New Information Technologies: Some Observations on What Is in Store for Libraries.

ERIC Educational Resources Information Center

Black, John B.

This outline of new technological developments and their applications in the library and information world considers innovations in three areas: automation, telecommunications, and the publishing industry. There is mention of the growth of online systems, minicomputers, microcomputers, and word processing; the falling costs of automation; the…

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.

PubMed

Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

2017-02-08

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

PubMed Central

Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

2017-01-01

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

PubMed

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2016-12-01

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow's. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

NASA Astrophysics Data System (ADS)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

Improving detection of low SNR targets using moment-based detection

NASA Astrophysics Data System (ADS)

Young, Shannon R.; Steward, Bryan J.; Hawks, Michael; Gross, Kevin C.

2016-05-01

Increases in the number of cameras deployed, frame rate, and detector array sizes have led to a dramatic increase in the volume of motion imagery data that is collected. Without a corresponding increase in analytical manpower, much of the data is not analyzed to full potential. This creates a need for fast, automated, and robust methods for detecting signals of interest. Current approaches fall into two categories: detect-before-track (DBT), which are fast but often poor at detecting dim targets, and track-before-detect (TBD) methods which can offer better performance but are typically much slower. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallelizable. It was found that intelligent selection of parameters can improve probability of detection by as much as 25% compared to earlier work with higherorder moments. The present method can reduce detection thresholds by 40% compared to the Reed-Xiaoli anomaly detector for low SNR targets (for a given probability of detection and false alarm).

Status of the Transneptunian Automated Occultation Survey (TAOS II)

NASA Astrophysics Data System (ADS)

Lehner, Matthew; Wang, Shiang-Yu; Reyes-Ruiz, Mauricio; Alcock, Charles; Castro Chacón, Joel; Chen, Wen-Ping; Chu, You-Hua; Cook, Kem H.; Figueroa, Liliana; Geary, John C.; Hernandez, Benjamin; Huang, Chung-Kai; Norton, Timothy; Szentgyorgyi, Andrew; Yen, Wei-Ling; Zhang, Zhi-Wei

2017-10-01

The Transneptunian Automated Occultation Survey (TAOS II) will aim to detect occultations of stars by small (~1 km diameter) objects in the Kuiper Belt and beyond. Such events are very rare (<0.001 events per star per year) and short in duration (~200 ms), so many stars must be monitored at a high readout cadence. TAOS II will operate three 1.3 meter telescopes at the Observatorio Astronómico Nacional at San Pedro Mártir in Baja California, México. With a 2.3 square degree field of view and a high speed camera comprising CMOS imagers, the survey will monitor 10,000 stars simultaneously with all three telescopes at a readout cadence of 20 Hz. Construction of the site began in the fall of 2013 and was completed this summer. Telescope installation began in August 2017. This poster will provide an update on the status of the survey development and the schedule leading to the beginning of survey operations.

Determination of simple thresholds for accelerometry-based parameters for fall detection.

PubMed

Kangas, Maarit; Konttila, Antti; Winblad, Ilkka; Jämsä, Timo

2007-01-01

The increasing population of elderly people is mainly living in a home-dwelling environment and needs applications to support their independency and safety. Falls are one of the major health risks that affect the quality of life among older adults. Body attached accelerometers have been used to detect falls. The placement of the accelerometric sensor as well as the fall detection algorithms are still under investigation. The aim of the present pilot study was to determine acceleration thresholds for fall detection, using triaxial accelerometric measurements at the waist, wrist, and head. Intentional falls (forward, backward, and lateral) and activities of daily living (ADL) were performed by two voluntary subjects. The results showed that measurements from the waist and head have potential to distinguish between falls and ADL. Especially, when the simple threshold-based detection was combined with posture detection after the fall, the sensitivity and specificity of fall detection were up to 100 %. On the contrary, the wrist did not appear to be an optimal site for fall detection.

MARC: A Thought Experiment in the Morality of Automated Marking of English

ERIC Educational Resources Information Center

Elliott, Victoria

2014-01-01

Automated essay scoring programs are becoming more common and more technically advanced. They provoke strong reactions from both their advocates and their detractors. Arguments tend to fall into two categories: technical and principled. This paper argues that since technical difficulties will be overcome with time, the debate ought to be held in…

After the Fall: The Use of Surplus Capacity in an Academic Library Automation System.

ERIC Educational Resources Information Center

Wright, A. J.

The possible uses of excess central processing unit capacity in an integrated academic library automation system discussed in this draft proposal include (1) in-house services such as word processing, electronic mail, management decision support using PERT/CPM techniques, and control of physical plant operation; (2) public services such as the…

Changing the Tooth-to-Tail Ratio Using Robotics and Automation to Beat Sequestration

DTIC Science & Technology

2015-10-01

September–October 2015 | 75 Views Changing the Tooth-to-Tail Ratio Using Robotics and Automation to Beat Sequestration Capt Rachael L. Nussbaum...falls remains a matter of great debate. The US Air Force is the world’s leader in war-fighting automation and robotics . In fact, in accordance with the...progress in using robots to en- hance the effectiveness of the larger part of Air Force business. The amount of maintenance required by modern aerial war

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

PubMed

Pham, Thuy T; Moore, Steven T; Lewis, Simon John Geoffrey; Nguyen, Diep N; Dutkiewicz, Eryk; Fuglevand, Andrew J; McEwan, Alistair L; Leong, Philip H W

2017-11-01

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).

Doppler radar sensor positioning in a fall detection system.

PubMed

Liu, Liang; Popescu, Mihail; Ho, K C; Skubic, Marjorie; Rantz, Marilyn

2012-01-01

Falling is a common health problem for more than a third of the United States population over 65. We are currently developing a Doppler radar based fall detection system that already has showed promising results. In this paper, we study the sensor positioning in the environment with respect to the subject. We investigate three sensor positions, floor, wall and ceiling of the room, in two experimental configurations. Within each system configuration, subjects performed falls towards or across the radar sensors. We collected 90 falls and 341 non falls for the first configuration and 126 falls and 817 non falls for the second one. Radar signature classification was performed using a SVM classifier. Fall detection performance was evaluated using the area under the ROC curves (AUCs) for each sensor deployment. We found that a fall is more likely to be detected if the subject is falling toward or away from the sensor and a ceiling Doppler radar is more reliable for fall detection than a wall mounted one.

Student Characteristics as Compared to the Community Profile, Fall, 1986. Volume XVI, No. 8.

ERIC Educational Resources Information Center

Flaherty, Toni

In fall 1986, a study was conducted at Illinois' William Rainey Harper College (WRHC) to provide a student profile for general information purposes, to gather data not available on the college's automated student data file, and to analyze WRHC's market outreach. Surveys were mailed to random samples of 500 credit degree students and 300 continuing…

Student Characteristics as Compared to the Community Profile, Fall, 1985. [Research Report Series], Volume XV, No. 11.

ERIC Educational Resources Information Center

Lucas, John A.

In fall 1985, a study was conducted at William Rainey Harper College (WRHC) to provide a student profile for general information purposes, to gather data not available on the college's automated data file, and to analyze WRHC's marketing outreach. Surveys were mailed to random samples of 500 credit degree students and 300 continuing education…

The design and development of a long-term fall detection system incorporated into a custom vest for the elderly.

PubMed

Bourke, Alan K; van de Ven, Pepijn W J; Chaya, Amy E; OLaighin, Gearóid M; Nelson, John

2008-01-01

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer, microcontroller, battery and Bluetooth module. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested on young healthy subjects performing normal activities of daily living (ADL) and falls onto crash mats, while wearing the best and sensor. Results show that falls can de distinguished from normal activities with a sensitivity >90% and a specificity of >99%, from a total data set of 264 falls and 165 normal ADL. By incorporating the fall-detection sensor into a custom designed garment it is anticipated that greater compliance when wearing a fall-detection system can be achieved and will help reduce the incidence of the long-lie, when falls occur in the elderly population. However further long-term testing using elderly subjects is required to validate the systems performance.

A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

PubMed Central

Huang, Chih-Ning; Chan, Chia-Tai

2014-01-01

Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person. PMID:24743841

Kinect4FOG: monitoring and improving mobility in people with Parkinson's using a novel system incorporating the Microsoft Kinect v2.

PubMed

Amini, Amin; Banitsas, Konstantinos; Young, William R

2018-05-23

Parkinson's is a neurodegenerative condition associated with several motor symptoms including tremors and slowness of movement. Freezing of gait (FOG); the sensation of one's feet being "glued" to the floor, is one of the most debilitating symptoms associated with advanced Parkinson's. FOG not only contributes to falls and related injuries, but also compromises quality of life as people often avoid engaging in functional daily activities both inside and outside the home. In the current study, we describe a novel system designed to detect FOG and falling in people with Parkinson's (PwP) as well as monitoring and improving their mobility using laser-based visual cues cast by an automated laser system. The system utilizes a RGB-D sensor based on Microsoft Kinect v2 and a laser casting system consisting of two servo motors and an Arduino microcontroller. This system was evaluated by 15 PwP with FOG. Here, we present details of the system along with a summary of feedback provided by PwP. Despite limitations regarding its outdoor use, feedback was very positive in terms of domestic usability and convenience, where 12/15 PwP showed interest in installing and using the system at their homes. Implications for Rehabilitation Providing an automatic and remotely manageable monitoring system for PwP gait analysis and fall detection. Providing an automatic, unobtrusive and dynamic visual cue system for PwP based on laser line projection. Gathering feedback from PwP about the practical usage of the implemented system through focus group events.

Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care.

PubMed

Potter, Patricia; Allen, Kelly; Costantinou, Eileen; Klinkenberg, William Dean; Malen, Jill; Norris, Traci; O'Connor, Elizabeth; Roney, Wilhemina; Tymkew, Heidi Hahn; Wolf, Laurie

2017-08-01

Sensor technology that dynamically identifies hospitalized patients' fall risk and detects and alerts nurses of high-risk patients' early exits out of bed has potential for reducing fall rates and preventing patient harm. During Phase 1 (August 2014-January 2015) of a previously reported performance improvement project, an innovative depth sensor was evaluated on two inpatient medical units to study fall characteristics. In Phase 2 (April 2015-January 2016), a combined depth and bed sensor system designed to assign patient fall probability, detect patient bed exits, and subsequently prevent falls was evaluated. Fall detection depth sensors remained in place on two medicine units; bed sensors used to detect patient bed exits were added on only one of the medicine units. Fall rates and fall with injury rates were evaluated on both units. During Phase 2, the designated evaluation unit had 14 falls, for a fall rate of 2.22 per 1,000 patient-days-a 54.1% reduction compared with the Phase 1 fall rate. The difference in rates from Phase 1 to Phase 2 was statistically significant (z = 2.20; p = 0.0297). The comparison medicine unit had 30 falls-a fall rate of 4.69 per 1,000 patient-days, representing a 57.9% increase as compared with Phase 1. A fall detection sensor system affords a level of surveillance that standard fall alert systems do not have. Fall prevention remains a complex issue, but sensor technology is a viable fall prevention option. Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.

Infrared polarimetric sensing of oil on water

NASA Astrophysics Data System (ADS)

Chenault, David B.; Vaden, Justin P.; Mitchell, Douglas A.; DeMicco, Erik D.

2016-10-01

Infrared polarimetry is an emerging sensing modality that offers the potential for significantly enhanced contrast in situations where conventional thermal imaging falls short. Polarimetric imagery leverages the different polarization signatures that result from material differences, surface roughness quality, and geometry that are frequently different from those features that lead to thermal signatures. Imaging of the polarization in a scene can lead to enhanced understanding, particularly when materials in a scene are at thermal equilibrium. Polaris Sensor Technologies has measured the polarization signatures of oil on water in a number of different scenarios and has shown significant improvement in detection through the contrast improvement offered by polarimetry. The sensing improvement offers the promise of automated detection of oil spills and leaks for routine monitoring and accidents with the added benefit of being able to continue monitoring at night. In this paper, we describe the instrumentation, and the results of several measurement exercises in both controlled and uncontrolled conditions.

Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS

NASA Astrophysics Data System (ADS)

Bang, Eugene; Lee, Jiyun

2013-11-01

ionospheric anomalies occurring during severe ionospheric storms can pose integrity threats to Global Navigation Satellite System (GNSS) Ground-Based Augmentation Systems (GBAS). Ionospheric anomaly threat models for each region of operation need to be developed to analyze the potential impact of these anomalies on GBAS users and develop mitigation strategies. Along with the magnitude of ionospheric gradients, the speed of the ionosphere "fronts" in which these gradients are embedded is an important parameter for simulation-based GBAS integrity analysis. This paper presents a methodology for automated ionosphere front velocity estimation which will be used to analyze a vast amount of ionospheric data, build ionospheric anomaly threat models for different regions, and monitor ionospheric anomalies continuously going forward. This procedure automatically selects stations that show a similar trend of ionospheric delays, computes the orientation of detected fronts using a three-station-based trigonometric method, and estimates speeds for the front using a two-station-based method. It also includes fine-tuning methods to improve the estimation to be robust against faulty measurements and modeling errors. It demonstrates the performance of the algorithm by comparing the results of automated speed estimation to those manually computed previously. All speed estimates from the automated algorithm fall within error bars of ± 30% of the manually computed speeds. In addition, this algorithm is used to populate the current threat space with newly generated threat points. A larger number of velocity estimates helps us to better understand the behavior of ionospheric gradients under geomagnetic storm conditions.

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.

AsteroidZoo: A New Zooniverse project to detect asteroids and improve asteroid detection algorithms

NASA Astrophysics Data System (ADS)

Beasley, M.; Lewicki, C. A.; Smith, A.; Lintott, C.; Christensen, E.

2013-12-01

We present a new citizen science project: AsteroidZoo. A collaboration between Planetary Resources, Inc., the Zooniverse Team, and the Catalina Sky Survey, we will bring the science of asteroid identification to the citizen scientist. Volunteer astronomers have proved to be a critical asset in identification and characterization of asteroids, especially potentially hazardous objects. These contributions, to date, have required that the volunteer possess a moderate telescope and the ability and willingness to be responsive to observing requests. Our new project will use data collected by the Catalina Sky Survey (CSS), currently the most productive asteroid survey, to be used by anyone with sufficient interest and an internet connection. As previous work by the Zooniverse has demonstrated, the capability of the citizen scientist is superb at classification of objects. Even the best automated searches require human intervention to identify new objects. These searches are optimized to reduce false positive rates and to prevent a single operator from being overloaded with requests. With access to the large number of people in Zooniverse, we will be able to avoid that problem and instead work to produce a complete detection list. Each frame from CSS will be searched in detail, generating a large number of new detections. We will be able to evaluate the completeness of the CSS data set and potentially provide improvements to the automated pipeline. The data corpus produced by AsteroidZoo will be used as a training environment for machine learning challenges in the future. Our goals include a more complete asteroid detection algorithm and a minimum computation program that skims the cream of the data suitable for implemention on small spacecraft. Our goal is to have the site become live in the Fall 2013.

Selecting Power-Efficient Signal Features for a Low-Power Fall Detector.

PubMed

Wang, Changhong; Redmond, Stephen J; Lu, Wei; Stevens, Michael C; Lord, Stephen R; Lovell, Nigel H

2017-11-01

Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.

[Automated external defibrillators, life vest defibrillator, or both?].

PubMed

Conti, C Richard

2012-03-01

As most understand, survival of cardiac arrest victims falls significantly if cardioversion is not performed promptly. The standard of practice for out-of-hospital defibrillation is the implantable cardiac defibrillator; however, much has been written and discussed about the use of automated external defibrillators. Not as much has been written about life vest wearable defibrillators. How to use these devices will be reviewed in this article.

Highly Portable, Sensor-Based System for Human Fall Monitoring.

PubMed

Mao, Aihua; Ma, Xuedong; He, Yinan; Luo, Jie

2017-09-13

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user's body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.

Highly Portable, Sensor-Based System for Human Fall Monitoring

PubMed Central

Mao, Aihua; Ma, Xuedong; He, Yinan; Luo, Jie

2017-01-01

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system. PMID:28902149

Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

PubMed Central

Habib, Mohammad Ashfak; Mohktar, Mas S.; Kamaruzzaman, Shahrul Bahyah; Lim, Kheng Seang; Pin, Tan Maw; Ibrahim, Fatimah

2014-01-01

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems. PMID:24759116

Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.

PubMed

Rovira, Ericka; Parasuraman, Raja

2010-06-01

This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation. Research has shown that the type of automation imperfection leads to differential human performance costs. Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence. ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance. The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

PubMed

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

Retrieval-travel-time model for free-fall-flow-rack automated storage and retrieval system

NASA Astrophysics Data System (ADS)

Metahri, Dhiyaeddine; Hachemi, Khalid

2018-03-01

Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturing and distribution centers. The modelling of the retrieval-travel time of an AS/RS (expected product delivery time) is practically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RS has emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses an operator or a single machine for storage operations, and uses a combination between the free-fall movement and a transport conveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expected retrieval-travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which is based on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results show that the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracy of our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess the system throughput, and evaluate different storage policies.

Effects of a Longer Detection Window in VHF Time-of-Arrival Lightning Detection Systems

NASA Astrophysics Data System (ADS)

Murphy, M.; Holle, R.; Demetriades, N.

2003-12-01

Lightning detection systems that operate by measuring the times of arrival (TOA) of short bursts of radiation at VHF can produce huge volumes of data. The first automated system of this kind, the NASA Kennedy Space Center LDAR network, is capable of producing one detection every 100 usec from each of seven sensors (Lennon and Maier, 1991), where each detection consists of the time and amplitude of the highest-amplitude peak observed within the 100 usec window. More modern systems have been shown to produce very detailed information with one detection every 10 usec (Rison et al., 2001). Operating such systems in real time, however, can become expensive because of the large data communications rates required. One solution to this problem is to use a longer detection window, say 500 usec. In principle, this has little or no effect on the flash detection efficiency because each flash typically produces a very large number of these VHF bursts (known as sources). By simply taking the largest-amplitude peak from every 500-usec interval instead of every 100-usec interval, we should detect the largest 20{%} of the sources that would have been detected using the 100-usec window. However, questions remain about the exact effect of a longer detection window on the source detection efficiency with distance from the network, its effects on how well flashes are represented in space, and how well the reduced information represents the parent thunderstorm. The latter issue is relevant for automated location and tracking of thunderstorm cells using data from VHF TOA lightning detection networks, as well as for understanding relationships between lightning and severe weather. References Lennon, C.L. and L.M. Maier, Lightning mapping system. Proceedings, Intl. Aerospace and Ground Conf. on Lightning and Static Elec., Cocoa Beach, Fla., NASA Conf. Pub. 3106, vol. II, pp. 89-1 - 89-10, 1991. Rison, W., P. Krehbiel, R. Thomas, T. Hamlin, J. Harlin, High time resolution lightning mapping observations of a small thunderstorm during STEPS. Eos Trans. AGU, 82 (47), Fall Meet. Suppl., Abstract AE12A-83, 2001.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection.

