (NTRS)

Hill, Andrea

2015-01-01

For autonomous health monitoring. NanoSonic, Inc., has developed comfortable garments with multiple integrated sensors designed to monitor astronaut health throughout long-duration space missions. The combined high electrical conductivity, low mechanical modulus, and environmental robustness of the sensors make them an effective, lightweight, and comfortable alternative to conventional use of metal wiring and cabling.

Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD.

PubMed

Fletcher, Richard Ribón; Tam, Sharon; Omojola, Olufemi; Redemske, Richard; Kwan, Joyce

2011-01-01

We present a wearable sensor platform designed for monitoring and studying autonomic nervous system (ANS) activity for the purpose of mental health treatment and interventions. The mobile sensor system consists of a sensor band worn on the ankle that continuously monitors electrodermal activity (EDA), 3-axis acceleration, and temperature. A custom-designed ECG heart monitor worn on the chest is also used as an optional part of the system. The EDA signal from the ankle bands provides a measure sympathetic nervous system activity and used to detect arousal events. The optional ECG data can be used to improve the sensor classification algorithm and provide a measure of emotional "valence." Both types of sensor bands contain a Bluetooth radio that enables communication with the patient's mobile phone. When a specific arousal event is detected, the phone automatically presents therapeutic and empathetic messages to the patient in the tradition of Cognitive Behavioral Therapy (CBT). As an example of clinical use, we describe how the system is currently being used in an ongoing study for patients with drug-addiction and post-traumatic stress disorder (PTSD).

Printable elastic conductors with a high conductivity for electronic textile applications

PubMed Central

Matsuhisa, Naoji; Kaltenbrunner, Martin; Yokota, Tomoyuki; Jinno, Hiroaki; Kuribara, Kazunori; Sekitani, Tsuyoshi; Someya, Takao

2015-01-01

The development of advanced flexible large-area electronics such as flexible displays and sensors will thrive on engineered functional ink formulations for printed electronics where the spontaneous arrangement of molecules aids the printing processes. Here we report a printable elastic conductor with a high initial conductivity of 738 S cm−1 and a record high conductivity of 182 S cm−1 when stretched to 215% strain. The elastic conductor ink is comprised of Ag flakes, a fluorine rubber and a fluorine surfactant. The fluorine surfactant constitutes a key component which directs the formation of surface-localized conductive networks in the printed elastic conductor, leading to a high conductivity and stretchability. We demonstrate the feasibility of our inks by fabricating a stretchable organic transistor active matrix on a rubbery stretchability-gradient substrate with unimpaired functionality when stretched to 110%, and a wearable electromyogram sensor printed onto a textile garment. PMID:26109453

Implementation study of wearable sensors for activity recognition systems

PubMed Central

Ghassemian, Mona

2015-01-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

Telehealth in older adults with cancer in the United States: The emerging use of wearable sensors.

PubMed

Shen, John; Naeim, Arash

2017-11-01

As the aging and cancer populations in the world continue to increase, the need for complements to traditional geriatric assessments and the logical incorporation of fast and reliable telehealth tools have become interlinked. In the United States, studies examining the use of telehealth for chronic disease management have shown promising results in small groups. The implementation of health technology on a broader scale requires older adults to both accept and adapt such innovation into routine medical care. Though the commercial and recreational use of new technology has increased in older individuals, the transition into creating a smart and connected home that can interface with both patients and healthcare professionals is in its early phases. Current limitations include an inherent digital divide, as well as concerns regarding privacy, data volume, rapid change, cost and reimbursement. The emergence of low-cost, high-fidelity wearable sensors with a spectrum of clinical utility may be the key to increased use and adaptation by older adults. An opportunity to utilize wearable sensors for objective and real-time assessment of older patients with cancer for baseline functional status and treatment toxicity may be on the horizon. Copyright © 2017 Elsevier Inc. All rights reserved.

A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors.

PubMed

Kakria, Priyanka; Tripathi, N K; Kitipawang, Peerapong

2015-01-01

Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heart monitoring system is developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio. The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years) using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts). The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances.

What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?

PubMed Central

Carcreff, Lena; Paraschiv-Ionescu, Anisoara; De Coulon, Geraldo; Armand, Stéphane; Aminian, Kamiar

2018-01-01

Wearable inertial devices have recently been used to evaluate spatiotemporal parameters of gait in daily life situations. Given the heterogeneity of gait patterns in children with cerebral palsy (CP), the sensor placement and analysis algorithm may influence the validity of the results. This study aimed at comparing the spatiotemporal measurement performances of three wearable configurations defined by different sensor positioning on the lower limbs: (1) shanks and thighs, (2) shanks, and (3) feet. The three configurations were selected based on their potential to be used in daily life for children with CP and typically developing (TD) controls. For each configuration, dedicated gait analysis algorithms were used to detect gait events and compute spatiotemporal parameters. Fifteen children with CP and 11 TD controls were included. Accuracy, precision, and agreement of the three configurations were determined in comparison with an optoelectronic system as a reference. The three configurations were comparable for the evaluation of TD children and children with a low level of disability (CP-GMFCS I) whereas the shank-and-thigh-based configuration was more robust regarding children with a higher level of disability (CP-GMFCS II–III). PMID:29385700

A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors

PubMed Central

Kakria, Priyanka; Tripathi, N. K.; Kitipawang, Peerapong

2015-01-01

Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heart monitoring system is developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio. The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years) using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts). The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances. PMID:26788055

A novel yet effective motion artefact reduction method for continuous physiological monitoring

NASA Astrophysics Data System (ADS)

Alzahrani, A.; Hu, S.; Azorin-Peris, V.; Kalawsky, R.; Zhang, X.; Liu, C.

2014-03-01

This study presents a non-invasive and wearable optical technique to continuously monitor vital human signs as required for personal healthcare in today's increasing ageing population. The study has researched an effective way to capture human critical physiological parameters, i.e., oxygen saturation (SaO2%), heart rate, respiration rate, body temperature, heart rate variability by a closely coupled wearable opto-electronic patch sensor (OEPS) together with real-time and secure wireless communication functionalities. The work presents the first step of this research; an automatic noise cancellation method using a 3-axes MEMS accelerometer to recover signals corrupted by body movement which is one of the biggest sources of motion artefacts. The effects of these motion artefacts have been reduced by an enhanced electronic design and development of self-cancellation of noise and stability of the sensor. The signals from the acceleration and the opto-electronic sensor are highly correlated thus leading to the desired pulse waveform with rich bioinformatics signals to be retrieved with reduced motion artefacts. The preliminary results from the bench tests and the laboratory setup demonstrate that the goal of the high performance wearable opto-electronics is viable and feasible.

Improved response time of flexible microelectromechanical sensors employing eco-friendly nanomaterials.

PubMed

Fan, Shicheng; Dan, Li; Meng, Lingju; Zheng, Wei; Elias, Anastasia; Wang, Xihua

2017-11-09

Flexible force/pressure sensors are of interest for academia and industry and have applications in wearable technologies. Most of such sensors on the market or reported in journal publications are based on the operation mechanism of probing capacitance or resistance changes of the materials under pressure. Recently, we reported the microelectromechanical (MEM) sensors based on a different mechanism: mechanical switches. Multiples of such MEM sensors can be integrated to achieve the same function of regular force/pressure sensors while having the advantages of ease of fabrication and long-term stability in operation. Herein, we report the dramatically improved response time (more than one order of magnitude) of these MEM sensors by employing eco-friendly nanomaterials-cellulose nanocrystals. For instance, the incorporation of polydimethysiloxane filled with cellulose nanocrystals shortened the response time of MEM sensors from sub-seconds to several milliseconds, leading to the detection of both diastolic and systolic pressures in the radial arterial blood pressure measurement. Comprehensive mechanical and electrical characterization of the materials and the devices reveal that greatly enhanced storage modulus and loss modulus play key roles in this improved response time. The demonstrated fast-response flexible sensors enabled continuous monitoring of heart rate and complex cardiovascular signals using pressure sensors for future wearable sensing platforms.