PubMed

Hättenschwiler, Nicole; Sterchi, Yanik; Mendes, Marcia; Schwaninger, Adrian

2018-10-01

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Algorithm for Identifying Erroneous Rain-Gauge Readings

NASA Technical Reports Server (NTRS)

Rickman, Doug

2005-01-01

An algorithm analyzes rain-gauge data to identify statistical outliers that could be deemed to be erroneous readings. Heretofore, analyses of this type have been performed in burdensome manual procedures that have involved subjective judgements. Sometimes, the analyses have included computational assistance for detecting values falling outside of arbitrary limits. The analyses have been performed without statistically valid knowledge of the spatial and temporal variations of precipitation within rain events. In contrast, the present algorithm makes it possible to automate such an analysis, makes the analysis objective, takes account of the spatial distribution of rain gauges in conjunction with the statistical nature of spatial variations in rainfall readings, and minimizes the use of arbitrary criteria. The algorithm implements an iterative process that involves nonparametric statistics.

Fast-time Simulation of an Automated Conflict Detection and Resolution Concept

NASA Technical Reports Server (NTRS)

Windhorst, Robert; Erzberger, Heinz

2006-01-01

This paper investigates the effect on the National Airspace System of reducing air traffc controller workload by automating conflict detection and resolution. The Airspace Concept Evaluation System is used to perform simulations of the Cleveland Center with conventional and with automated conflict detection and resolution concepts. Results show that the automated conflict detection and resolution concept significantly decreases growth of delay as traffic demand is increased in en-route airspace.

Testing of a long-term fall detection system incorporated into a custom vest for the elderly.

PubMed

Bourke, Alan K; van de Ven, Pepijn W J; Chaya, Amy E; OLaighin, Gearóid M; Nelson, John

2008-01-01

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day. The system previously achieved sensitivity of >90% and a specificity of >99%, using young healthy subjects performing falls and normal activities of daily living (ADL). In this study, over 833 hours of monitoring was performed over the course of the four weeks from the elderly subjects, during normal daily activity. In this time no actual falls were recorded, however the system registered a total of the 42 fall-alerts however only 9 were received at the care taker site. A fall detection system incorporated into a custom designed garment has been developed which will help reduce the incidence of the long-lie, when falls occur in the elderly population. However further development is required to reduce the number of false-positives and improve the transmission of messages.

Fall prevention walker during rehabilitation

NASA Astrophysics Data System (ADS)

Tee, Kian Sek; E, Chun Zhi; Saim, Hashim; Zakaria, Wan Nurshazwani Wan; Khialdin, Safinaz Binti Mohd; Isa, Hazlita; Awad, M. I.; Soon, Chin Fhong

2017-09-01

This paper proposes on the design of a walker for the prevention of falling among elderlies or patients during rehabilitation whenever they use a walker to assist them. Fall happens due to impaired balance or gait problem. The assistive device is designed by applying stability concept and an accelerometric fall detection system is included. The accelerometric fall detection system acts as an alerting device that acquires body accelerometric data and detect fall. Recorded accelerometric data could be useful for further assessment. Structural strength of the walker was verified via iterations of simulation using finite element analysis, before being fabricated. Experiments were conducted to identify the fall patterns using accelerometric data. The design process and detection of fall pattern demonstrates the design of a walker that could support the user without fail and alerts the helper, thus salvaging the users from injuries due to fall and unattended situation.

Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

PubMed

Li, Yun; Ho, K C; Popescu, Mihail

2014-03-01

Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects.

PubMed

Kangas, M; Vikman, I; Nyberg, L; Korpelainen, R; Lindblom, J; Jämsä, T

2012-03-01

Falling is a common accident among older people. Automatic fall detectors are one method of improving security. However, in most cases, fall detectors are designed and tested with data from experimental falls in younger people. This study is one of the first to provide fall-related acceleration data obtained from real-life falls. Wireless sensors were used to collect acceleration data during a six-month test period in older people. Data from five events representing forward falls, a sideways fall, a backwards fall, and a fall out of bed were collected and compared with experimental falls performed by middle-aged test subjects. The signals from real-life falls had similar features to those from intentional falls. Real-life forward, sideways and backward falls all showed a pre impact phase and an impact phase that were in keeping with the model that was based on experimental falls. In addition, the fall out of bed had a similar acceleration profile as the experimental falls of the same type. However, there were differences in the parameters that were used for the detection of the fall phases. The beginning of the fall was detected in all of the real-life falls starting from a standing posture, whereas the high pre impact velocity was not. In some real-life falls, multiple impacts suggested protective actions. In conclusion, this study demonstrated similarities between real-life falls of older people and experimental falls of middle-aged subjects. However, some fall characteristics detected from experimental falls were not detectable in acceleration signals from corresponding heterogeneous real-life falls. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparison and characterization of Android-based fall detection systems.

PubMed

Luque, Rafael; Casilari, Eduardo; Morón, María-José; Redondo, Gema

2014-10-08

Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.

Comparison and Characterization of Android-Based Fall Detection Systems

PubMed Central

Luque, Rafael; Casilari, Eduardo; Morón, María-José; Redondo, Gema

2014-01-01

Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems. PMID:25299953

Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.

PubMed

Casilari, Eduardo; Oviedo-Jiménez, Miguel A

2015-01-01

Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of 'false negatives' or actual falls that remain undetected).

Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch

PubMed Central

Casilari, Eduardo; Oviedo-Jiménez, Miguel A.

2015-01-01

Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient’s movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system’s capability to avoid false alarms or ‘false positives’ (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of ‘false negatives’ or actual falls that remain undetected). PMID:26560737

It is always on your mind: experiences and perceptions of falling of older people and their carers and the potential of a mobile falls detection device.

PubMed

Williams, Veronika; Victor, Christina R; McCrindle, Rachel

2013-01-01

Background. Falls and fear of falling present a major risk to older people as both can affect their quality of life and independence. Mobile assistive technologies (AT) fall detection devices may maximise the potential for older people to live independently for as long as possible within their own homes by facilitating early detection of falls. Aims. To explore the experiences and perceptions of older people and their carers as to the potential of a mobile falls detection AT device. Methods. Nine focus groups with 47 participants including both older people with a range of health conditions and their carers. Interviews were audio recorded, transcribed verbatim, and thematically analysed. Results. Four key themes were identified relating to participants' experiences and perceptions of falling and the potential impact of a mobile falls detector: cause of falling, falling as everyday vulnerability, the environmental context of falling, and regaining confidence and independence by having a mobile falls detector. Conclusion. The perceived benefits of a mobile falls detector may differ between older people and their carers. The experience of falling has to be taken into account when designing mobile assistive technology devices as these may influence perceptions of such devices and how older people utilise them.

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.

Falls event detection using triaxial accelerometry and barometric pressure measurement.

PubMed

Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

2009-01-01

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

PubMed

Despins, Laurel A

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

PubMed

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques

DTIC Science & Technology

2018-01-09

ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological and...is no longer needed. Do not return it to the originator. ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy ...4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques 5a. CONTRACT NUMBER

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.

Challenges, issues and trends in fall detection systems

PubMed Central

2013-01-01

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls. PMID:23829390

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

PubMed

Bourke, Alan K; Klenk, Jochen; Schwickert, Lars; Aminian, Kamiar; Ihlen, Espen A F; Mellone, Sabato; Helbostad, Jorunn L; Chiari, Lorenzo; Becker, Clemens

2016-08-01

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms.

PubMed

Baldewijns, Greet; Debard, Glen; Mertes, Gert; Vanrumste, Bart; Croonenborghs, Tom

2016-03-01

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.

Fat, weather, and date affect migratory songbirds' departure decisions, routes, and time it takes to cross the Gulf of Mexico.

PubMed

Deppe, Jill L; Ward, Michael P; Bolus, Rachel T; Diehl, Robert H; Celis-Murillo, Antonio; Zenzal, Theodore J; Moore, Frank R; Benson, Thomas J; Smolinsky, Jaclyn A; Schofield, Lynn N; Enstrom, David A; Paxton, Eben H; Bohrer, Gil; Beveroth, Tara A; Raim, Arlo; Obringer, Renee L; Delaney, David; Cochran, William W

2015-11-17

Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson's Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf.

Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico

USGS Publications Warehouse

Deppe, Jill L.; Ward, Michael P.; Bolus, Rachel T.; Diehl, Robert H.; Celis-Murillo, A.; Zenzal, Theodore J.; Moore, Frank R.; Benson, Thomas J.; Smolinsky, Jaclyn A.; Schofield, Lynn N.; Enstrom, David A.; Paxton, Eben H.; Bohrer, Gil; Beveroth, Tara A.; Raim, Arlo; Obringer, Renee L.; Delaney, David; Cochran, William W.

2015-01-01

Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson’s Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf.

Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico

PubMed Central

Deppe, Jill L.; Ward, Michael P.; Bolus, Rachel T.; Diehl, Robert H.; Celis-Murillo, Antonio; Zenzal, Theodore J.; Moore, Frank R.; Benson, Thomas J.; Smolinsky, Jaclyn A.; Schofield, Lynn N.; Enstrom, David A.; Paxton, Eben H.; Bohrer, Gil; Beveroth, Tara A.; Raim, Arlo; Obringer, Renee L.; Delaney, David; Cochran, William W.

2015-01-01

Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson’s Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf. PMID:26578793

A study of using smartphone to detect and identify construction workers' near-miss falls based on ANN

NASA Astrophysics Data System (ADS)

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2018-03-01

As an effective fall accident preventive method, insight into near-miss falls provides an efficient solution to find out the causes of fall accidents, classify the type of near-miss falls and control the potential hazards. In this context, the paper proposes a method to detect and identify near-miss falls that occur when a worker walks in a workplace based on artificial neural network (ANN). The energy variation generated by workers who meet with near-miss falls is measured by sensors embedded in smart phone. Two experiments were designed to train the algorithm to identify various types of near-miss falls and test the recognition accuracy, respectively. At last, a test was conducted by workers wearing smart phones as they walked around a simulated construction workplace. The motion data was collected, processed and inputted to the trained ANN to detect and identify near-miss falls. Thresholds were obtained to measure the relationship between near-miss falls and fall accidents in a quantitate way. This approach, which integrates smart phone and ANN, will help detect near-miss fall events, identify hazardous elements and vulnerable workers, providing opportunities to eliminate dangerous conditions in a construction site or to alert possible victims that need to change their behavior before the occurrence of a fall accident.

Fall detection in homes of older adults using the Microsoft Kinect.

PubMed

Stone, Erik E; Skubic, Marjorie

2015-01-01

A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time.

PubMed

Greenlee, Eric T; DeLucia, Patricia R; Newton, David C

2018-06-01

The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Participants "drove" a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Fall migratory departure decisions and routes of blackpoll warblers Setophaga striata and red-eyed vireos Vireo olivaceus at a coastal barrier in the Gulf of Maine

Treesearch

Jennifer R. Smetzer; David I. King; Philip D. Taylor

2017-01-01

Each year, millions of songbirds concentrate in coastal areas during fall migration. The choices birds make at the coast about stopover habitat use and migratory route can influence both the success of their migratory journey and fitness in subsequent life stages. We made use of a regional-scale automated radio telemetry array to study stopover and migratory flights...

Analysis of Android Device-Based Solutions for Fall Detection

PubMed Central

Casilari, Eduardo; Luque, Rafael; Morón, María-José

2015-01-01

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions. PMID:26213928

Analysis of Android Device-Based Solutions for Fall Detection.

PubMed

Casilari, Eduardo; Luque, Rafael; Morón, María-José

2015-07-23

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

Can we make a carpet smart enough to detect falls?

PubMed

Muheidat, Fadi; Tyrer, Harry W

2016-08-01

In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or falling on the carpet. The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data obtained allowed examining data frames (a frame is a single scan of the carpet sensors) read from the data acquisition system. We used different algorithms and techniques, and varied the windows size of number of frames (WS ≥ 1) and threshold (TH) to build our data set, which later used machine learning to help decide a fall or no fall. We then used the dataset obtained from applying a set of fall detection algorithms and the video recorded for the fall pattern experiments to train a set of classifiers using multiple test options using the Weka framework. We measured the sensitivity and specificity of the system and other metrics for intelligent detection of falls. Results showed that Computational Intelligence techniques detect falls with 96.2% accuracy and 81% sensitivity and 97.8% specificity. In addition to fall detection, we developed a database system and web applications to retain these data for years. We can display this data in realtime and for all activities in the carpet for extensive data analysis any time in the future.

ODOT research news : fall quarter 2001.

DOT National Transportation Integrated Search

2001-01-01

The ODOT Research News including: 1) Research Management Peer Exchange. Six research experts from other agencies, other states, and FHWA interviewed ODOT staff and our research partners to collect their views of the program. 2) Automated Data Collect...

Flagging threshold optimization for manual blood smear review in primary care laboratory.

PubMed

Bihl, Pierre-Adrien

2018-04-01

Manual blood smear review is required when an anomaly detected by the automated hematologic analyzer triggers a flag. Our will through this study is to optimize these flagging thresholds for manual slide review in order to limit workload, while insuring clinical care through no extra false-negative. Flagging causes of 4,373 samples were investigated by manual slide review, after having been run on ADVIA 2120i. A set of 6 user-adjustments is proposed. By implementing all recommendations that we made, false-positive rate falls from 81.8% to 58.6%, while PPV increases from 18.2% to 23.7%. Hence, use of such optimized thresholds enables us to maximize efficiency without altering clinical care, but each laboratory should establish its own criteria to take into consideration local distinctive features.

Fall detection of elderly through floor vibrations and sound.

PubMed

Litvak, Dima; Zigel, Yaniv; Gannot, Israel

2008-01-01

Falls are very prevalent among the elderly especially in their home. The statistics show that approximately one in every three adults 65 years old or older falls each year. Almost 30% of those falls result in serious injuries. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Therefore, reliable and immediate fall detection system is important so that adequate medical support could be delivered. We have developed a unique and inexpensive solution that does not require subjects to wear anything. The solution is based on floor vibration and acoustic sensing, and uses a pattern recognition algorithm to discriminate between human or inanimate object fall events. Using the proposed system we can detect human falls with a sensitivity of 95% and specificity of 95%.

Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly.

PubMed

Hwang, J Y; Kang, J M; Jang, Y W; Kim, H

2004-01-01

Novel algorithm and real-time ambulatory monitoring system for fall detection in elderly people is described. Our system is comprised of accelerometer, tilt sensor and gyroscope. For real-time monitoring, we used Bluetooth. Accelerometer measures kinetic force, tilt sensor and gyroscope estimates body posture. Also, we suggested algorithm using signals which obtained from the system attached to the chest for fall detection. To evaluate our system and algorithm, we experimented on three people aged over 26 years. The experiment of four cases such as forward fall, backward fall, side fall and sit-stand was repeated ten times and the experiment in daily life activity was performed one time to each subject. These experiments showed that our system and algorithm could distinguish between falling and daily life activity. Moreover, the accuracy of fall detection is 96.7%. Our system is especially adapted for long-time and real-time ambulatory monitoring of elderly people in emergency situation.

A comparison of public datasets for acceleration-based fall detection.

PubMed

Igual, Raul; Medrano, Carlos; Plaza, Inmaculada

2015-09-01

Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

A posture recognition based fall detection system for monitoring an elderly person in a smart home environment.

PubMed

Yu, Miao; Rhuma, Adel; Naqvi, Syed Mohsen; Wang, Liang; Chambers, Jonathon

2012-11-01

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

PubMed

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

2017-01-01

Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

Home Camera-Based Fall Detection System for the Elderly.

PubMed

de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

2017-12-09

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Home Camera-Based Fall Detection System for the Elderly

PubMed Central

de Miguel, Koldo

2017-01-01

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%. PMID:29232846

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

PubMed

Campillo-Gimenez, Boris; Garcelon, Nicolas; Jarno, Pascal; Chapplain, Jean Marc; Cuggia, Marc

2013-01-01

The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

PubMed Central

Chira, Camelia; González, Víctor M.; de la Cal, Enrique

2018-01-01

Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. PMID:29701721

Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

PubMed

Goatman, Keith; Charnley, Amanda; Webster, Laura; Nussey, Stephen

2011-01-01

To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography. Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone. Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%). Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service.

The state of knowledge on technologies and their use for fall detection: A scoping review.

PubMed

Lapierre, N; Neubauer, N; Miguel-Cruz, A; Rios Rincon, A; Liu, L; Rousseau, J

2018-03-01

Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. To examine the extent and the diversity of current technologies for fall detection in older adults. A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults. Copyright © 2017 Elsevier B.V. All rights reserved.

Geohazard reconnaissance mapping for potential rock boulder fall using low altitude UAV photogrammetry

NASA Astrophysics Data System (ADS)

Sharan Kumar, N.; Ashraf Mohamad Ismail, Mohd; Sukor, Nur Sabahiah Abdul; Cheang, William

2018-05-01

This paper discusses potential applications of unmanned aerial vehicles (UAVs) for evaluation of risk immediately with photos and 3-dimensional digital element. Aerial photography using UAV ready to give a powerful technique for potential rock boulder fall recognition. High-resolution outputs from this method give the chance to evaluate the site for potential rock boulder falls spatially. The utilization of UAV to capture the aerial photos is a quick, reliable, and cost-effective technique contrasted with terrestrial laser scanning method. Reconnaissance of potential rock boulder susceptible to fall is very crucial during the geotechnical investigation. This process is essential in the view of the rock fall hazards nearby site before the beginning of any preliminary work. Photogrammetric applications have empowered the automated way to deal with identification of rock boulder susceptible to fall by recognizing the location, size, and position. A developing examination of the utilization of digital photogrammetry gives numerous many benefits for civil engineering application. These advancements have made important contributions to our capabilities to create the geohazard map on potential rock boulder fall.

A pilot clinical trial on a Variable Automated Speed and Sensing Treadmill (VASST) for hemiparetic gait rehabilitation in stroke patients.