Nano-enabled sensors, electronics and energy source on polymer, paper and thread substrates

NASA Astrophysics Data System (ADS)

Mostafalu, Pooria

Over the past decades, design and development of portable devices for monitoring of biomarkers especially for at risk patients is receiving considerable attention. These devices are either single use diagnostic platforms, wearable on body or on fabric, or they are implanted close to the tissue and organ that it monitors and cures. Sensors, energy sources, and data acquisition devices are the main components of a such monitoring platform. Sensors collect the information using bio-recognition tools such as enzymes and antibodies. Then, the transducers (electrodes, fluorophore, etc) convert it to the appropriate format, for instance electrical and optical signals. After that, data acquisition system amplifies and digitizes the signal and transfers the data to the recording instruments for further processing. Moreover, energy sources are necessary for powering the sensors and electronics. In wearable and implantable applications, these devices need to be flexible, light weight and biocompatible, and their performance should be similar to their rigid counterparts. In this dissertation we address these requirement for wearable and implantable devices. We showed integrated sensors, electronics, and energy sources on flexible polymers, paper, and thread. These devices provide many advantages for monitoring of the physiological condition of a patient and treatment accordingly. Real-time capability of the platform was enabled using wireless telemetry. One of the major innovations of this dissertation is the use of thread as a substrate for making medical diagnostic devices. While conventional substrates (glass, silicon, polyimide, PDMS etc) hold great promise for making wearable and implantable devices, their overall structure and form has remained essentially two dimensional, limiting their function to tissue surfaces such as skin. However, the ability to integrate functional components such as sensors, actuators, and electronics in a way that they penetrate multiple layers of tissues in a 3D topology would be a significant surgical advance. We have devised an integrated thread-based diagnostic (TDD) system with the ability to measure physical (strain and temperature) and chemical (pH and glucose) markers in the body in vivo. Such device was made from threads, which have been widely used in the apparel industry and is readily available as a low-cost biocompatible material.

A Textile-Based Wearable Sensing Device Designed for Monitoring the Flexion Angle of Elbow and Knee Movements

PubMed Central

Shyr, Tien-Wei; Shie, Jing-Wen; Jiang, Chang-Han; Li, Jung-Jen

2014-01-01

In this work a wearable gesture sensing device consisting of a textile strain sensor, using elastic conductive webbing, was designed for monitoring the flexion angle of elbow and knee movements. The elastic conductive webbing shows a linear response of resistance to the flexion angle. The wearable gesture sensing device was calibrated and then the flexion angle-resistance equation was established using an assembled gesture sensing apparatus with a variable resistor and a protractor. The proposed device successfully monitored the flexion angle during elbow and knee movements. PMID:24577526

Implementing Internet of Things in a military command and control environment

NASA Astrophysics Data System (ADS)

Raglin, Adrienne; Metu, Somiya; Russell, Stephen; Budulas, Peter

2017-05-01

While the term Internet of Things (IoT) has been coined relatively recently, it has deep roots in multiple other areas of research including cyber-physical systems, pervasive and ubiquitous computing, embedded systems, mobile ad-hoc networks, wireless sensor networks, cellular networks, wearable computing, cloud computing, big data analytics, and intelligent agents. As the Internet of Things, these technologies have created a landscape of diverse heterogeneous capabilities and protocols that will require adaptive controls to effect linkages and changes that are useful to end users. In the context of military applications, it will be necessary to integrate disparate IoT devices into a common platform that necessarily must interoperate with proprietary military protocols, data structures, and systems. In this environment, IoT devices and data will not be homogeneous and provenance-controlled (i.e. single vendor/source/supplier owned). This paper presents a discussion of the challenges of integrating varied IoT devices and related software in a military environment. A review of contemporary commercial IoT protocols is given and as a practical example, a middleware implementation is proffered that provides transparent interoperability through a proactive message dissemination system. The implementation is described as a framework through which military applications can integrate and utilize commercial IoT in conjunction with existing military sensor networks and command and control (C2) systems.

Dedicated cardiac rehabilitation wearable sensor and its clinical potential.

PubMed

Lee, Hooseok; Chung, Heewon; Ko, Hoon; Jeong, Changwon; Noh, Se-Eung; Kim, Chul; Lee, Jinseok

2017-01-01

We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.

Designing Metallic and Insulating Nanocrystal Heterostructures to Fabricate Highly Sensitive and Solution Processed Strain Gauges for Wearable Sensors.

PubMed

Lee, Woo Seok; Lee, Seung-Wook; Joh, Hyungmok; Seong, Mingi; Kim, Haneun; Kang, Min Su; Cho, Ki-Hyun; Sung, Yun-Mo; Oh, Soong Ju

2017-12-01

All-solution processed, high-performance wearable strain sensors are demonstrated using heterostructure nanocrystal (NC) solids. By incorporating insulating artificial atoms of CdSe quantum dot NCs into metallic artificial atoms of Au NC thin film matrix, metal-insulator heterostructures are designed. This hybrid structure results in a shift close to the percolation threshold, modifying the charge transport mechanism and enhancing sensitivity in accordance with the site percolation theory. The number of electrical pathways is also manipulated by creating nanocracks to further increase its sensitivity, inspired from the bond percolation theory. The combination of the two strategies achieves gauge factor up to 5045, the highest sensitivity recorded among NC-based strain gauges. These strain sensors show high reliability, durability, frequency stability, and negligible hysteresis. The fundamental charge transport behavior of these NC solids is investigated and the combined site and bond percolation theory is developed to illuminate the origin of their enhanced sensitivity. Finally, all NC-based and solution-processed strain gauge sensor arrays are fabricated, which effectively measure the motion of each finger joint, the pulse of heart rate, and the movement of vocal cords of human. This work provides a pathway for designing low-cost and high-performance electronic skin or wearable devices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review

PubMed Central

Rooksby, John; Gray, Cindy M

2018-01-01

Background Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines. PMID:29572200

Development of a belt-type wearable sensor system with multi-function for home health care

NASA Astrophysics Data System (ADS)

Ban, Yunho; Choi, Samjin; Jiang, Zhongwei; Park, Chanwon

2005-12-01

Some reports show that the physiological information measured in hospital is not enough without the one measured in home. The physiological information monitored in home, therefore, is strongly required recently. The goal of this research is to develop a wearable and tractable sensor system for detecting biomedical signals such as cardiac rhythm, respiration, body movement, and percentage of body fat (%BF) and for home health care. A belt type sensor for this purpose is developed, which consists of sensing materials of PVDF film and conductive fabrics. Also several data processing techniques, such as the discrete wavelet transform, cross correlation and adaptive filtering method, were introduced to eliminate noises and base wandering and to extract the specified components. The ECG and respiration signals obtained by the proposed belt type sensor system gave good agreements with commercial medical system. Furthermore, the body fat (%BF) measurement based on the four-electrode BIA was also built in the belt sensor. The body fat was calculated by measuring the body impedance from the belt type sensor and compared with the predicted %BF measured by the commercial adipometer (TBF-607). The results validated also the efficiency of the belt type sensor system.

Wearable Environmental and Physiological Sensing Unit

NASA Technical Reports Server (NTRS)

Spremo, Stevan; Ahlman, Jim; Stricker, Ed; Santos, Elmer

2007-01-01

The wearable environmental and physiological sensing unit (WEPS) is a prototype of systems to be worn by emergency workers (e.g., firefighters and members of hazardous-material response teams) to increase their level of safety. The WEPS includes sensors that measure a few key physiological and environmental parameters, a microcontroller unit that processes the digitized outputs of the sensors, and a radio transmitter that sends the processed sensor signals to a computer in a mobile command center for monitoring by a supervisor. The monitored parameters serve as real-time indications of the wearer s physical condition and level of activity, and of the degree and type of danger posed by the wearer s environment. The supervisor could use these indications to determine, for example, whether the wearer should withdraw in the face of an increasing hazard or whether the wearer should be rescued.

A Printed Organic Circuit System for Wearable Amperometric Electrochemical Sensors.