PubMed

Chua, Karen S G; Chee, Johnny; Wong, Chin J; Lim, Pang H; Lim, Wei S; Hoo, Chuan M; Ong, Wai S; Shen, Mira L; Yu, Wei S

2015-01-01

Impairments in walking speed and capacity are common problems after stroke which may benefit from treadmill training. However, standard treadmills, are unable to adapt to the slower walking speeds of stroke survivors and are unable to automate training progression. This study tests a Variable Automated Speed and Sensing Treadmill (VASST) using a standard clinical protocol. VASST is a semi-automated treadmill with multiple sensors and micro controllers, including wireless control to reposition a fall-prevention harness, variable pre-programmed exercise parameters and laser beam foot sensors positioned on the belt to detect subject's foot positions. An open-label study with assessor blinding was conducted in 10 community-dwelling chronic hemiplegic patients who could ambulate at least 0.1 m/s. Interventions included physiotherapist-supervised training on VASST for 60 min three times per week for 4 weeks (total 12 h). Outcome measures of gait speed, quantity, balance, and adverse events were assessed at baseline, 2, 4, and 8 weeks. Ten subjects (8 males, mean age 55.5 years, 2.1 years post stroke) completed VASST training. Mean 10-m walk test speed was 0.69 m/s (SD = 0.29) and mean 6-min walk test distance was 178.3 m (84.0). After 4 weeks of training, 70% had significant positive gains in gait speed (0.06 m/s, SD = 0.08 m/s, P = 0.037); and 90% improved in walking distance. (54.3 m, SD = 30.9 m, P = 0.005). There were no adverse events. This preliminary study demonstrates the initial feasibility and short-term efficacy of VASST for walking speed and distance for people with chronic post-stroke hemiplegia.

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood.

PubMed Central

Oosterwijk, J C; Knepflé, C F; Mesker, W E; Vrolijk, H; Sloos, W C; Pattenier, H; Ravkin, I; van Ommen, G J; Kanhai, H H; Tanke, H J

1998-01-01

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood. PMID:9837832

Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation.

PubMed

Boyer, Célia; Dolamic, Ljiljana

2015-06-02

To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website's HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the "document" definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed.

Automated Detection of HONcode Website Conformity Compared to Manual Detection: An Evaluation

PubMed Central

2015-01-01

Background To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website’s HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. Objective The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Methods Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. Results For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the “document” definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Conclusions Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed. PMID:26036669

Optimal fall indicators for slip induced falls on a cross-slope.

PubMed

Domone, Sarah; Lawrence, Daniel; Heller, Ben; Hendra, Tim; Mawson, Sue; Wheat, Jonathan

2016-08-01

Slip-induced falls are among the most common cause of major occupational injuries in the UK as well as being a major public health concern in the elderly population. This study aimed to determine the optimal fall indicators for fall detection models which could be used to reduce the detrimental consequences of falls. A total of 264 kinematic variables covering three-dimensional full body model translation and rotational measures were analysed during normal walking, successful recovery from slips and falls on a cross-slope. Large effect sizes were found for three kinematic variables which were able to distinguish falls from normal walking and successful recovery. Further work should consider other types of daily living activities as results show that the optimal kinematic fall indicators can vary considerably between movement types. Practitioner Summary: Fall detection models are used to minimise the adverse consequences of slip-induced falls, a major public health concern. Optimal fall indicators were derived from a comprehensive set of kinematic variables for slips on a cross-slope. Results suggest robust detection of falls is possible on a cross-slope but may be more difficult than level walking.

Fall classification by machine learning using mobile phones.

PubMed

Albert, Mark V; Kording, Konrad; Herrmann, Megan; Jayaraman, Arun

2012-01-01

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.

PubMed

Shawen, Nicholas; Lonini, Luca; Mummidisetty, Chaithanya Krishna; Shparii, Ilona; Albert, Mark V; Kording, Konrad; Jayaraman, Arun

2017-10-11

Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting. Such approaches may provide a limited ability to detect falls in amputees and in real-world scenarios. The aim was to develop a classifier that uses data from able-bodied individuals to detect falls in individuals with a lower limb amputation, while they freely carry the mobile phone in different locations and during free-living. We obtained 861 simulated indoor and outdoor falls from 10 young control (non-amputee) individuals and 6 individuals with a lower limb amputation. In addition, we recorded a broad database of activities of daily living, including data from three participants' free-living routines. Sensor readings (accelerometer and gyroscope) from a mobile phone were recorded as participants freely carried it in three common locations-on the waist, in a pocket, and in the hand. A set of 40 features were computed from the sensors data and four classifiers were trained and combined through stacking to detect falls. We compared the performance of two population-specific models, trained and tested on either able-bodied or amputee participants, with that of a model trained on able-bodied participants and tested on amputees. A simple threshold-based classifier was used to benchmark our machine-learning classifier. The accuracy of fall detection in amputees for a model trained on control individuals (sensitivity: mean 0.989, 1.96*standard error of the mean [SEM] 0.017; specificity: mean 0.968, SEM 0.025) was not statistically different (P=.69) from that of a model trained on the amputee population (sensitivity: mean 0.984, SEM 0.016; specificity: mean 0.965, SEM 0.022). Detection of falls in control individuals yielded similar results (sensitivity: mean 0.979, SEM 0.022; specificity: mean 0.991, SEM 0.012). A mean 2.2 (SD 1.7) false alarms per day were obtained when evaluating the model (vs mean 122.1, SD 166.1 based on thresholds) on data recorded as participants carried the phone during their daily routine for two or more days. Machine-learning classifiers outperformed the threshold-based one (P<.001). A mobile phone-based fall detection model can use data from non-amputee individuals to detect falls in individuals walking with a prosthesis. We successfully detected falls when the mobile phone was carried across multiple locations and without a predetermined orientation. Furthermore, the number of false alarms yielded by the model over a longer period of time was reasonably low. This moves the application of mobile phone-based fall detection systems closer to a real-world use case scenario. ©Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.10.2017.

Understanding reliance on automation: effects of error type, error distribution, age and experience

PubMed Central

Sanchez, Julian; Rogers, Wendy A.; Fisk, Arthur D.; Rovira, Ericka

2015-01-01

An obstacle detection task supported by “imperfect” automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation. PMID:25642142

Understanding reliance on automation: effects of error type, error distribution, age and experience.

PubMed

Sanchez, Julian; Rogers, Wendy A; Fisk, Arthur D; Rovira, Ericka

2014-03-01

An obstacle detection task supported by "imperfect" automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation.

Load-Differential Features for Automated Detection of Fatigue Cracks Using Guided Waves (Preprint)

DTIC Science & Technology

2011-11-01

AFRL-RX-WP-TP-2011-4363 LOAD-DIFFERENTIAL FEATURES FOR AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) Jennifer E...AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) 5a. CONTRACT NUMBER FA8650-09-C-5206 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...tensile loads open fatigue cracks and thus enhance their detectability using ultrasonic methods. Here we introduce a class of load-differential methods

Network-level pavement evaluation of Virginia's interstate system using the falling weight deflectometer.

DOT National Transportation Integrated Search

2008-01-01

The Virginia Department of Transportation (VDOT) currently uses the results of automated surface distress surveys to assist in developing pavement maintenance strategies for its interstate and primary roadways. Totaling nearly 27,000 lane-miles, thes...

Field evaluation of descent advisor trajectory prediction accuracy

DOT National Transportation Integrated Search

1996-07-01

The Descent Advisor (DA) automation tool has undergone a series of field tests : at the Denver Air Route Traffic Control Center to study the feasibility of : DA-based clearances and procedures. The latest evaluation, conducted in the : fall of 1995, ...

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

PubMed Central

Ahmed, Moiz; Mehmood, Nadeem; Mehmood, Amir; Rizwan, Kashif

2017-01-01

Objectives Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. Methods We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. Results To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. Conclusions In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios. PMID:28875049

Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications

PubMed Central

Lee, Young-Sook; Chung, Wan-Young

2012-01-01

Vision-based abnormal event detection for home healthcare systems can be greatly improved using visual sensor-based techniques able to detect, track and recognize objects in the scene. However, in moving object detection and tracking processes, moving cast shadows can be misclassified as part of objects or moving objects. Shadow removal is an essential step for developing video surveillance systems. The goal of the primary is to design novel computer vision techniques that can extract objects more accurately and discriminate between abnormal and normal activities. To improve the accuracy of object detection and tracking, our proposed shadow removal algorithm is employed. Abnormal event detection based on visual sensor by using shape features variation and 3-D trajectory is presented to overcome the low fall detection rate. The experimental results showed that the success rate of detecting abnormal events was 97% with a false positive rate of 2%. Our proposed algorithm can allow distinguishing diverse fall activities such as forward falls, backward falls, and falling asides from normal activities. PMID:22368486

Marine mammal acoustic detections in the northeastern Chukchi Sea, September 2007-July 2011

NASA Astrophysics Data System (ADS)

Hannay, David E.; Delarue, Julien; Mouy, Xavier; Martin, Bruce S.; Leary, Del; Oswald, Julie N.; Vallarta, Jonathan

2013-09-01

Several cetacean and pinniped species use the northeastern Chukchi Sea as seasonal or year-round habitat. This area has experienced pronounced reduction in the extent of summer sea ice over the last decade, as well as increased anthropogenic activity, particularly in the form of oil and gas exploration. The effects of these changes on marine mammal species are presently unknown. Autonomous passive acoustic recorders were deployed over a wide area of the northeastern Chukchi Sea off the coast of Alaska from Cape Lisburne to Barrow, at distances from 8 km to 200 km from shore: up to 44 each summer and up to 8 each winter. Acoustic data were acquired at 16 kHz continuously during summer and on a duty cycle of 40 or 48 min within each 4-h period during winter. Recordings were analyzed manually and using automated detection and classification systems to identify calls. Bowhead (Balaena mysticetus) and beluga (Delphinapterus leucas) whale calls were detected primarily from April through June and from September to December during their migrations between the Bering and Beaufort seas. Summer detections were rare and usually concentrated off Wainwright and Barrow, Alaska. Gray (Eschrichtius robustus) whale calls were detected between July and October, their occurrence decreasing with increasing distance from shore. Fin (Balaenoptera physalus), killer (Orcinus orca), minke (Balaenoptera acutorostrata), and humpback (Megaptera novaeangliae) whales were detected sporadically in summer and early fall. Walrus (Odobenus rosmarus) was the most commonly detected species between June and October, primarily occupying the southern edge of Hanna Shoal and haul-outs near coastal recording stations off Wainwright and Point Lay. Ringed (Pusa hispida) and bearded (Erignathus barbatus) seals occur year-round in the Chukchi Sea. Ringed seal acoustic detections occurred throughout the year but detection numbers were low, likely due to low vocalization rates. Bearded seal acoustic detections peaked in April and May during their breeding season, with much lower detection numbers in July and August, likely as a result of reduced calling rates after breeding season. Ribbon seals (Histriophoca fasciata) were only detected in the fall as they migrated south through the study area toward the Bering Sea. These results suggest a regular presence of marine mammals in the Chukchi Sea year-round, with species-dependent seasonal and spatial density variations.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

USGS Publications Warehouse

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Monitoring of bedridden patients: development of a fall detection tool.

PubMed

Vilas-Boas, M; Silva, P; Cunha, S R; Correia, M V

2013-01-01

Falls of patients are an important issue in hospitals nowadays; it causes severe injuries, increases hospitalization time and treatment costs. The detection of a fall, in time, provides faster rescue to the patient, preventing more serious injuries, as well as saving nursing time. The MovinSense® is an electronic device designed for monitoring patients to prevent pressure sores, and the main goal of this work was to develop a new tool for this device, with the purpose of detecting if the patient has fallen from the hospital bed, without changing any of the device's original features. Experiments for gathering data samples of inertial signals of falling from the bed were obtained using the device. For fall detection a sensitivity of 72% and specificity of 100% were reached. Another algorithm was developed to detect if the patient got out of his/her bed.

An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision

DTIC Science & Technology

2018-01-01

ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision...needed. Do not return it to the originator. ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

[Research and Design of a System for Detecting Automated External Defbrillator Performance Parameters].

PubMed

Wang, Kewu; Xiao, Shengxiang; Jiang, Lina; Hu, Jingkai

2017-09-30

In order to regularly detect the performance parameters of automated external defibrillator (AED), to make sure it is safe before using the instrument, research and design of a system for detecting automated external defibrillator performance parameters. According to the research of the characteristics of its performance parameters, combing the STM32's stability and high speed with PWM modulation control, the system produces a variety of ECG normal and abnormal signals through the digital sampling methods. Completed the design of the hardware and software, formed a prototype. This system can accurate detect automated external defibrillator discharge energy, synchronous defibrillation time, charging time and other key performance parameters.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Chair rise transfer detection and analysis using a pendant sensor: an algorithm for fall risk assessment in older people.

PubMed

Zhang, Wei; Regterschot, G Ruben H; Wahle, Fabian; Geraedts, Hilde; Baldus, Heribert; Zijlstra, Wiebren

2014-01-01

Falls result in substantial disability, morbidity, and mortality among older people. Early detection of fall risks and timely intervention can prevent falls and injuries due to falls. Simple field tests, such as repeated chair rise, are used in clinical assessment of fall risks in older people. Development of on-body sensors introduces potential beneficial alternatives for traditional clinical methods. In this article, we present a pendant sensor based chair rise detection and analysis algorithm for fall risk assessment in older people. The recall and the precision of the transfer detection were 85% and 87% in standard protocol, and 61% and 89% in daily life activities. Estimation errors of chair rise performance indicators: duration, maximum acceleration, peak power and maximum jerk were tested in over 800 transfers. Median estimation error in transfer peak power ranged from 1.9% to 4.6% in various tests. Among all the performance indicators, maximum acceleration had the lowest median estimation error of 0% and duration had the highest median estimation error of 24% over all tests. The developed algorithm might be feasible for continuous fall risk assessment in older people.

Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system.

PubMed

Shieh, Wann-Yun; Huang, Ju-Chin

2012-09-01

For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

The value of automated gel column agglutination technology in the identification of true inherited D blood types in massively transfused patients.

PubMed

Summers, Thomas; Johnson, Viviana V; Stephan, John P; Johnson, Gloria J; Leonard, George

2009-08-01

Massive transfusion of D- trauma patients in the combat setting involves the use of D+ red blood cells (RBCs) or whole blood along with suboptimal pretransfusion test result documentation. This presents challenges to the transfusion service of tertiary care military hospitals who ultimately receive these casualties because initial D typing results may only reflect the transfused RBCs. After patients are stabilized, mixed-field reaction results on D typing indicate the patient's true inherited D phenotype. This case series illustrates the utility of automated gel column agglutination in detecting mixed-field reactions in these patients. The transfusion service test results, including the automated gel column agglutination D typing results, of four massively transfused D- patients transfused D+ RBCs is presented. To test the sensitivity of the automated gel column agglutination method in detecting mixed-field agglutination reactions, a comparative analysis of three automated technologies using predetermined mixtures of D+ and D- RBCs is also presented. The automated gel column agglutination method detected mixed-field agglutination in D typing in all four patients and in the three prepared control specimens. The automated microwell tube method identified one of the three prepared control specimens as indeterminate, which was subsequently manually confirmed as a mixed-field reaction. The automated solid-phase method was unable to detect any mixed fields. The automated gel column agglutination method provides a sensitive means for detecting mixed-field agglutination reactions in the determination of the true inherited D phenotype of combat casualties transfused massive amounts of D+ RBCs.

Wearable technology and ECG processing for fall risk assessment, prevention and detection.

PubMed

Melillo, Paolo; Castaldo, Rossana; Sannino, Giovanna; Orrico, Ada; de Pietro, Giuseppe; Pecchia, Leandro

2015-01-01

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

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.

In-home fall risk assessment and detection sensor system.

PubMed

Rantz, Marilyn J; Skubic, Marjorie; Abbott, Carmen; Galambos, Colleen; Pak, Youngju; Ho, Dominic K C; Stone, Erik E; Rui, Liyang; Back, Jessica; Miller, Steven J

2013-07-01

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported. Copyright 2013, SLACK Incorporated.

MEMS-based sensing and algorithm development for fall detection and gait analysis

NASA Astrophysics Data System (ADS)

Gupta, Piyush; Ramirez, Gabriel; Lie, Donald Y. C.; Dallas, Tim; Banister, Ron E.; Dentino, Andrew

2010-02-01

Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

PubMed

Putra, I Putu Edy Suardiyana; Brusey, James; Gaura, Elena; Vesilo, Rein

2017-12-22

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k -nearest neighbor ( k -NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

Doppler radar fall activity detection using the wavelet transform.

PubMed

Su, Bo Yu; Ho, K C; Rantz, Marilyn J; Skubic, Marjorie

2015-03-01

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

Automated System for Early Breast Cancer Detection in Mammograms

NASA Technical Reports Server (NTRS)

Bankman, Isaac N.; Kim, Dong W.; Christens-Barry, William A.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

1993-01-01

The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed.

The effect of JPEG compression on automated detection of microaneurysms in retinal images

NASA Astrophysics Data System (ADS)

Cree, M. J.; Jelinek, H. F.

2008-02-01

As JPEG compression at source is ubiquitous in retinal imaging, and the block artefacts introduced are known to be of similar size to microaneurysms (an important indicator of diabetic retinopathy) it is prudent to evaluate the effect of JPEG compression on automated detection of retinal pathology. Retinal images were acquired at high quality and then compressed to various lower qualities. An automated microaneurysm detector was run on the retinal images of various qualities of JPEG compression and the ability to predict the presence of diabetic retinopathy based on the detected presence of microaneurysms was evaluated with receiver operating characteristic (ROC) methodology. The negative effect of JPEG compression on automated detection was observed even at levels of compression sometimes used in retinal eye-screening programmes and these may have important clinical implications for deciding on acceptable levels of compression for a fully automated eye-screening programme.

A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

PubMed Central

Cates, Benjamin; Sim, Taeyong; Heo, Hyun Mu; Kim, Bori; Kim, Hyunggun; Mun, Joung Hwan

2018-01-01

In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe. PMID:29673165

Automatic fall detection using wearable biomedical signal measurement terminal.

PubMed

Nguyen, Thuy-Trang; Cho, Myeong-Chan; Lee, Tae-Soo

2009-01-01

In our study, we developed a mobile waist-mounted device which can monitor the subject's acceleration signal and detect the fall events in real-time with high accuracy and automatically send an emergency message to a remote server via CDMA module. When fall event happens, the system also generates an alarm sound at 50Hz to alarm other people until a subject can sit up or stand up. A Kionix KXM52-1050 tri-axial accelerometer and a Bellwave BSM856 CDMA standalone modem were used to detect and manage fall events. We used not only a simple threshold algorithm but also some supporting methods to increase an accuracy of our system (nearly 100% in laboratory environment). Timely fall detection can prevent regrettable death due to long-lie effect; therefore increase the independence of elderly people in an unsupervised living environment.

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

PubMed

Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, And Moongu

2018-06-12

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall ( with 2 classes and 3 classes ) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

A vision-based fall detection algorithm of human in indoor environment

NASA Astrophysics Data System (ADS)

Liu, Hao; Guo, Yongcai

2017-02-01

Elderly care becomes more and more prominent in China as the population is aging fast and the number of aging population is large. Falls, as one of the biggest challenges in elderly guardianship system, have a serious impact on both physical health and mental health of the aged. Based on feature descriptors, such as aspect ratio of human silhouette, velocity of mass center, moving distance of head and angle of the ultimate posture, a novel vision-based fall detection method was proposed in this paper. A fast median method of background modeling with three frames was also suggested. Compared with the conventional bounding box and ellipse method, the novel fall detection technique is not only applicable for recognizing the fall behaviors end of lying down but also suitable for detecting the fall behaviors end of kneeling down and sitting down. In addition, numerous experiment results showed that the method had a good performance in recognition accuracy on the premise of not adding the cost of time.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

PubMed

Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

2018-06-05

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Case-control. San Francisco Mammography Registry and Mayo Clinic. 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. National Cancer Institute.

Fully Automated Sunspot Detection and Classification Using SDO HMI Imagery in MATLAB

DTIC Science & Technology

2014-03-27

FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Gordon M. Spahr, Second Lieutenant, USAF AFIT-ENP-14-M-34...CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air...DISTRIUBUTION UNLIMITED. AFIT-ENP-14-M-34 FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB Gordon M. Spahr, BS Second

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

PubMed

Rajagopalan, Ramesh; Litvan, Irene; Jung, Tzyy-Ping

2017-11-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Statistical data mining of streaming motion data for fall detection in assistive environments.

PubMed

Tasoulis, S K; Doukas, C N; Maglogiannis, I; Plagianakos, V P

2011-01-01

The analysis of human motion data is interesting for the purpose of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio or video sensors on the monitored subject (wearable sensors) or the surrounding environment. The output of such sensors is data streams that require real time recognition, especially in emergency situations, thus traditional classification approaches may not be applicable for immediate alarm triggering or fall prevention. This paper presents a statistical mining methodology that may be used for the specific problem of real time fall detection. Visual data captured from the user's environment, using overhead cameras along with motion data are collected from accelerometers on the subject's body and are fed to the fall detection system. The paper includes the details of the stream data mining methodology incorporated in the system along with an initial evaluation of the achieved accuracy in detecting falls.

Performance of the Spot Vision Screener in Children Younger Than 3 Years of Age.

PubMed

Forcina, Blake D; Peterseim, M Millicent; Wilson, M Edward; Cheeseman, Edward W; Feldman, Samuel; Marzolf, Amanda L; Wolf, Bethany J; Trivedi, Rupal H

2017-06-01

To evaluate the use of the Spot Vision Screener (Spot; Welch Allyn, Skaneateles Falls, New York, USA) for detection of amblyopia risk factors in children aged 6 months to 3 years, as defined by the 2013 guidelines of the American Association for Pediatric Ophthalmology and Strabismus. Reliability analysis. In this study, children seen from June 1, 2012, to April 30, 2016 were tested with the Spot during a routine visit. Enrolled children underwent a comprehensive eye examination including cycloplegic refraction and sensorimotor testing within 6 months of the testing date by a pediatric ophthalmologist masked to the Spot results. A total of 184 children were included. The Spot successfully obtained readings in 89.7% of patients. Compared with the ophthalmologist's examination, the Spot had an overall sensitivity of 89.8% and a specificity of 70.4%. The Spot achieved good sensitivity and specificity for detection of amblyopia risk factors in this young cohort, particularly in the older subgroup. Our data offer support for automated vision screening in young children. Copyright © 2017 Elsevier Inc. All rights reserved.

The inescapable smart impact detection system (ISIS): An ubiquitous and personalized fall detector based on a distributed 'divide and conquer strategy'.

PubMed

Prado-Velasco, Manuel; del Rio-Cidoncha, Maria Gloria; Ortiz-Marin, Rafael

2008-01-01

Despite the intense research in the last decade with the aim of developing a reliable solution for fall detection in the elderly and other risk populations, it can be asserted that the diffusion of fall detectors in the geriatric practice is near null. This scenario is similar to the very scarce use of telemedicine in healthcare. The present work begins analyzing why fall detectors have not achieved to permeate the industry. That road is used to know the drawbacks of current devices and systems, besides to allow studying several important concepts underlying the principles of fall detection. A novel smart detection system based on that survey is finally briefly presented. The design of this device is founded on the experience and results obtained by an earlier device that was designed in the framework of the thesis of one of the authors.

[Automated analyzer of enzyme immunoassay].

PubMed

Osawa, S

1995-09-01

Automated analyzers for enzyme immunoassay can be classified by several points of view: the kind of labeled antibodies or enzymes, detection methods, the number of tests per unit time, analytical time and speed per run. In practice, it is important for us consider the several points such as detection limits, the number of tests per unit time, analytical range, and precision. Most of the automated analyzers on the market can randomly access and measure samples. I will describe the recent advance of automated analyzers reviewing their labeling antibodies and enzymes, the detection methods, the number of test per unit time and analytical time and speed per test.

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs.

PubMed

Matthews, Stephen G; Miller, Amy L; Clapp, James; Plötz, Thomas; Kyriazakis, Ilias

2016-11-01

Early detection of health and welfare compromises in commercial piggeries is essential for timely intervention to enhance treatment success, reduce impact on welfare, and promote sustainable pig production. Behavioural changes that precede or accompany subclinical and clinical signs may have diagnostic value. Often referred to as sickness behaviour, this encompasses changes in feeding, drinking, and elimination behaviours, social behaviours, and locomotion and posture. Such subtle changes in behaviour are not easy to quantify and require lengthy observation input by staff, which is impractical on a commercial scale. Automated early-warning systems may provide an alternative by objectively measuring behaviour with sensors to automatically monitor and detect behavioural changes. This paper aims to: (1) review the quantifiable changes in behaviours with potential diagnostic value; (2) subsequently identify available sensors for measuring behaviours; and (3) describe the progress towards automating monitoring and detection, which may allow such behavioural changes to be captured, measured, and interpreted and thus lead to automation in commercial, housed piggeries. Multiple sensor modalities are available for automatic measurement and monitoring of behaviour, which require humans to actively identify behavioural changes. This has been demonstrated for the detection of small deviations in diurnal drinking, deviations in feeding behaviour, monitoring coughs and vocalisation, and monitoring thermal comfort, but not social behaviour. However, current progress is in the early stages of developing fully automated detection systems that do not require humans to identify behavioural changes; e.g., through automated alerts sent to mobile phones. Challenges for achieving automation are multifaceted and trade-offs are considered between health, welfare, and costs, between analysis of individuals and groups, and between generic and compromise-specific behaviours. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Towards a social and context-aware multi-sensor fall detection and risk assessment platform.

PubMed

De Backere, F; Ongenae, F; Van den Abeele, F; Nelis, J; Bonte, P; Clement, E; Philpott, M; Hoebeke, J; Verstichel, S; Ackaert, A; De Turck, F

2015-09-01

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms. In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task. The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Automated detection of retinal landmarks for the identification of clinically relevant regions in fundus photography

NASA Astrophysics Data System (ADS)

Ometto, Giovanni; Calivá, Francesco; Al-Diri, Bashir; Bek, Toke; Hunter, Andrew

2016-03-01

Automatic, quick and reliable identification of retinal landmarks from fundus photography is key for measurements used in research, diagnosis, screening and treating of common diseases affecting the eyes. This study presents a fast method for the detection of the centre of mass of the vascular arcades, optic nerve head (ONH) and fovea, used in the definition of five clinically relevant areas in use for screening programmes for diabetic retinopathy (DR). Thirty-eight fundus photographs showing 7203 DR lesions were analysed to find the landmarks manually by two retina-experts and automatically by the proposed method. The automatic identification of the ONH and fovea were performed using template matching based on normalised cross correlation. The centre of mass of the arcades was obtained by fitting an ellipse on sample coordinates of the main vessels. The coordinates were obtained by processing the image with hessian filtering followed by shape analyses and finally sampling the results. The regions obtained manually and automatically were used to count the retinal lesions falling within, and to evaluate the method. 92.7% of the lesions were falling within the same regions based on the landmarks selected by the two experts. 91.7% and 89.0% were counted in the same areas identified by the method and the first and second expert respectively. The inter-repeatability of the proposed method and the experts is comparable, while the 100% intra-repeatability makes the algorithm a valuable tool in tasks like analyses in real-time, of large datasets and of intra-patient variability.

A novel wearable smart button system for fall detection

NASA Astrophysics Data System (ADS)

Zhuang, Wei; Sun, Xiang; Zhi, Yueyan; Han, Yue; Mao, Hande

2017-05-01

Fall has been the second most cause of accidental injury to death in the world. It has been a serious threat to the physical and mental health of the elders. Therefore, developing wearable node system with fall detecting ability has become increasingly pressing at present. A novel smart button for long-term fall detection is proposed in this paper, which is able to accurately monitor the falling behavior, and sending warning message online as well. The smart button is based on the tri-axis acceleration sensor which is used to collect the body motion signals. By using the statistical metrics of acceleration characteristics, a new SVM classification algorithm with high positive accuracy and stability is proposed so as to classify the falls and activities of daily living, and the results can be real-time displayed on Android based mobile phone. The experiments show that our wearable node system can continuously monitor the falling behavior with positive rate 94.8%.

Real-time monitoring system for elderly people in detecting falling movement using accelerometer and gyroscope

NASA Astrophysics Data System (ADS)

Siregar, B.; Andayani, U.; Bahri, R. P.; Seniman; Fahmi, F.

2018-03-01

Most of the elderly people is experiencing a decrease in physical quality, especially the weakness in the legs. This will cause elderly easy to fall and can have a serious impact on their health if not getting help very quickly. It is, therefore, necessary to take immediate action against the falling cases experienced by the elderly. One such action is by developing supervision and detecting of falling movements in real-time, which is then the connection to a member of the family. In this research, we used Arduino Uno as a microcontroller, sensor accelerometer, and gyroscope that serves to measure falling movement of the elderly person and supported by GPS technology Ublox Neo 6M to provide information about coordinates. The result was the high accuracy of delivering notification data to server and accuracy of data delivery to family notification equal to 93,75%. The system successfully detects the direction of falling: forward, backward, left or right and able to distinguish between unintentional falling and conscious falling like a bow or prostrate position.

Review of fall detection techniques: A data availability perspective.

PubMed

Khan, Shehroz S; Hoey, Jesse

2017-01-01

A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. We conclude our paper by discussing several open research problems in the field and pointers for future research. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Effects of Automation Types on Air Traffic Controller Situation Awareness and Performance

NASA Technical Reports Server (NTRS)

Sethumadhavan, A.

2009-01-01

The Joint Planning and Development Office has proposed the introduction of automated systems to help air traffic controllers handle the increasing volume of air traffic in the next two decades (JPDO, 2007). Because fully automated systems leave operators out of the decision-making loop (e.g., Billings, 1991), it is important to determine the right level and type of automation that will keep air traffic controllers in the loop. This study examined the differences in the situation awareness (SA) and collision detection performance of individuals when they worked with information acquisition, information analysis, decision and action selection and action implementation automation to control air traffic (Parasuraman, Sheridan, & Wickens, 2000). When the automation was unreliable, the time taken to detect an upcoming collision was significantly longer for all the automation types compared with the information acquisition automation. This poor performance following automation failure was mediated by SA, with lower SA yielding poor performance. Thus, the costs associated with automation failure are greater when automation is applied to higher order stages of information processing. Results have practical implications for automation design and development of SA training programs.

Final Technical Report

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

de Szoeke, Simon P.

The investigator and DOE-supported student [1] retrieved vertical air velocity and microphysical fall velocity retrieval for VOCALS and CAP-MBL homogeneous clouds. [2] Calculated in-cloud and cloud top dissipation calculation and diurnal cycle computed for VOCALS. [3] Compared CAP-MBL Doppler cloud radar scenes with (Remillard et al. 2012) automated classification.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

PubMed Central

Rajagopalan, Ramesh; Jung, Tzyy-Ping

2017-01-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems. PMID:29104256

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera.

PubMed

Schmidt, Jürgen; Laarousi, Rihab; Stolzmann, Wolfgang; Karrer-Gauß, Katja

2018-06-01

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

Automation of Classical QEEG Trending Methods for Early Detection of Delayed Cerebral Ischemia: More Work to Do.

PubMed

Wickering, Ellis; Gaspard, Nicolas; Zafar, Sahar; Moura, Valdery J; Biswal, Siddharth; Bechek, Sophia; OʼConnor, Kathryn; Rosenthal, Eric S; Westover, M Brandon

2016-06-01

The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

NASA Technical Reports Server (NTRS)

Kennedy, Kellie D.; Stephens, Chad L.; Williams, Ralph A.; Schutte, Paul C.

2017-01-01

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This paper explored the relationship between automation and inattentional blindness (IB) occurrences in a repeated induction paradigm using two types of runway incursions. The critical stimuli for both runway incursions were directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation conditions: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70 percent (42 of 60) detection failure rate with those in the PA condition significantly more likely to detect the incursion compared to the FA condition or the NA condition. The second induction yielded a 50 percent detection failure rate. Although detection improved (detection failure rates declined) in all conditions, those in the FA condition demonstrated the greatest improvement with doubled detection rates. The detection behavior in the first trial did not preclude a failed detection in the second induction. Group membership (IB vs. Detection) in the FA condition showed a greater improvement than those in the NA condition and rated the Mental Demand and Effort subscales of the NASA-TLX (NASA Task Load Index) significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to improve task performance whereas those in the NA condition did not, indicating the availability and reallocation of attentional resources in the FA condition. These findings support the role of engagement in operational attention detriment and the consideration of attentional failure causation to determine appropriate mitigation strategies.

Automated Corrosion Detection Program

DTIC Science & Technology

2001-10-01

More detailed explanations of the methodology development can be found in Hidden Corrosion Detection Technology Assessment, a paper presented at...Detection Program, a paper presented at the Fourth Joint DoD/FAA/NASA Conference on Aging Aircraft, 2000. AS&M PULSE. The PULSE system, developed...selection can be found in The Evaluation of Hidden Corrosion Detection Technologies on the Automated Corrosion Detection Program, a paper presented

Automated Content Detection for Cassini Images

NASA Astrophysics Data System (ADS)

Stanboli, A.; Bue, B.; Wagstaff, K.; Altinok, A.

2017-06-01

NASA missions generate numerous images ever organized in increasingly large archives. Image archives are currently not searchable by image content. We present an automated content detection prototype that can enable content search.

What Is an Automated External Defibrillator?

MedlinePlus

ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? An automated external defibrillator (AED) is a lightweight, portable device ... ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? detect a rhythm that should be ...

Falling Person Detection Using Multi-Sensor Signal Processing

NASA Astrophysics Data System (ADS)

Toreyin, B. Ugur; Soyer, A. Birey; Onaran, Ibrahim; Cetin, E. Enis

2007-12-01

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.

Rapid toxicity detection in water quality control utilizing automated multispecies biomonitoring for permanent space stations

NASA Technical Reports Server (NTRS)

Morgan, E. L.; Young, R. C.; Smith, M. D.; Eagleson, K. W.

1986-01-01

The objective of this study was to evaluate proposed design characteristics and applications of automated biomonitoring devices for real-time toxicity detection in water quality control on-board permanent space stations. Simulated tests in downlinking transmissions of automated biomonitoring data to Earth-receiving stations were simulated using satellite data transmissions from remote Earth-based stations.

Comparison of automated processing of flocked swabs with manual processing of fiber swabs for detection of nasal carriage of Staphylococcus aureus.

PubMed

Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-07-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture.

An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups.

PubMed

Saleh, George Michael; Wawrzynski, James; Caputo, Silvestro; Peto, Tunde; Al Turk, Lutfiah Ismail; Wang, Su; Hu, Yin; Da Cruz, Lyndon; Smith, Phil; Tang, Hongying Lilian

2016-01-01

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system's performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.

Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

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

Aykac, Deniz; Chaum, Edward; Fox, Karen

A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection,more » and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.« less

Analysis of Public Datasets for Wearable Fall Detection Systems.

PubMed

Casilari, Eduardo; Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

2017-06-27

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

Analysis of Public Datasets for Wearable Fall Detection Systems

PubMed Central

Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

2017-01-01

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs. PMID:28653991

Automated detection of retinal disease.

PubMed

Helmchen, Lorens A; Lehmann, Harold P; Abràmoff, Michael D

2014-11-01

Nearly 4 in 10 Americans with diabetes currently fail to undergo recommended annual retinal exams, resulting in tens of thousands of cases of blindness that could have been prevented. Advances in automated retinal disease detection could greatly reduce the burden of labor-intensive dilated retinal examinations by ophthalmologists and optometrists and deliver diagnostic services at lower cost. As the current availability of ophthalmologists and optometrists is inadequate to screen all patients at risk every year, automated screening systems deployed in primary care settings and even in patients' homes could fill the current gap in supply. Expanding screens to all patients at risk by switching to automated detection systems would in turn yield significantly higher rates of detecting and treating diabetic retinopathy per dilated retinal examination. Fewer diabetic patients would develop complications such as blindness, while ophthalmologists could focus on more complex cases.

Automated detection of a prostate Ni-Ti stent in electronic portal images.

PubMed

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-12-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins.

An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

PubMed

Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

2014-01-01

This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

PubMed Central

Gjoreski, Martin; Gjoreski, Hristijan; Luštrek, Mitja; Gams, Matjaž

2016-01-01

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). PMID:27258282

Aviation Safety: Modeling and Analyzing Complex Interactions between Humans and Automated Systems

NASA Technical Reports Server (NTRS)

Rungta, Neha; Brat, Guillaume; Clancey, William J.; Linde, Charlotte; Raimondi, Franco; Seah, Chin; Shafto, Michael

2013-01-01

The on-going transformation from the current US Air Traffic System (ATS) to the Next Generation Air Traffic System (NextGen) will force the introduction of new automated systems and most likely will cause automation to migrate from ground to air. This will yield new function allocations between humans and automation and therefore change the roles and responsibilities in the ATS. Yet, safety in NextGen is required to be at least as good as in the current system. We therefore need techniques to evaluate the safety of the interactions between humans and automation. We think that current human factor studies and simulation-based techniques will fall short in front of the ATS complexity, and that we need to add more automated techniques to simulations, such as model checking, which offers exhaustive coverage of the non-deterministic behaviors in nominal and off-nominal scenarios. In this work, we present a verification approach based both on simulations and on model checking for evaluating the roles and responsibilities of humans and automation. Models are created using Brahms (a multi-agent framework) and we show that the traditional Brahms simulations can be integrated with automated exploration techniques based on model checking, thus offering a complete exploration of the behavioral space of the scenario. Our formal analysis supports the notion of beliefs and probabilities to reason about human behavior. We demonstrate the technique with the Ueberligen accident since it exemplifies authority problems when receiving conflicting advices from human and automated systems.

Automated detection of bacteria in urine

NASA Technical Reports Server (NTRS)

Fleig, A. J.; Picciolo, G. L.; Chappelle, E. W.; Kelbaugh, B. N.