PubMed

Shiwaku, Rei; Matsui, Hiroyuki; Nagamine, Kuniaki; Uematsu, Mayu; Mano, Taisei; Maruyama, Yuki; Nomura, Ayako; Tsuchiya, Kazuhiko; Hayasaka, Kazuma; Takeda, Yasunori; Fukuda, Takashi; Kumaki, Daisuke; Tokito, Shizuo

2018-04-23

Wearable sensor device technologies, which enable continuous monitoring of biological information from the human body, are promising in the fields of sports, healthcare, and medical applications. Further thinness, light weight, flexibility and low-cost are significant requirements for making the devices attachable onto human tissues or clothes like a patch. Here we demonstrate a flexible and printed circuit system consisting of an enzyme-based amperometric sensor, feedback control and amplification circuits based on organic thin-film transistors. The feedback control and amplification circuits based on pseudo-CMOS inverters were successfuly integrated by printing methods on a plastic film. This simple system worked very well like a potentiostat for electrochemical measurements, and enabled the quantitative and real-time measurement of lactate concentration with high sensitivity of 1 V/mM and a short response time of a hundred seconds.

cHRV Uncovering Daily Stress Dynamics Using Bio-Signal from Consumer Wearables.

PubMed

Hao, Tian; Chang, Henry; Ball, Marion; Lin, Kun; Zhu, Xinxin

2017-01-01

Knowing the dynamics of one's daily stress is essential to effective stress management in the context of smart and connected health. However, there lacks a practical and unobtrusive means to obtain real-time and longitudinal stress information. In this paper, we attempt to derive a convenient HRV-based (heart rate variability) biomarker named cHRV, which can be used to reliably reflect stress dynamics. cHRV's key advantage lies in its low maintenance and high practicality. It can be efficiently calculated only using data from photoplethysmography (PPG) sensors, the mainstream heart rate sensor embedded in most of the consumer wearables like Apple Watch. Benefiting from the proliferation of wearables, cHRV is ideal for day-to-day stress monitoring. To evaluate its feasibility and performance, we have conducted 14 in-lab controlled experiments. The result shows that the proposed cHRV has strong correlation with the stress dynamics (r > 0.95), therefore exhibits great potential for continuous stress assessment.

Mobile health: the power of wearables, sensors, and apps to transform clinical trials.

PubMed

Munos, Bernard; Baker, Pamela C; Bot, Brian M; Crouthamel, Michelle; de Vries, Glen; Ferguson, Ian; Hixson, John D; Malek, Linda A; Mastrototaro, John J; Misra, Veena; Ozcan, Aydogan; Sacks, Leonard; Wang, Pei

2016-07-01

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies. © 2016 New York Academy of Sciences.

Detecting Gunshots Using Wearable Accelerometers

PubMed Central

Loeffler, Charles E.

2014-01-01

Gun violence continues to be a staggering and seemingly intractable issue in many communities. The prevalence of gun violence among the sub-population of individuals under court-ordered community supervision provides an opportunity for intervention using remote monitoring technology. Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. This paper presents the first results demonstrating the feasibility of using wearable inertial sensors to recognize wrist movements and other signals corresponding to firearm usage. Data were collected from accelerometers worn on the wrists of subjects shooting a number of different firearms, conducting routine daily activities, and participating in activities and tasks that could be potentially confused with firearm discharges. A training sample was used to construct a combined detector and classifier for individual gunshots, which achieved a classification accuracy of 99.4 percent when tested against a hold-out sample of observations. These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges. PMID:25184416

Detecting gunshots using wearable accelerometers.

PubMed

Loeffler, Charles E

2014-01-01

Gun violence continues to be a staggering and seemingly intractable issue in many communities. The prevalence of gun violence among the sub-population of individuals under court-ordered community supervision provides an opportunity for intervention using remote monitoring technology. Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. This paper presents the first results demonstrating the feasibility of using wearable inertial sensors to recognize wrist movements and other signals corresponding to firearm usage. Data were collected from accelerometers worn on the wrists of subjects shooting a number of different firearms, conducting routine daily activities, and participating in activities and tasks that could be potentially confused with firearm discharges. A training sample was used to construct a combined detector and classifier for individual gunshots, which achieved a classification accuracy of 99.4 percent when tested against a hold-out sample of observations. These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges.