1972-01-01

A method for detecting the presence of bacteria in urine was developed which utilizes the bioluminescent reaction of adenosine triphosphate with luciferin and luciferase derived from the tails of fireflies. The method was derived from work on extraterrestrial life detection. A device was developed which completely automates the assay process.

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases

PubMed Central

2014-01-01

Background Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. Methods We used a set of complex detection rules to take account of the patient’s clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules’ analytical quality was evaluated for ADEs. Results In terms of recall, 89.5% of ADEs with hyperkalaemia “with or without an abnormal symptom” were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs. Conclusions The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases. PMID:25212108

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.

PubMed

Ficheur, Grégoire; Chazard, Emmanuel; Beuscart, Jean-Baptiste; Merlin, Béatrice; Luyckx, Michel; Beuscart, Régis

2014-09-12

Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs. In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs. The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

Effects of Response Bias and Judgment Framing on Operator Use of an Automated Aid in a Target Detection Task

ERIC Educational Resources Information Center

Rice, Stephen; McCarley, Jason S.

2011-01-01

Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in…

Comparison of Automated Processing of Flocked Swabs with Manual Processing of Fiber Swabs for Detection of Nasal Carriage of Staphylococcus aureus▿‡

PubMed Central

Jones, Gillian; Matthews, Roger; Cunningham, Richard; Jenks, Peter

2011-01-01

The sensitivity of automated culture of Staphylococcus aureus from flocked swabs versus that of manual culture of fiber swabs was prospectively compared using nasal swabs from 867 patients. Automated culture from flocked swabs significantly increased the detection rate, by 13.1% for direct culture and 10.2% for enrichment culture. PMID:21525218

Comparison study of membrane filtration direct count and an automated coliform and Escherichia coli detection system for on-site water quality testing.

PubMed

Habash, Marc; Johns, Robert

2009-10-01

This study compared an automated Escherichia coli and coliform detection system with the membrane filtration direct count technique for water testing. The automated instrument performed equal to or better than the membrane filtration test in analyzing E. coli-spiked samples and blind samples with interference from Proteus vulgaris or Aeromonas hydrophila.

Using microwave Doppler radar in automated manufacturing applications

NASA Astrophysics Data System (ADS)

Smith, Gregory C.

Since the beginning of the Industrial Revolution, manufacturers worldwide have used automation to improve productivity, gain market share, and meet growing or changing consumer demand for manufactured products. To stimulate further industrial productivity, manufacturers need more advanced automation technologies: "smart" part handling systems, automated assembly machines, CNC machine tools, and industrial robots that use new sensor technologies, advanced control systems, and intelligent decision-making algorithms to "see," "hear," "feel," and "think" at the levels needed to handle complex manufacturing tasks without human intervention. The investigator's dissertation offers three methods that could help make "smart" CNC machine tools and industrial robots possible: (1) A method for detecting acoustic emission using a microwave Doppler radar detector, (2) A method for detecting tool wear on a CNC lathe using a Doppler radar detector, and (3) An online non-contact method for detecting industrial robot position errors using a microwave Doppler radar motion detector. The dissertation studies indicate that microwave Doppler radar could be quite useful in automated manufacturing applications. In particular, the methods developed may help solve two difficult problems that hinder further progress in automating manufacturing processes: (1) Automating metal-cutting operations on CNC machine tools by providing a reliable non-contact method for detecting tool wear, and (2) Fully automating robotic manufacturing tasks by providing a reliable low-cost non-contact method for detecting on-line position errors. In addition, the studies offer a general non-contact method for detecting acoustic emission that may be useful in many other manufacturing and non-manufacturing areas, as well (e.g., monitoring and nondestructively testing structures, materials, manufacturing processes, and devices). By advancing the state of the art in manufacturing automation, the studies may help stimulate future growth in industrial productivity, which also promises to fuel economic growth and promote economic stability. The study also benefits the Department of Industrial Technology at Iowa State University and the field of Industrial Technology by contributing to the ongoing "smart" machine research program within the Department of Industrial Technology and by stimulating research into new sensor technologies within the University and within the field of Industrial Technology.

Planning and executing motions for multibody systems in free-fall. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cameron, Jonathan M.

1991-01-01

The purpose of this research is to develop an end-to-end system that can be applied to a multibody system in free-fall to analyze its possible motions, save those motions in a database, and design a controller that can execute those motions. A goal is for the process to be highly automated and involve little human intervention. Ideally, the output of the system would be data and algorithms that could be put in ROM to control the multibody system in free-fall. The research applies to more than just robots in space. It applies to any multibody system in free-fall. Mathematical techniques from nonlinear control theory were used to study the nature of the system dynamics and its possible motions. Optimization techniques were applied to plan motions. Image compression techniques were proposed to compress the precomputed motion data for storage. A linearized controller was derived to control the system while it executes preplanned trajectories.

Automatic detection of lift-off and touch-down of a pick-up walker using 3D kinematics.

PubMed

Grootveld, L; Thies, S B; Ogden, D; Howard, D; Kenney, L P J

2014-02-01

Walking aids have been associated with falls and it is believed that incorrect use limits their usefulness. Measures are therefore needed that characterize their stable use and the classification of key events in walking aid movement is the first step in their development. This study presents an automated algorithm for detection of lift-off (LO) and touch-down (TD) events of a pick-up walker. For algorithm design and initial testing, a single user performed trials for which the four individual walker feet lifted off the ground and touched down again in various sequences, and for different amounts of frame loading (Dataset_1). For further validation, ten healthy young subjects walked with the pick-up walker on flat ground (Dataset_2a) and on a narrow beam (Dataset_2b), to challenge balance. One 88-year-old walking frame user was also assessed. Kinematic data were collected with a 3D optoelectronic camera system. The algorithm detected over 93% of events (Dataset_1), and 95% and 92% in Dataset_2a and b, respectively. Of the various LO/TD sequences, those associated with natural progression resulted in up to 100% correctly identified events. For the 88-year-old walking frame user, 96% of LO events and 93% of TD events were detected, demonstrating the potential of the approach. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Tracking wakefulness as it fades: Micro-measures of alertness.

PubMed

Jagannathan, Sridhar R; Ezquerro-Nassar, Alejandro; Jachs, Barbara; Pustovaya, Olga V; Bareham, Corinne A; Bekinschtein, Tristan A

2018-08-01

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings. Copyright © 2018. Published by Elsevier Inc.

Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir.II. Spring and fall cold-hardiness

Treesearch

K.D. Jermstad; D.L. Bassoni; N.C. Wheeler; T.S. Anekonda; S.N. Aitken; W.T. Adams; D.B. Neale

2001-01-01

Abstract Quantitative trait loci (QTLs) affecting fall and spring cold-hardiness were identified in a three-generation outbred pedigree of coastal Douglas-fir [Pseudotsuga meniziesii (Mirb.) Franco var. menziesii]. Eleven QTLs controlling fall cold-hardiness were detected on four linkage groups, and 15 QTLs controlling spring cold-hardiness were detected on four...

Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network.

PubMed

Lee, Youngbum; Kim, Jinkwon; Son, Muntak; Lee, Myoungho

2007-01-01

This research implements wireless accelerometer sensor module and algorithm to determine wearer's posture, activity and fall. Wireless accelerometer sensor module uses ADXL202, 2-axis accelerometer sensor (Analog Device). And using wireless RF module, this module measures accelerometer signal and shows the signal at ;Acceloger' viewer program in PC. ADL algorithm determines posture, activity and fall that activity is determined by AC component of accelerometer signal and posture is determined by DC component of accelerometer signal. Those activity and posture include standing, sitting, lying, walking, running, etc. By the experiment for 30 subjects, the performance of implemented algorithm was assessed, and detection rate for postures, motions and subjects was calculated. Lastly, using wireless sensor network in experimental space, subject's postures, motions and fall monitoring system was implemented. By the simulation experiment for 30 subjects, 4 kinds of activity, 3 times, fall detection rate was calculated. In conclusion, this system can be application to patients and elders for activity monitoring and fall detection and also sports athletes' exercise measurement and pattern analysis. And it can be expected to common person's exercise training and just plaything for entertainment.

Automated detection of diabetic retinopathy: barriers to translation into clinical practice.

PubMed

Abramoff, Michael D; Niemeijer, Meindert; Russell, Stephen R

2010-03-01

Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.

Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection.

PubMed

Ernstsen, Christina L; Login, Frédéric H; Jensen, Helene H; Nørregaard, Rikke; Møller-Jensen, Jakob; Nejsum, Lene N

2017-10-01

Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

[Establishment of Automation System for Detection of Alcohol in Blood].

PubMed

Tian, L L; Shen, Lei; Xue, J F; Liu, M M; Liang, L J

2017-02-01

To establish an automation system for detection of alcohol content in blood. The determination was performed by automated workstation of extraction-headspace gas chromatography （HS-GC）. The blood collection with negative pressure, sealing time of headspace bottle and sample needle were checked and optimized in the abstraction of automation system. The automatic sampling was compared with the manual sampling. The quantitative data obtained by the automated workstation of extraction-HS-GC for alcohol was stable. The relative differences of two parallel samples were less than 5%. The automated extraction was superior to the manual extraction. A good linear relationship was obtained at the alcohol concentration range of 0.1-3.0 mg/mL （ r ≥0.999） with good repeatability. The method is simple and quick, with more standard experiment process and accurate experimental data. It eliminates the error from the experimenter and has good repeatability, which can be applied to the qualitative and quantitative detections of alcohol in blood. Copyright© by the Editorial Department of Journal of Forensic Medicine

Automated Historical and Real-Time Cyclone Discovery With Multimodal Remote Satellite Measurements

NASA Astrophysics Data System (ADS)

Ho, S.; Talukder, A.; Liu, T.; Tang, W.; Bingham, A.

2008-12-01

Existing cyclone detection and tracking solutions involve extensive manual analysis of modeled-data and field campaign data by teams of experts. We have developed a novel automated global cyclone detection and tracking system by assimilating and sharing information from multiple remote satellites. This unprecedented solution of combining multiple remote satellite measurements in an autonomous manner allows leveraging off the strengths of each individual satellite. Use of multiple satellite data sources also results in significantly improved temporal tracking accuracy for cyclones. Our solution involves an automated feature extraction and machine learning technique based on an ensemble classifier and Kalman filter for cyclone detection and tracking from multiple heterogeneous satellite data sources. Our feature-based methodology that focuses on automated cyclone discovery is fundamentally different from, and actually complements, the well-known Dvorak technique for cyclone intensity estimation (that often relies on manual detection of cyclonic regions) from field and remote data. Our solution currently employs the QuikSCAT wind measurement and the merged level 3 TRMM precipitation data for automated cyclone discovery. Assimilation of other types of remote measurements is ongoing and planned in the near future. Experimental results of our automated solution on historical cyclone datasets demonstrate the superior performance of our automated approach compared to previous work. Performance of our detection solution compares favorably against the list of cyclones occurring in North Atlantic Ocean for the 2005 calendar year reported by the National Hurricane Center (NHC) in our initial analysis. We have also demonstrated the robustness of our cyclone tracking methodology in other regions over the world by using multiple heterogeneous satellite data for detection and tracking of three arbitrary historical cyclones in other regions. Our cyclone detection and tracking methodology can be applied to (i) historical data to support Earth scientists in climate modeling, cyclonic-climate interactions, and obtain a better understanding of the cause and effects of cyclone (e.g. cyclo-genesis), and (ii) automatic cyclone discovery in near real-time using streaming satellite to support and improve the planning of global cyclone field campaigns. Additional satellite data from GOES and other orbiting satellites can be easily assimilated and integrated into our automated cyclone detection and tracking module to improve the temporal tracking accuracy of cyclones down to ½ hr and reduce the incidence of false alarms.

A cyber-physical system for senior collapse detection

NASA Astrophysics Data System (ADS)

Grewe, Lynne; Magaña-Zook, Steven

2014-06-01

Senior Collapse Detection (SCD) is a system that uses cyber-physical techniques to create a "smart home" system to predict and detect the falling of senior/geriatric participants in home environments. This software application addresses the needs of millions of senior citizens who live at home by themselves and can find themselves in situations where they have fallen and need assistance. We discuss how SCD uses imagery, depth and audio to fuse and interact in a system that does not require the senior to wear any devices allowing them to be more autonomous. The Microsoft Kinect Sensor is used to collect imagery, depth and audio. We will begin by discussing the physical attributes of the "collapse detection problem". Next, we will discuss the task of feature extraction resulting in skeleton and joint tracking. Improvements in error detection of joint tracking will be highlighted. Next, we discuss the main module of "fall detection" using our mid-level skeleton features. Attributes including acceleration, position and room environment factor into the SCD fall detection decision. Finally, how a detected fall and the resultant emergency response are handled will be presented. Results in a home environment will be given.

NASA Tech Briefs, December 2007

NASA Technical Reports Server (NTRS)

2007-01-01

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Anatomic viral detection is automated: the application of a robotic molecular pathology system for the detection of DNA viruses in anatomic pathology substrates, using immunocytochemical and nucleic acid hybridization techniques.

PubMed Central

Montone, K. T.; Brigati, D. J.; Budgeon, L. R.

1989-01-01

This paper presents the first automated system for simultaneously detecting human papilloma, herpes simplex, adenovirus, or cytomegalovirus viral antigens and gene sequences in standard formalin-fixed, paraffin-embedded tissue substrates and tissue culture. These viruses can be detected by colorimetric in situ nucleic acid hybridization, using biotinylated DNA probes, or by indirect immunoperoxidase techniques, using polyclonal or monoclonal antibodies, in a 2.0-hour assay performed at a single automated robotic workstation. Images FIG. 1 FIG. 4 FIG. 5 FIG. 6 FIG. 7 FIG. 8 FIG. 9 FIG. 10 FIG. 11 PMID:2773514

iFall: an Android application for fall monitoring and response.

PubMed

Sposaro, Frank; Tyson, Gary

2009-01-01

Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity. The algorithm adapts to unique movements that a phone experiences as opposed to similar systems which require users to mount accelerometers to their chest or trunk. If a fall is suspected a notification is raised requiring the user's response. If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS. If a contact responds the system commits an audible notification, automatically connects, and enables the speakerphone. If a social contact confirms a fall, an appropriate emergency service is alerted. Our system provides a realizable, cost effective solution to fall detection using a simple graphical interface while not overwhelming the user with uncomfortable sensors.

SisFall: A Fall and Movement Dataset

PubMed Central

Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

2017-01-01

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark. PMID:28117691

SisFall: A Fall and Movement Dataset.

PubMed

Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

2017-01-20

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

A miniature, wearable activity/fall monitor to assess the efficacy of mobility therapy for children with cerebral palsy during everyday living.

PubMed

Smith, Warren D; Bagley, Anita

2010-01-01

Children with cerebral palsy may have difficulty walking and may fall frequently, resulting in a decrease in their participation in school and community activities. It is desirable to assess the effectiveness of mobility therapies for these children on their functioning during everyday living. Over 50 hours of tri-axial accelerometer and digital video recordings from 35 children with cerebral palsy and 51 typically-developing children were analyzed to develop algorithms for automatic real-time processing of the accelerometer signals to monitor a child's level of activity and to detect falls. The present fall-detection algorithm has 100% specificity and a sensitivity of 100% for falls involving trunk rotation. Sensitivities for drops to the knees and to the bottom are 72% and 78%, respectively. The activity and fall-detection algorithms were implemented in a miniature, battery-powered microcontroller-based activity/fall monitor that the child wears in a small fanny pack during everyday living. The monitor continuously logs 1-min. activity levels and the occurrence and characteristics of each fall for two-week recording sessions. Pre-therapy and post-therapy recordings from these monitors will be used to assess the efficacies of alternative treatments for gait abnormalities.

Survey on fall detection and fall prevention using wearable and external sensors.

PubMed

Delahoz, Yueng Santiago; Labrador, Miguel Angel

2014-10-22

According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

Operations management system advanced automation: Fault detection isolation and recovery prototyping

NASA Technical Reports Server (NTRS)

Hanson, Matt

1990-01-01

The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection

PubMed Central

Santoyo-Ramón, Jose Antonio; Cano-García, Jose Manuel

2016-01-01

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture. PMID:27930736

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection.

PubMed

Casilari, Eduardo; Santoyo-Ramón, Jose Antonio; Cano-García, Jose Manuel

2016-01-01

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient's mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Huang, Alex S.; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M.; Weinreb, Robert N.

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm's canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC's was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

Automated methods for multiplexed pathogen detection.

PubMed

Straub, Timothy M; Dockendorff, Brian P; Quiñonez-Díaz, Maria D; Valdez, Catherine O; Shutthanandan, Janani I; Tarasevich, Barbara J; Grate, Jay W; Bruckner-Lea, Cynthia J

2005-09-01

Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cycler where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides "live vs. dead" capabilities. However, sensitivity of the method will need to be improved for RNA analysis to replace PCR.

Automated Methods for Multiplexed Pathogen Detection

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

Straub, Tim M.; Dockendorff, Brian P.; Quinonez-Diaz, Maria D.

2005-09-01

Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cyclermore » where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides ''live vs. dead'' capabilities. However, sensitivity of the method will need to be improved for RNA analysis to replace PCR.« less

A novel tool for high-throughput screening of granulocyte-specific antibodies using the automated flow cytometric granulocyte immunofluorescence test (Flow-GIFT).

PubMed

Nguyen, Xuan Duc; Dengler, Thomas; Schulz-Linkholt, Monika; Klüter, Harald

2011-02-03

Transfusion-related acute lung injury (TRALI) is a severe complication related with blood transfusion. TRALI has usually been associated with antibodies against leukocytes. The flow cytometric granulocyte immunofluorescence test (Flow-GIFT) has been introduced for routine use when investigating patients and healthy blood donors. Here we describe a novel tool in the automation of the Flow-GIFT that enables a rapid screening of blood donations. We analyzed 440 sera from healthy female blood donors for the presence of granulocyte antibodies. As positive controls, 12 sera with known antibodies against anti-HNA-1a, -b, -2a; and -3a were additionally investigated. Whole-blood samples from HNA-typed donors were collected and the test cells isolated using cell sedimentation in a Ficoll density gradient. Subsequently, leukocytes were incubated with the respective serum and binding of antibodies was detected using FITC-conjugated antihuman antibody. 7-AAD was used to exclude dead cells. Pipetting steps were automated using the Biomek NXp Multichannel Automation Workstation. All samples were prepared in the 96-deep well plates and analyzed by flow cytometry. The standard granulocyte immunofluorescence test (GIFT) and granulocyte agglutination test (GAT) were also performed as reference methods. Sixteen sera were positive in the automated Flow-GIFT, while five of these sera were negative in the standard GIFT (anti-HNA 3a, n = 3; anti-HNA-1b, n = 1) and GAT (anti-HNA-2a, n = 1). The automated Flow-GIFT was able to detect all granulocyte antibodies, which could be only detected in GIFT in combination with GAT. In serial dilution tests, the automated Flow-GIFT detected the antibodies at higher dilutions than the reference methods GIFT and GAT. The Flow-GIFT proved to be feasible for automation. This novel high-throughput system allows an effective antigranulocyte antibody detection in a large donor population in order to prevent TRALI due to transfusion of blood products.