The challenges facing wearable sensor systems.

PubMed

McAdams, Eric; Gehin, Claudine; Massot, Bertrand; McLaughlin, James

2012-01-01

It has been pointed out that, in spite of significant national and international funding programmes, there is a dearth of successfully commercialised wearable monitoring systems. Although problems such as financial reimbursement, device interoperability and the present lack of the required connected healthcare infrastructure are major hurdles to the provision of remote clinical monitoring of home-based patients, the "Mount Everest" of monitoring applications, why are wearable systems not already commercialised and used in less demanding applications? The numerous wearable systems which appear on the Web and even in the literature are, for the most part, basic prototypes unsuited to the demands of real-life applications. SMEs which do seek to commercialise clinically promising systems are unfortunately faced with many challenges and few as yet have survived long enough to successfully commercialise their innovations.

Free-living monitoring of Parkinson's disease: Lessons from the field.

PubMed

Del Din, Silvia; Godfrey, Alan; Mazzà, Claudia; Lord, Sue; Rochester, Lynn

2016-09-01

Wearable technology comprises miniaturized sensors (eg, accelerometers) worn on the body and/or paired with mobile devices (eg, smart phones) allowing continuous patient monitoring in unsupervised, habitual environments (termed free-living). Wearable technologies are revolutionizing approaches to health care as a result of their utility, accessibility, and affordability. They are positioned to transform Parkinson's disease (PD) management through the provision of individualized, comprehensive, and representative data. This is particularly relevant in PD where symptoms are often triggered by task and free-living environmental challenges that cannot be replicated with sufficient veracity elsewhere. This review concerns use of wearable technology in free-living environments for people with PD. It outlines the potential advantages of wearable technologies and evidence for these to accurately detect and measure clinically relevant features including motor symptoms, falls risk, freezing of gait, gait, functional mobility, and physical activity. Technological limitations and challenges are highlighted, and advances concerning broader aspects are discussed. Recommendations to overcome key challenges are made. To date there is no fully validated system to monitor clinical features or activities in free-living environments. Robust accuracy and validity metrics for some features have been reported, and wearable technology may be used in these cases with a degree of confidence. Utility and acceptability appears reasonable, although testing has largely been informal. Key recommendations include adopting a multidisciplinary approach for standardizing definitions, protocols, and outcomes. Robust validation of developed algorithms and sensor-based metrics is required along with testing of utility. These advances are required before widespread clinical adoption of wearable technology can be realized. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Wearable physiological systems and technologies for metabolic monitoring.

PubMed

Gao, Wei; Brooks, George A; Klonoff, David C

2018-03-01

Wearable sensors allow continuous monitoring of metabolites for diabetes, sports medicine, exercise science, and physiology research. These sensors can continuously detect target analytes in skin interstitial fluid (ISF), tears, saliva, and sweat. In this review, we will summarize developments on wearable devices and their potential applications in research, clinical practice, and recreational and sporting activities. Sampling skin ISF can require insertion of a needle into the skin, whereas sweat, tears, and saliva can be sampled by devices worn outside the body. The most widely sampled metabolite from a wearable device is glucose in skin ISF for monitoring diabetes patients. Continuous ISF glucose monitoring allows estimation of the glucose concentration in blood without the pain, inconvenience, and blood waste of fingerstick capillary blood glucose testing. This tool is currently used by diabetes patients to provide information for dosing insulin and determining a diet and exercise plan. Similar technologies for measuring concentrations of other analytes in skin ISF could be used to monitor athletes, emergency responders, warfighters, and others in states of extreme physiological stress. Sweat is a potentially useful substrate for sampling analytes for metabolic monitoring during exercise. Lactate, sodium, potassium, and hydrogen ions can be measured in sweat. Tools for converting the concentrations of these analytes sampled from sweat, tears, and saliva into blood concentrations are being developed. As an understanding of the relationships between the concentrations of analytes in blood and easily sampled body fluid increases, then the benefits of new wearable devices for metabolic monitoring will also increase.