Status of the Desert Fireball Network

NASA Astrophysics Data System (ADS)

Devillepoix, H. A. R.; Bland, P. A.; Towner, M. C.; Cupák, M.; Sansom, E. K.; Jansen-Sturgeon, T.; Howie, R. M.; Paxman, J.; Hartig, B. A. D.

2016-01-01

A meteorite fall precisely observed from multiple locations allows us to track the object back to the region of the Solar System it came from, and sometimes link it with a parent body, providing context information that helps trace the history of the Solar System. The Desert Fireball Network (DFN) is built in arid areas of Australia: its observatories get favorable observing conditions, and meteorite recovery is eased thanks to the mostly featureless terrain. After the successful recovery of two meteorites with 4 film cameras, the DFN has now switched to a digital network, operating 51 cameras, covering 2.5 million km2 of double station triangulable area. Mostly made of off-the-shelf components, the new observatories are cost effective while maintaining high imaging performance. To process the data (~70TB/month), a significant effort has been put to writing an automated reduction pipeline so that all events are reduced with little human intervention. Innovative techniques have been implemented for this purpose: machine learning algorithms for event detection, blind astrometric calibration, and particle filter simulations to estimate both physical properties and state vector of the meteoroid. On 31 December 2015, the first meteorite from the digital systems was recovered: Murrili (the 1.68 kg H5 ordinary chondrite was observed to fall on 27 November 2015). Another 11 events have been flagged as potential meteorites droppers, and are to be searched in the coming months.

Characterization of Multianode Photomultiplier Tubes for a Cherenkov Detector

NASA Astrophysics Data System (ADS)

Benninghoff, Morgen; Turisini, Matteo; Kim, Andrey; Benmokhtar, Fatiha; Kubarovsky, Valery; Duquesne University Collaboration; Jefferson Lab Collaboration

2017-09-01

In the Fall of 2017, Jefferson Lab's CLAS12 (CEBAF Large Acceptance Spectrometer) detector is expecting the addition of a RICH (ring imaging Cherenkov) detector which will allow enhanced particle identification in the momentum range of 3 to 8 GeV/c. RICH detectors measure the velocity of charged particles through the detection of produced Cherenkov radiation and the reconstruction of the angle of emission. The emitted Cherenkov photons are detected by a triangular-shaped grid of 391 multianode photomultiplier tubes (MAPMTs) made by Hamamatsu. The custom readout electronics consist of MAROC (multianode read out chip) boards controlled by FPGA (Field Programmable Gate Array) boards, and adapters used to connect the MAROC boards and MAPMTs. The focus of this project is the characterization of the MAPMTs with the new front end electronics. To perform these tests, a black box setup with a picosecond diode laser was constructed with low and high voltage supplies. A highly automated procedure was developed to acquire data at different combinations of high voltage values, light intensities and readout electronics settings. Future work involves using the collected data in calibration procedures and analyzing that data to resolve the best location for each MAPMT. SULI, NSF.

MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Wright, R.; Pilger, E.

2009-12-01

We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found that MODVOLC2 achieved good results on multiple sensors (MODIS and GOES), which provides confidence that MODVOLC2 can be run on future instruments regardless of their spatial and temporal resolutions. The improved performance of MODVOLC2 over MODVOLC makes possible the detection of lower temperature thermal anomalies that will be useful in improving our ability to document Earth’s volcanic eruptions as well as detect possible low temperature thermal precursors to larger eruptions.

Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home

PubMed Central

Yang, Mau-Tsuen; Chuang, Min-Wen

2013-01-01

Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second. PMID:24335727

Fall risk assessment and early-warning for toddler behaviors at home.

PubMed

Yang, Mau-Tsuen; Chuang, Min-Wen

2013-12-10

Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second.

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

PubMed Central

Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

2011-01-01

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

Accurate Fall Detection in a Top View Privacy Preserving Configuration.

PubMed

Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio

2018-05-29

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

Automated detection of diabetic retinopathy on digital fundus images.

PubMed

Sinthanayothin, C; Boyce, J F; Williamson, T H; Cook, H L; Mensah, E; Lal, S; Usher, D

2002-02-01

The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Semi-Disk Structure

DTIC Science & Technology

2018-01-01

statistical moments of order 2, 3, and 4. The probability density function (PDF) of the vibrational time series of a good bearing has a Gaussian...ARL-TR-8271 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...when it is no longer needed. Do not return it to the originator. ARL-TR-8271 ● JAN 2018 US Army Research Laboratory An Automated

[Problems with placement and using of automated external defibrillators in Czech Republic].

PubMed

Olos, Tomás; Bursa, Filip; Gregor, Roman; Holes, David

2011-01-01

The use of automated external defibrillators improves the survival of adults who suffer from cardiopulmonary arrest. Automated external defibrillators detect ventricular fibrillation with almost perfect sensitivity and specificity. Authors describe the use of automated external defibrillator during cardiopulmonary resuscitation in a patient with sudden cardiac arrest during ice-hockey match. The article reports also the use of automated external defibrillators in children.

InPRO: Automated Indoor Construction Progress Monitoring Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Hamledari, Hesam

In this research, an envisioned automated intelligent robotic solution for automated indoor data collection and inspection that employs a series of unmanned aerial vehicles (UAV), entitled "InPRO", is presented. InPRO consists of four stages, namely: 1) automated path planning; 2) autonomous UAV-based indoor inspection; 3) automated computer vision-based assessment of progress; and, 4) automated updating of 4D building information models (BIM). The works presented in this thesis address the third stage of InPRO. A series of computer vision-based methods that automate the assessment of construction progress using images captured at indoor sites are introduced. The proposed methods employ computer vision and machine learning techniques to detect the components of under-construction indoor partitions. In particular, framing (studs), insulation, electrical outlets, and different states of drywall sheets (installing, plastering, and painting) are automatically detected using digital images. High accuracy rates, real-time performance, and operation without a priori information are indicators of the methods' promising performance.

Trace-Level Automated Mercury Speciation Analysis

PubMed Central

Taylor, Vivien F.; Carter, Annie; Davies, Colin; Jackson, Brian P.

2011-01-01

An automated system for methyl Hg analysis by purge and trap gas chromatography (GC) was evaluated, with comparison of several different instrument configurations including chromatography columns (packed column or capillary), detector (atomic fluorescence, AFS, or inductively coupled plasma mass spectrometry, ICP-MS, using quadrupole and sector field ICP- MS instruments). Method detection limits (MDL) of 0.042 pg and 0.030 pg for CH3Hg+ were achieved with the automated Hg analysis system configured with AFS and ICPMS detection, respectively. Capillary GC with temperature programming was effective in improving resolution and decreasing retention times of heavier Hg species (in this case C3H7Hg+) although carryover between samples was increased. With capillary GC, the MDL for CH3Hg+ was 0.25 pg for AFS detection and 0.060 pg for ICP-MS detection. The automated system was demonstrated to have high throughput (72 samples analyzed in 8 hours) requiring considerably less analyst time than the manual method for methyl mercury analysis described in EPA 1630. PMID:21572543

Automated detection of exudates for diabetic retinopathy screening

NASA Astrophysics Data System (ADS)

Fleming, Alan D.; Philip, Sam; Goatman, Keith A.; Williams, Graeme J.; Olson, John A.; Sharp, Peter F.

2007-12-01

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

Towards an Automated Acoustic Detection System for Free Ranging Elephants.

PubMed

Zeppelzauer, Matthias; Hensman, Sean; Stoeger, Angela S

The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In this paper, we present a method for the automated detection of elephant vocalizations that is robust to the diverse noise sources present in the field. We evaluate the method on a dataset recorded under natural field conditions to simulate a real-world scenario. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants. Furthermore, the method may be a useful tool for scientists in bioacoustics for the study of wildlife recordings.

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

PubMed Central

Guvensan, M. Amac; Kansiz, A. Oguz; Camgoz, N. Cihan; Turkmen, H. Irem; Yavuz, A. Gokhan; Karsligil, M. Elif

2017-01-01

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions. PMID:28644378

Does smart home technology prevent falls in community-dwelling older adults: a literature review.

PubMed

Pietrzak, Eva; Cotea, Cristina; Pullman, Stephen

2014-01-01

Falls in older Australians are an increasingly costly public health issue, driving the development of novel modes of intervention, especially those that rely on computer-driven technologies. The aim of this paper was to gain an understanding of the state of the art of research on smart homes and computer-based monitoring technologies to prevent and detect falls in the community-dwelling elderly. Cochrane, Medline, Embase and Google databases were searched for articles on fall prevention in the elderly using pre-specified search terms. Additional papers were searched for in the reference lists of relevant reviews and by the process of 'snowballing'. Only studies that investigated outcomes related to falling such as fall prevention and detection, change in participants' fear of falling and attitudes towards monitoring technology were included. Nine papers fulfilled the inclusion criteria. The following outcomes were observed: (1) older adults' attitudes towards fall detectors and smart home technology are generally positive; (2) privacy concerns and intrusiveness of technology were perceived as less important to participants than their perception of health needs and (3) unfriendly and age-inappropriate design of the interface may be one of the deciding factors in not using the technology. So far, there is little evidence that using smart home technology may assist in fall prevention or detection, but there are some indications that it may increase older adults' confidence and sense of security, thus possibly enabling aging in place.

Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer.

PubMed

Lu, Hao; Papathomas, Thomas G; van Zessen, David; Palli, Ivo; de Krijger, Ronald R; van der Spek, Peter J; Dinjens, Winand N M; Stubbs, Andrew P

2014-11-25

In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI. We have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking. We have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the 'hottest' hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/Automated_Selection_of_Hotspots . The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216.

Autofocusing and Polar Body Detection in Automated Cell Manipulation.

PubMed

Wang, Zenan; Feng, Chen; Ang, Wei Tech; Tan, Steven Yih Min; Latt, Win Tun

2017-05-01

Autofocusing and feature detection are two essential processes for performing automated biological cell manipulation tasks. In this paper, we have introduced a technique capable of focusing on a holding pipette and a mammalian cell under a bright-field microscope automatically, and a technique that can detect and track the presence and orientation of the polar body of an oocyte that is rotated at the tip of a micropipette. Both algorithms were evaluated by using mouse oocytes. Experimental results show that both algorithms achieve very high success rates: 100% and 96%. As robust and accurate image processing methods, they can be widely applied to perform various automated biological cell manipulations.

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

PubMed Central

Delahoz, Yueng Santiago; Labrador, Miguel Angel

2014-01-01

According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters. PMID:25340452

Laboratory Testing Protocols for Heparin-Induced Thrombocytopenia (HIT) Testing.

PubMed

Lau, Kun Kan Edwin; Mohammed, Soma; Pasalic, Leonardo; Favaloro, Emmanuel J

2017-01-01

Heparin-induced thrombocytopenia (HIT) represents a significant high morbidity complication of heparin therapy. The clinicopathological diagnosis of HIT remains challenging for many reasons; thus, laboratory testing represents an important component of an accurate diagnosis. Although there are many assays available to assess HIT, these essentially fall into two categories-(a) immunological assays, and (b) functional assays. The current chapter presents protocols for several HIT assays, being those that are most commonly performed in laboratory practice and have the widest geographic distribution. These comprise a manual lateral flow-based system (STiC), a fully automated latex immunoturbidimetric assay, a fully automated chemiluminescent assay (CLIA), light transmission aggregation (LTA), and whole blood aggregation (Multiplate).

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

PubMed

Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

2018-03-01

We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Automated Immunomagnetic Separation and Microarray Detection of E. coli O157:H7 from Poultry Carcass Rinse

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

Chandler, Darrell P.; Brown, Jeremy D.; Call, Douglas R.

2001-09-01

We describe the development and application of a novel electromagnetic flow cell and fluidics system for automated immunomagnetic separation of E. coli directly from unprocessed poultry carcass rinse, and the biochemical coupling of automated sample preparation with nucleic acid microarrays without cell growth. Highly porous nickel foam was used as a magnetic flux conductor. Up to 32% recovery efficiency of 'total' E. coli was achieved within the automated system with 6 sec contact times and 15 minute protocol (from sample injection through elution), statistically similar to cell recovery efficiencies in > 1 hour 'batch' captures. The electromagnet flow cell allowedmore » complete recovery of 2.8 mm particles directly from unprocessed poultry carcass rinse whereas the batch system did not. O157:H7 cells were reproducibly isolated directly from unprocessed poultry rinse with 39% recovery efficiency at 103 cells ml-1 inoculum. Direct plating of washed beads showed positive recovery of O 157:H7 directly from carcass rinse at an inoculum of 10 cells ml-1. Recovered beads were used for direct PCR amplification and microarray detection, with a process-level detection limit (automated cell concentration through microarray detection) of < 103 cells ml-1 carcass rinse. The fluidic system and analytical approach described here are generally applicable to most microbial detection problems and applications.« less

Failure of the MicroScan WalkAway System To Detect Heteroresistance to Carbapenems in a Patient with Enterobacter aerogenes Bacteremia▿

PubMed Central

Gordon, N. C.; Wareham, D. W.

2009-01-01

We report the failure of the automated MicroScan WalkAway system to detect carbapenem heteroresistance in Enterobacter aerogenes. Carbapenem resistance has become an increasing concern in recent years, and robust surveillance is required to prevent dissemination of resistant strains. Reliance on automated systems may delay the detection of emerging resistance. PMID:19641071

Automated circumferential construction of first-order aqueous humor outflow pathways using spectral-domain optical coherence tomography.

PubMed

Huang, Alex S; Belghith, Akram; Dastiridou, Anna; Chopra, Vikas; Zangwill, Linda M; Weinreb, Robert N

2017-06-01

The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.

A First Cut at Doctrine for Automation of Division Command and Control.

DTIC Science & Technology

1985-12-02

34MSE: Mobile Subscriber Equipmentd," Arm-Y Communicator (Fall 1984), pp. 6-22. 20. Anthony L. Borelli and LTC Raymond J. Leopold, "Enhanced JTIDS: High...three, 1985. I’- pp. 85-8,. Berceau, CPT Stan. "ASAS." C2MUG Bulletin. September 1965, pp. 1-2. Borelli , Anthony L. and Leopold, LTC Raymond J

A segmental analysis of current and future scanning and optimizing technology in the hardwood sawmill industry

Treesearch

S.A. Bowe; R.L. Smith; D. Earl Kline; Philip A. Araman

2002-01-01

A nationwide survey of advanced scanning and optimizing technology in the hardwood sawmill industry was conducted in the fall of 1999. Three specific hardwood sawmill technologies were examined that included current edger-optimizer systems, future edger-optimizer systems, and future automated grading systems. The objectives of the research were to determine differences...

Framework for Human-Automation Collaboration: Conclusions from Four Studies

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

Oxstrand, Johanna; Le Blanc, Katya L.; O'Hara, John

The Human Automation Collaboration (HAC) research project is investigating how advanced technologies that are planned for Advanced Small Modular Reactors (AdvSMR) will affect the performance and the reliability of the plant from a human factors and human performance perspective. The HAC research effort investigates the consequences of allocating functions between the operators and automated systems. More specifically, the research team is addressing how to best design the collaboration between the operators and the automated systems in a manner that has the greatest positive impact on overall plant performance and reliability. Oxstrand et al. (2013 - March) describes the efforts conductedmore » by the researchers to identify the research needs for HAC. The research team reviewed the literature on HAC, developed a model of HAC, and identified gaps in the existing knowledge of human-automation collaboration. As described in Oxstrand et al. (2013 – June), the team then prioritized the research topics identified based on the specific needs in the context of AdvSMR. The prioritization was based on two sources of input: 1) The preliminary functions and tasks, and 2) The model of HAC. As a result, three analytical studies were planned and conduced; 1) Models of Teamwork, 2) Standardized HAC Performance Measurement Battery, and 3) Initiators and Triggering Conditions for Adaptive Automation. Additionally, one field study was also conducted at Idaho Falls Power.« less

Detection of anti-salmonella flgk antibodies in chickens by automated capillary immunoassay

USDA-ARS?s Scientific Manuscript database

Western blot is a very useful tool to identify specific protein, but is tedious, labor-intensive and time-consuming. An automated "Simple Western" assay has recently been developed that enables the protein separation, blotting and detection in an automatic manner. However, this technology has not ...

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

NASA Technical Reports Server (NTRS)

Jackson, Gail

2012-01-01

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. In order to collect information on the complete global precipitation cycle and to understand the energy budget in terms of precipitation, uniform global estimates of both liquid and frozen precipitation must be collected. Active observations of falling snow are somewhat easier to estimate since the radar will detect the precipitation particles and one only needs to know surface temperature to determine if it is liquid rain or snow. The challenges of estimating falling snow from passive spaceborne observations still exist though progress is being made. While these challenges are still being addressed, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Important information to assess falling snow retrievals includes knowing thresholds of detection for active and passive sensors, various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (2.5 km) cloud tops having an ice water content (Iwe) at the surface of 0.25 g m-3 and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The analysis relies on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Results are presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz (Skofronick-Jackson, et al. submitted to IEEE TGRS, April 2012). The notable results show: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with 0.05 g m-3 detected at the surface, or an 0.5-1 mm hr-l melted snow rate (equivalent to 0.5-2 cm hr-l solid fluffy snowflake rate).

Automated Monitoring with a BSP Fault-Detection Test

NASA Technical Reports Server (NTRS)

Bickford, Randall L.; Herzog, James P.

2003-01-01

The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.

Automatic fall monitoring: a review.

PubMed

Pannurat, Natthapon; Thiemjarus, Surapa; Nantajeewarawat, Ekawit

2014-07-18

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

Automatic Fall Monitoring: A Review

PubMed Central

Pannurat, Natthapon; Thiemjarus, Surapa; Nantajeewarawat, Ekawit

2014-01-01

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address. PMID:25046016

Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter

NASA Astrophysics Data System (ADS)

Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi

2013-03-01

Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

PubMed Central

2018-01-01

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected. PMID:29621156

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer.

PubMed

Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

2018-04-05

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail; Johnson, Benjamin T.; Munchak, S. Joseph

2012-01-01

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Automated biosurveillance data from England and Wales, 1991-2011.

PubMed

Enki, Doyo G; Noufaily, Angela; Garthwaite, Paul H; Andrews, Nick J; Charlett, André; Lane, Chris; Farrington, C Paddy

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

Automated Biosurveillance Data from England and Wales, 1991–2011

PubMed Central

Enki, Doyo G.; Noufaily, Angela; Garthwaite, Paul H.; Andrews, Nick J.; Charlett, André; Lane, Chris

2013-01-01

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity. PMID:23260848

Segmentation of 3D ultrasound computer tomography reflection images using edge detection and surface fitting

NASA Astrophysics Data System (ADS)

Hopp, T.; Zapf, M.; Ruiter, N. V.

2014-03-01

An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

Automated exploitation of sky polarization imagery.

PubMed

Sadjadi, Firooz A; Chun, Cornell S L

2018-03-10

We propose an automated method for detecting neutral points in the sunlit sky. Until now, detecting these singularities has been done manually. Results are presented that document the application of this method on a limited number of polarimetric images of the sky captured with a camera and rotating polarizer. The results are significant because a method for automatically detecting the neutral points may aid in the determination of the solar position when the sun is obscured and may have applications in meteorology and pollution detection and characterization.

Automating quantum dot barcode assays using microfluidics and magnetism for the development of a point-of-care device.

PubMed

Gao, Yali; Lam, Albert W Y; Chan, Warren C W

2013-04-24

The impact of detecting multiple infectious diseases simultaneously at point-of-care with good sensitivity, specificity, and reproducibility would be enormous for containing the spread of diseases in both resource-limited and rich countries. Many barcoding technologies have been introduced for addressing this need as barcodes can be applied to detecting thousands of genetic and protein biomarkers simultaneously. However, the assay process is not automated and is tedious and requires skilled technicians. Barcoding technology is currently limited to use in resource-rich settings. Here we used magnetism and microfluidics technology to automate the multiple steps in a quantum dot barcode assay. The quantum dot-barcoded microbeads are sequentially (a) introduced into the chip, (b) magnetically moved to a stream containing target molecules, (c) moved back to the original stream containing secondary probes, (d) washed, and (e) finally aligned for detection. The assay requires 20 min, has a limit of detection of 1.2 nM, and can detect genetic targets for HIV, hepatitis B, and syphilis. This study provides a simple strategy to automate the entire barcode assay process and moves barcoding technologies one step closer to point-of-care applications.

Human versus automation in responding to failures: an expected-value analysis

NASA Technical Reports Server (NTRS)

Sheridan, T. B.; Parasuraman, R.

2000-01-01

A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

Automated Detection of Optic Disc in Fundus Images

NASA Astrophysics Data System (ADS)

Burman, R.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.

Optic disc (OD) localization is an important preprocessing step in the automated image detection of fundus image infected with glaucoma. An Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution (DE) is applied to determine the location of the OD in the right of left eye retinal fundus image. The algorithm, when applied to 460 fundus images from the MESSIDOR dataset, shows a success rate of 99.07 % for 217 normal images and 95.47 % for 243 pathological images. The mean computational time is 1.709 s for normal images and 1.753 s for pathological images. These results are important for automated detection of glaucoma and for telemedicine purposes.

Involvement of older people in the development of fall detection systems: a scoping review.

PubMed

Thilo, Friederike J S; Hürlimann, Barbara; Hahn, Sabine; Bilger, Selina; Schols, Jos M G A; Halfens, Ruud J G

2016-02-11

The involvement of users is recommended in the development of health related technologies, in order to address their needs and preferences and to improve the daily usage of these technologies. The objective of this literature review was to identify the nature and extent of research involving older people in the development of fall detection systems. A scoping review according to the framework of Arksey and O'Malley was carried out. A key term search was employed in eight relevant databases. Included articles were summarized using a predetermined charting form and subsequently thematically analysed. A total of 53 articles was included. In 49 of the 53 articles, older people were involved in the design and/or testing stages, and in 4 of 53 articles, they were involved in the conceptual or market deployment stages. In 38 of the 53 articles, the main focus of the involvement of older people was technical aspects. In 15 of the 53 articles, the perspectives of the elderly related to the fall detection system under development were determined using focus groups, single interviews or questionnaires. Until presently, involvement of older people in the development of fall detection systems has focused mainly on technical aspects. Little attention has been given to the specific needs and views of older people in the context of fall detection system development and usage.

Effect of different alcohol levels on take-over performance in conditionally automated driving.

PubMed

Wiedemann, Katharina; Naujoks, Frederik; Wörle, Johanna; Kenntner-Mabiala, Ramona; Kaussner, Yvonne; Neukum, Alexandra

2018-06-01

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the driver's task will consist of being available as a fall-back level when the automation reaches its limits. These so-called take-over situations have attracted a great body of research, focusing on various human factors aspects (e.g., sleepiness) that could undermine the safety of control transitions between automated and manual driving. However, a major source of accidents in manual driving, alcohol consumption, has been a non-issue so far, although a false understanding of the driver's responsibility (i.e., being available as a fallback level) might promote driving under its influence. In this experiment, N = 36 drivers were exposed to different levels of blood alcohol concentrations (BACs: placebo vs. 0.05% vs. 0.08%) in a high fidelity driving simulator, and the effect on take-over time and quality was assessed. The results point out that a 0.08% BAC increases the time needed to re-engage in the driving task and impairs several aspects of longitudinal and lateral vehicle control, whereas 0.05% BAC did only go along with descriptive impairments in fewer parameters. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

PubMed Central

Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

2016-01-01

Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

Combined Falling Drop/Open Port Sampling Interface System for Automated Flow Injection Mass Spectrometry

DOE PAGES

Van Berkel, Gary J.; Kertesz, Vilmos; Orcutt, Matt; ...

2017-11-07

The aim of this work was to demonstrate and to evaluate the analytical performance of a combined falling drop/open port sampling interface (OPSI) system as a simple noncontact, no-carryover, automated system for flow injection analysis with mass spectrometry. The falling sample drops were introduced into the OPSI using a widely available autosampler platform utilizing low cost disposable pipet tips and conventional disposable microtiter well plates. The volume of the drops that fell onto the OPSI was in the 7–15 μL range with an injected sample volume of several hundred nanoliters. Sample drop height, positioning of the internal capillary on themore » sampling end of the probe, and carrier solvent flow rate were optimized for maximum signal. Sample throughput, signal reproducibility, matrix effects, and quantitative analysis capability of the system were established using the drug molecule propranolol and its isotope labeled internal standard in water, unprocessed river water and two commercially available buffer matrices. A sample-to-sample throughput of ~45 s with a ~4.5 s base-to-base flow injection peak profile was obtained in these experiments. In addition, quantitation with minimally processed rat plasma samples was demonstrated with three different statin drugs (atorvastatin, rosuvastatin, and fluvastatin). Direct characterization capability of unprocessed samples was demonstrated by the analysis of neat vegetable oils. Employing the autosampler system for spatially resolved liquid extraction surface sampling exemplified by the analysis of propranolol and its hydroxypropranolol glucuronide phase II metabolites from a rat thin tissue section was also illustrated.« less

Combined Falling Drop/Open Port Sampling Interface System for Automated Flow Injection Mass Spectrometry

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

Van Berkel, Gary J.; Kertesz, Vilmos; Orcutt, Matt

The aim of this work was to demonstrate and to evaluate the analytical performance of a combined falling drop/open port sampling interface (OPSI) system as a simple noncontact, no-carryover, automated system for flow injection analysis with mass spectrometry. The falling sample drops were introduced into the OPSI using a widely available autosampler platform utilizing low cost disposable pipet tips and conventional disposable microtiter well plates. The volume of the drops that fell onto the OPSI was in the 7–15 μL range with an injected sample volume of several hundred nanoliters. Sample drop height, positioning of the internal capillary on themore » sampling end of the probe, and carrier solvent flow rate were optimized for maximum signal. Sample throughput, signal reproducibility, matrix effects, and quantitative analysis capability of the system were established using the drug molecule propranolol and its isotope labeled internal standard in water, unprocessed river water and two commercially available buffer matrices. A sample-to-sample throughput of ~45 s with a ~4.5 s base-to-base flow injection peak profile was obtained in these experiments. In addition, quantitation with minimally processed rat plasma samples was demonstrated with three different statin drugs (atorvastatin, rosuvastatin, and fluvastatin). Direct characterization capability of unprocessed samples was demonstrated by the analysis of neat vegetable oils. Employing the autosampler system for spatially resolved liquid extraction surface sampling exemplified by the analysis of propranolol and its hydroxypropranolol glucuronide phase II metabolites from a rat thin tissue section was also illustrated.« less

Proof of Concept of Automated Collision Detection Technology in Rugby Sevens.

PubMed

Clarke, Anthea C; Anson, Judith M; Pyne, David B

2017-04-01

Clarke, AC, Anson, JM, and Pyne, DB. Proof of concept of automated collision detection technology in rugby sevens. J Strength Cond Res 31(4): 1116-1120, 2017-Developments in microsensor technology allow for automated detection of collisions in various codes of football, removing the need for time-consuming postprocessing of video footage. However, little research is available on the ability of microsensor technology to be used across various sports or genders. Game video footage was matched with microsensor-detected collisions (GPSports) in one men's (n = 12 players) and one women's (n = 12) rugby sevens match. True-positive, false-positive, and false-negative events between video and microsensor-detected collisions were used to calculate recall (ability to detect a collision) and precision (accurately identify a collision). The precision was similar between the men's and women's rugby sevens game (∼0.72; scale 0.00-1.00); however, the recall in the women's game (0.45) was less than that for the men's game (0.69). This resulted in 45% of collisions for men and 62% of collisions for women being incorrectly labeled. Currently, the automated collision detection system in GPSports microtechnology units has only modest utility in rugby sevens, and it seems that a rugby sevens-specific algorithm is needed. Differences in measures between the men's and women's game may be a result of physical size, and strength, and physicality, as well as technical and tactical factors.

Fall risk assessment among older adults with mild Alzheimer disease.

PubMed

Ryan, John J; McCloy, Constance; Rundquist, Peter; Srinivasan, Visalakshi; Laird, Rosemary

2011-01-01

Older adults with Alzheimer disease (AD) fall more than twice as often as those without dementia, yet few studies have assessed fall risk in this population. The purpose of the study was to determine whether a fall assessment, the Physical Performance Test 7-item (PPT 7-item), could accurately identify subjects with history of falls in a group of community-dwelling elders with mild AD. An additional purpose was to determine whether the PPT 7-item, a cognitive screen, and/or nonperformance data could predict falling in this population. Forty-three community-dwelling elders diagnosed with mild AD completed the fall risk assessment. In addition, the following data were collected: Mini-Mental State Examination (MMSE) score, age, gender, education, gait aid use, number of falls in the past 6 months, and history of fall-related injury. There was a significant difference in the PPT 7-item total score between subjects with history of falls and subjects without history of falls (z = -2.04, P = .042), with items related to turning (z = -2.56, P = .01) and walking (z = -2.89, P = .004) accounting for most of the difference. However, only gait aid usage predicted falling (45.8% of the variance). While the PPT 7-item was able to detect differences in mobility between subjects with history of falls and subjects without history of falls in subjects with mild AD, total PPT 7-item score did not predict falling. Gait aid usage was more strongly related to falling in these subjects. Early detection of fall risk in individuals with mild AD is important to prevent injuries and moderate costs of care.

Automated Detection of Repetitive Motor Behaviors as an Outcome Measurement in Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Gilchrist, Kristin H.; Hegarty-Craver, Meghan; Christian, Robert B.; Grego, Sonia; Kies, Ashley C.; Wheeler, Anne C.

2018-01-01

Repetitive sensory motor behaviors are a direct target for clinical treatment and a potential treatment endpoint for individuals with intellectual or developmental disabilities. By removing the burden associated with video annotation or direct observation, automated detection of stereotypy would allow for longer term monitoring in ecologic…

Validation of shortened 2-day sterility testing of mesenchymal stem cell-based therapeutic preparation on an automated culture system.

PubMed

Lysák, Daniel; Holubová, Monika; Bergerová, Tamara; Vávrová, Monika; Cangemi, Giuseppina Cristina; Ciccocioppo, Rachele; Kruzliak, Peter; Jindra, Pavel

2016-03-01

Cell therapy products represent a new trend of treatment in the field of immunotherapy and regenerative medicine. Their biological nature and multistep preparation procedure require the application of complex release criteria and quality control. Microbial contamination of cell therapy products is a potential source of morbidity in recipients. The automated blood culture systems are widely used for the detection of microorganisms in cell therapy products. However the standard 2-week cultivation period is too long for some cell-based treatments and alternative methods have to be devised. We tried to verify whether a shortened cultivation of the supernatant from the mesenchymal stem cell (MSC) culture obtained 2 days before the cell harvest could sufficiently detect microbial growth and allow the release of MSC for clinical application. We compared the standard Ph. Eur. cultivation method and the automated blood culture system BACTEC (Becton Dickinson). The time to detection (TTD) and the detection limit were analyzed for three bacterial and two fungal strains. The Staphylococcus aureus and Pseudomonas aeruginosa were recognized within 24 h with both methods (detection limit ~10 CFU). The time required for the detection of Bacillus subtilis was shorter with the automated method (TTD 10.3 vs. 60 h for 10-100 CFU). The BACTEC system reached significantly shorter times to the detection of Candida albicans and Aspergillus brasiliensis growth compared to the classical method (15.5 vs. 48 and 31.5 vs. 48 h, respectively; 10-100 CFU). The positivity was demonstrated within 48 h in all bottles, regardless of the size of the inoculum. This study validated the automated cultivation system as a method able to detect all tested microorganisms within a 48-h period with a detection limit of ~10 CFU. Only in case of B. subtilis, the lowest inoculum (~10 CFU) was not recognized. The 2-day cultivation technique is then capable of confirming the microbiological safety of MSC and allows their timely release for clinical application.

Utilizing Weather RADAR for Rapid Location of Meteorite Falls and Space Debris Re-Entry

NASA Technical Reports Server (NTRS)

Fries, Marc D.

2016-01-01

This activity utilizes existing NOAA weather RADAR imagery to locate meteorite falls and space debris falls. The near-real-time availability and spatial accuracy of these data allow rapid recovery of material from both meteorite falls and space debris re-entry events. To date, at least 22 meteorite fall recoveries have benefitted from RADAR detection and fall modeling, and multiple debris re-entry events over the United States have been observed in unprecedented detail.

Progress in Fully Automated Abdominal CT Interpretation

PubMed Central

Summers, Ronald M.

2016-01-01

OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation. PMID:27101207

Automated aortic calcification detection in low-dose chest CT images

NASA Astrophysics Data System (ADS)

Xie, Yiting; Htwe, Yu Maw; Padgett, Jennifer; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

2014-03-01

The extent of aortic calcification has been shown to be a risk indicator for vascular events including cardiac events. We have developed a fully automated computer algorithm to segment and measure aortic calcification in low-dose noncontrast, non-ECG gated, chest CT scans. The algorithm first segments the aorta using a pre-computed Anatomy Label Map (ALM). Then based on the segmented aorta, aortic calcification is detected and measured in terms of the Agatston score, mass score, and volume score. The automated scores are compared with reference scores obtained from manual markings. For aorta segmentation, the aorta is modeled as a series of discrete overlapping cylinders and the aortic centerline is determined using a cylinder-tracking algorithm. Then the aortic surface location is detected using the centerline and a triangular mesh model. The segmented aorta is used as a mask for the detection of aortic calcification. For calcification detection, the image is first filtered, then an elevated threshold of 160 Hounsfield units (HU) is used within the aorta mask region to reduce the effect of noise in low-dose scans, and finally non-aortic calcification voxels (bony structures, calcification in other organs) are eliminated. The remaining candidates are considered as true aortic calcification. The computer algorithm was evaluated on 45 low-dose non-contrast CT scans. Using linear regression, the automated Agatston score is 98.42% correlated with the reference Agatston score. The automated mass and volume score is respectively 98.46% and 98.28% correlated with the reference mass and volume score.

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

VLSI synthesis of digital application specific neural networks

NASA Technical Reports Server (NTRS)

Beagles, Grant; Winters, Kel

1991-01-01

Neural networks tend to fall into two general categories: (1) software simulations, or (2) custom hardware that must be trained. The scope of this project is the merger of these two classifications into a system whereby a software model of a network is trained to perform a specific task and the results used to synthesize a standard cell realization of the network using automated tools.

THE DIFFERENCE IMAGING PIPELINE FOR THE TRANSIENT SEARCH IN THE DARK ENERGY SURVEY

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

Kessler, R.; Scolnic, D.; Marriner, J.

2015-12-15

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg{sup 2} fields are repeatedly observed in the g, r, i, z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImgmore » functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are ∼130 detections per deg{sup 2} per observation in each band, of which only ∼25% are artifacts. Of the ∼7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another ∼30% of the transients are artifacts in which a small number of observations satisfy the selection criteria for a single-epoch detection. Spectroscopic analysis shows that most of the remaining transients are AGNs and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a MC simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 “shallow” fields with single-epoch 50% completeness depth ∼23.5, the SN Ia efficiency falls to 1/2 at redshift z ≈ 0.7; in our 2 “deep” fields with mag-depth ∼24.5, the efficiency falls to 1/2 at z ≈ 1.1. A remaining performance issue is that the measured fluxes have additional scatter (beyond Poisson fluctuations) that increases with the host galaxy surface brightness at the transient location. This bright-galaxy issue has minimal impact on the SNe Ia program, but it may lower the efficiency for finding fainter transients on bright galaxies.« less

The Difference Imaging Pipeline for the Transient Search in the Dark Energy Survey

NASA Astrophysics Data System (ADS)

Kessler, R.; Marriner, J.; Childress, M.; Covarrubias, R.; D'Andrea, C. B.; Finley, D. A.; Fischer, J.; Foley, R. J.; Goldstein, D.; Gupta, R. R.; Kuehn, K.; Marcha, M.; Nichol, R. C.; Papadopoulos, A.; Sako, M.; Scolnic, D.; Smith, M.; Sullivan, M.; Wester, W.; Yuan, F.; Abbott, T.; Abdalla, F. B.; Allam, S.; Benoit-Lévy, A.; Bernstein, G. M.; Bertin, E.; Brooks, D.; Carnero Rosell, A.; Carrasco Kind, M.; Castander, F. J.; Crocce, M.; da Costa, L. N.; Desai, S.; Diehl, H. T.; Eifler, T. F.; Fausti Neto, A.; Flaugher, B.; Frieman, J.; Gerdes, D. W.; Gruen, D.; Gruendl, R. A.; Honscheid, K.; James, D. J.; Kuropatkin, N.; Li, T. S.; Maia, M. A. G.; Marshall, J. L.; Martini, P.; Miller, C. J.; Miquel, R.; Nord, B.; Ogando, R.; Plazas, A. A.; Reil, K.; Romer, A. K.; Roodman, A.; Sanchez, E.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Tarle, G.; Thaler, J.; Thomas, R. C.; Tucker, D.; Walker, A. R.; DES Collaboration

2015-12-01

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg2 fields are repeatedly observed in the g, r, i, z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImg functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are ˜130 detections per deg2 per observation in each band, of which only ˜25% are artifacts. Of the ˜7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another ˜30% of the transients are artifacts in which a small number of observations satisfy the selection criteria for a single-epoch detection. Spectroscopic analysis shows that most of the remaining transients are AGNs and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a MC simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 “shallow” fields with single-epoch 50% completeness depth ˜23.5, the SN Ia efficiency falls to 1/2 at redshift z ≈ 0.7; in our 2 “deep” fields with mag-depth ˜24.5, the efficiency falls to 1/2 at z ≈ 1.1. A remaining performance issue is that the measured fluxes have additional scatter (beyond Poisson fluctuations) that increases with the host galaxy surface brightness at the transient location. This bright-galaxy issue has minimal impact on the SNe Ia program, but it may lower the efficiency for finding fainter transients on bright galaxies.

THE DIFFERENCE IMAGING PIPELINE FOR THE TRANSIENT SEARCH IN THE DARK ENERGY SURVEY

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

Kessler, R.; Marriner, J.; Childress, M.

2015-11-06

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg(2) fields are repeatedly observed in the g, r, i, z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImg functionsmore » are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are similar to 130 detections per deg(2) per observation in each band, of which only similar to 25% are artifacts. Of the similar to 7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another similar to 30% of the transients are artifacts in which a small number of observations satisfy the selection criteria for a single-epoch detection. Spectroscopic analysis shows that most of the remaining transients are AGNs and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies and to understand the DiffImg performance. The DiffImg efficiency measured with fake SNe agrees well with expectations from a MC simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 "shallow" fields with single-epoch 50% completeness depth similar to 23.5, the SN Ia efficiency falls to 1/2 at redshift z approximate to 0.7; in our 2 "deep" fields with mag-depth similar to 24.5, the efficiency falls to 1/2 at z approximate to 1.1. A remaining performance issue is that the measured fluxes have additional scatter (beyond Poisson fluctuations) that increases with the host galaxy surface brightness at the transient location. This bright-galaxy issue has minimal impact on the SNe Ia program, but it may lower the efficiency for finding fainter transients on bright galaxies.« less

The Difference Imaging Pipeline for the Transient Search in the Dark Energy Survey

DOE PAGES

Kessler, R.

2015-09-09

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg 2 fields are repeatedly observed in the g, r, i, zpassbands with a cadence of about 1 week. Our observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImg functionsmore » are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are ~130 detections per deg 2 per observation in each band, of which only ~25% are artifacts. Of the ~7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another ~30% of the transients are artifacts in which a small number of observations satisfy the selection criteria for a single-epoch detection. Spectroscopic analysis shows that most of the remaining transients are AGNs and variable stars. Fake SNe Ia are overlaid onto the images to rigorously evaluate detection efficiencies and to understand the DiffImg performance. Furthermore, the DiffImg efficiency measured with fake SNe agrees well with expectations from a MC simulation that uses analytical calculations of the fluxes and their uncertainties. In our 8 "shallow" fields with single-epoch 50% completeness depth ~23.5, the SN Ia efficiency falls to 1/2 at redshift z ≈ 0.7; in our 2 "deep" fields with mag-depth ~24.5, the efficiency falls to 1/2 at z ≈ 1.1. A remaining performance issue is that the measured fluxes have additional scatter (beyond Poisson fluctuations) that increases with the host galaxy surface brightness at the transient location. This bright-galaxy issue has minimal impact on the SNe Ia program, but it may lower the efficiency for finding fainter transients on bright galaxies.« less

Evaluation of a fall-prevention program in older people after femoral neck fracture: a one-year follow-up.

PubMed

Berggren, M; Stenvall, M; Olofsson, B; Gustafson, Y

2008-06-01

A randomized, controlled fall-prevention study including 199 patients operated on for femoral neck fracture reduced inpatient falls and injuries. No statistically significant effects of the intervention program could be detected after discharge. It seems that fall-prevention must be part of everyday life in fall-prone old people. This study evaluates whether a postoperative multidisciplinary, multifactorial fall-prevention program performed by a geriatric team that reduced inpatient falls and injuries had any continuing effect after discharge. The intervention consisted of staff education, systematic assessment and treatment of fall risk factors and vitamin D and calcium supplementation. The randomized, controlled trial with a one-year follow-up at Umeå University Hospital, Sweden, included 199 patients operated on for femoral neck fracture, aged > or = 70 years. After one year 44 participants had fallen 138 times in the intervention group compared with 55 participants and 191 falls in the control group. The crude postoperative fall incidence was 4.16/1,000 days in the intervention group vs. 6.43/1,000 days in the control group. The incidence rate ratio was 0.64 (95% CI: 0.40-1.02, p = 0.063). Seven new fractures occurred in the intervention group and 11 in the control group. A team applying comprehensive geriatric assessment and rehabilitation, including prevention and treatment of fall-risk factors, reduced inpatient falls and injuries, but no statistically significant effects of the program could be detected after discharge. It seems that fall-prevention must be part of everyday life in fall-prone elderly.

Development and Validation of an Automated High-Throughput System for Zebrafish In Vivo Screenings

PubMed Central

Virto, Juan M.; Holgado, Olaia; Diez, Maria; Izpisua Belmonte, Juan Carlos; Callol-Massot, Carles

2012-01-01

The zebrafish is a vertebrate model compatible with the paradigms of drug discovery. The small size and transparency of zebrafish embryos make them amenable for the automation necessary in high-throughput screenings. We have developed an automated high-throughput platform for in vivo chemical screenings on zebrafish embryos that includes automated methods for embryo dispensation, compound delivery, incubation, imaging and analysis of the results. At present, two different assays to detect cardiotoxic compounds and angiogenesis inhibitors can be automatically run in the platform, showing the versatility of the system. A validation of these two assays with known positive and negative compounds, as well as a screening for the detection of unknown anti-angiogenic compounds, have been successfully carried out in the system developed. We present a totally automated platform that allows for high-throughput screenings in a vertebrate organism. PMID:22615792

Automated Processing of 2-D Gel Electrophoretograms of Genomic DNA for Hunting Pathogenic DNA Molecular Changes.

PubMed

Takahashi; Nakazawa; Watanabe; Konagaya

1999-01-01

We have developed the automated processing algorithms for 2-dimensional (2-D) electrophoretograms of genomic DNA based on RLGS (Restriction Landmark Genomic Scanning) method, which scans the restriction enzyme recognition sites as the landmark and maps them onto a 2-D electrophoresis gel. Our powerful processing algorithms realize the automated spot recognition from RLGS electrophoretograms and the automated comparison of a huge number of such images. In the final stage of the automated processing, a master spot pattern, on which all the spots in the RLGS images are mapped at once, can be obtained. The spot pattern variations which seemed to be specific to the pathogenic DNA molecular changes can be easily detected by simply looking over the master spot pattern. When we applied our algorithms to the analysis of 33 RLGS images derived from human colon tissues, we successfully detected several colon tumor specific spot pattern changes.

Real World Experience With Ion Implant Fault Detection at Freescale Semiconductor

NASA Astrophysics Data System (ADS)

Sing, David C.; Breeden, Terry; Fakhreddine, Hassan; Gladwin, Steven; Locke, Jason; McHugh, Jim; Rendon, Michael

2006-11-01

The Freescale automatic fault detection and classification (FDC) system has logged data from over 3.5 million implants in the past two years. The Freescale FDC system is a low cost system which collects summary implant statistics at the conclusion of each implant run. The data is collected by either downloading implant data log files from the implant tool workstation, or by exporting summary implant statistics through the tool's automation interface. Compared to the traditional FDC systems which gather trace data from sensors on the tool as the implant proceeds, the Freescale FDC system cannot prevent scrap when a fault initially occurs, since the data is collected after the implant concludes. However, the system can prevent catastrophic scrap events due to faults which are not detected for days or weeks, leading to the loss of hundreds or thousands of wafers. At the Freescale ATMC facility, the practical applications of the FD system fall into two categories: PM trigger rules which monitor tool signals such as ion gauges and charge control signals, and scrap prevention rules which are designed to detect specific failure modes that have been correlated to yield loss and scrap. PM trigger rules are designed to detect shifts in tool signals which indicate normal aging of tool systems. For example, charging parameters gradually shift as flood gun assemblies age, and when charge control rules start to fail a flood gun PM is performed. Scrap prevention rules are deployed to detect events such as particle bursts and excessive beam noise, events which have been correlated to yield loss. The FDC system does have tool log-down capability, and scrap prevention rules often use this capability to automatically log the tool into a maintenance state while simultaneously paging the sustaining technician for data review and disposition of the affected product.

Social-aware Event Handling within the FallRisk Project.

PubMed

De Backere, Femke; Van den Bergh, Jan; Coppers, Sven; Elprama, Shirley; Nelis, Jelle; Verstichel, Stijn; Jacobs, An; Coninx, Karin; Ongenae, Femke; De Turck, Filip

2017-01-09

With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way. The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account. The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process. The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.

Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

ERIC Educational Resources Information Center

Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-01-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

A comparison of automated crater detection methods

NASA Astrophysics Data System (ADS)

Bandeira, L.; Barreira, C.; Pina, P.; Saraiva, J.

2008-09-01

Abstract This work presents early results of a comparison between some common methodologies for automated crater detection. The three procedures considered were applied to images of the surface of Mars, thus illustrating some pros and cons of their use. We aim to establish the clear advantages in using this type of methods in the study of planetary surfaces.

Phase II: Automated System for Aneuploidy Detection in Sperm Final Report CRADA No. TC-1554-98

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

Wyrobek, W. J.; Dunlay, R. T.

This was a collaborative effort between the University of California, Lawrence Livermore National Laboratory (LLNL) and Cellomics, Inc. (formerly BioDx and Biological Detection, Inc.) to develop an automated system for detecting human sperm aneuploidy. Aneuploidy (an abnormal number of chromosomes) is one of the major categories of chromosomally abnormal sperm, which results in chromosomally defective pregnancies and babies. An automated system would be used for testing the effects of toxic agents and for other research and clinical applications. This collaborated effort was funded by a National Institutes of Environmental Health Services, Phase II, Small Business Innovation Research Program (SBIR) grantmore » to Cellornics (Contract No. N44-ES-82004).« less

Development of a Miniature Mass Spectrometer and an Automated Detector for Sampling Explosive Materials

PubMed Central

Hashimoto, Yuichiro

2017-01-01

The development of a robust ionization source using the counter-flow APCI, miniature mass spectrometer, and an automated sampling system for detecting explosives are described. These development efforts using mass spectrometry were made in order to improve the efficiencies of on-site detection in areas such as security, environmental, and industrial applications. A development team, including the author, has struggled for nearly 20 years to enhance the robustness and reduce the size of mass spectrometers to meet the requirements needed for on-site applications. This article focuses on the recent results related to the detection of explosive materials where automated particle sampling using a cyclone concentrator permitted the inspection time to be successfully reduced to 3 s. PMID:28337396

Behavior Analysis Based on Coordinates of Body Tags

NASA Astrophysics Data System (ADS)

Luštrek, Mitja; Kaluža, Boštjan; Dovgan, Erik; Pogorelc, Bogdan; Gams, Matjaž

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.

21 CFR 864.9300 - Automated Coombs test systems.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound to red cells. The Coombs test is used for the diagnosis of hemolytic disease of the newborn, and...

21 CFR 864.9300 - Automated Coombs test systems.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound to red cells. The Coombs test is used for the diagnosis of hemolytic disease of the newborn, and...

21 CFR 864.9300 - Automated Coombs test systems.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound to red cells. The Coombs test is used for the diagnosis of hemolytic disease of the newborn, and...

21 CFR 864.9300 - Automated Coombs test systems.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound to red cells. The Coombs test is used for the diagnosis of hemolytic disease of the newborn, and...

21 CFR 864.9300 - Automated Coombs test systems.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Blood and Blood Products § 864.9300 Automated Coombs test systems. (a) Identification. An automated Coombs test system is a device used to detect and identify antibodies in patient sera or antibodies bound to red cells. The Coombs test is used for the diagnosis of hemolytic disease of the newborn, and...

Automated Power-Distribution System

NASA Technical Reports Server (NTRS)

Ashworth, Barry; Riedesel, Joel; Myers, Chris; Miller, William; Jones, Ellen F.; Freeman, Kenneth; Walsh, Richard; Walls, Bryan K.; Weeks, David J.; Bechtel, Robert T.

1992-01-01

Autonomous power-distribution system includes power-control equipment and automation equipment. System automatically schedules connection of power to loads and reconfigures itself when it detects fault. Potential terrestrial applications include optimization of consumption of power in homes, power supplies for autonomous land vehicles and vessels, and power supplies for automated industrial processes.

Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket.

PubMed

Vermeulen, Joan; Willard, Sarah; Aguiar, Bruno; De Witte, Luc P

2015-01-01

The objective of this study was to evaluate the sensitivity and specificity of a smartphone-based fall detection application when different smartphone models are worn on a belt or in a trouser pocket. Eight healthy adults aged between 18 and 24 years old simulated 10 different types of true falls, 5 different types of falls with recovery, and 11 daily activities, five consecutive times. Participants wore one smartphone in a pocket that was attached to their belt and another one in their trouser pocket. All smartphones were equipped with a built-in accelerometer and the fall detection application. Four participants tested the application on a Samsung S3 and four tested the application on a Samsung S3 mini. Sensitivity scores were .75 (Samsung S3 belt), .88 (Samsung S3 mini trouser pocket), and .90 (Samsung S3 mini belt/Samsung S3 trouser pocket). Specificity scores were .87 (Samsung S3 trouser pocket), .91 (Samsung S3 mini trouser pocket), .97 (Samsung S3 belt), and .99 (Samsung S3 mini belt). These results suggest that an application on a smartphone can generate valid fall alarms when worn on a belt or in a trouser pocket. However, sensitivity should be improved before implementation of the application in practice.

Automated face detection for occurrence and occupancy estimation in chimpanzees.

PubMed

Crunchant, Anne-Sophie; Egerer, Monika; Loos, Alexander; Burghardt, Tilo; Zuberbühler, Klaus; Corogenes, Katherine; Leinert, Vera; Kulik, Lars; Kühl, Hjalmar S

2017-03-01

Surveying endangered species is necessary to evaluate conservation effectiveness. Camera trapping and biometric computer vision are recent technological advances. They have impacted on the methods applicable to field surveys and these methods have gained significant momentum over the last decade. Yet, most researchers inspect footage manually and few studies have used automated semantic processing of video trap data from the field. The particular aim of this study is to evaluate methods that incorporate automated face detection technology as an aid to estimate site use of two chimpanzee communities based on camera trapping. As a comparative baseline we employ traditional manual inspection of footage. Our analysis focuses specifically on the basic parameter of occurrence where we assess the performance and practical value of chimpanzee face detection software. We found that the semi-automated data processing required only 2-4% of the time compared to the purely manual analysis. This is a non-negligible increase in efficiency that is critical when assessing the feasibility of camera trap occupancy surveys. Our evaluations suggest that our methodology estimates the proportion of sites used relatively reliably. Chimpanzees are mostly detected when they are present and when videos are filmed in high-resolution: the highest recall rate was 77%, for a false alarm rate of 2.8% for videos containing only chimpanzee frontal face views. Certainly, our study is only a first step for transferring face detection software from the lab into field application. Our results are promising and indicate that the current limitation of detecting chimpanzees in camera trap footage due to lack of suitable face views can be easily overcome on the level of field data collection, that is, by the combined placement of multiple high-resolution cameras facing reverse directions. This will enable to routinely conduct chimpanzee occupancy surveys based on camera trapping and semi-automated processing of footage. Using semi-automated ape face detection technology for processing camera trap footage requires only 2-4% of the time compared to manual analysis and allows to estimate site use by chimpanzees relatively reliably. © 2017 Wiley Periodicals, Inc.

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

PubMed

Lequan Yu; Hao Chen; Qi Dou; Jing Qin; Pheng Ann Heng

2017-01-01

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

Automated crack detection in conductive smart-concrete structures using a resistor mesh model

NASA Astrophysics Data System (ADS)

Downey, Austin; D'Alessandro, Antonella; Ubertini, Filippo; Laflamme, Simon

2018-03-01

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a 500 × 500 × 50 mm3 reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure.

Automated detection of abnormalities in paranasal sinus on dental panoramic radiographs by using contralateral subtraction technique based on mandible contour

NASA Astrophysics Data System (ADS)

Mori, Shintaro; Hara, Takeshi; Tagami, Motoki; Muramatsu, Chicako; Kaneda, Takashi; Katsumata, Akitoshi; Fujita, Hiroshi

2013-02-01

Inflammation in paranasal sinus sometimes becomes chronic to take long terms for the treatment. The finding is important for the early treatment, but general dentists may not recognize the findings because they focus on teeth treatments. The purpose of this study was to develop a computer-aided detection (CAD) system for the inflammation in paranasal sinus on dental panoramic radiographs (DPRs) by using the mandible contour and to demonstrate the potential usefulness of the CAD system by means of receiver operating characteristic analysis. The detection scheme consists of 3 steps: 1) Contour extraction of mandible, 2) Contralateral subtraction, and 3) Automated detection. The Canny operator and active contour model were applied to extract the edge at the first step. At the subtraction step, the right region of the extracted contour image was flipped to compare with the left region. Mutual information between two selected regions was obtained to estimate the shift parameters of image registration. The subtraction images were generated based on the shift parameter. Rectangle regions of left and right paranasal sinus on the subtraction image were determined based on the size of mandible. The abnormal side of the regions was determined by taking the difference between the averages of each region. Thirteen readers were responded to all cases without and with the automated results. The averaged AUC of all readers was increased from 0.69 to 0.73 with statistical significance (p=0.032) when the automated detection results were provided. In conclusion, the automated detection method based on contralateral subtraction technique improves readers' interpretation performance of inflammation in paranasal sinus on DPRs.

Fusion of multiple quadratic penalty function support vector machines (QPFSVM) for automated sea mine detection and classification

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2002-08-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. The Quadratic Penalty Function Support Vector Machine (QPFSVM) algorithm to aid in the automated detection and classification of sea mines is introduced in this paper. The QPFSVM algorithm is easy to train, simple to implement, and robust to feature space dimension. Outputs of successive SVM algorithms are cascaded in stages (fused) to improve the Probability of Classification (Pc) and reduce the number of false alarms. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).

Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.

PubMed

Chen, Yukun; Wrenn, Jesse; Xu, Hua; Spickard, Anderson; Habermann, Ralf; Powers, James; Denny, Joshua C

2014-01-01

Competence is essential for health care professionals. Current methods to assess competency, however, do not efficiently capture medical students' experience. In this preliminary study, we used machine learning and natural language processing (NLP) to identify geriatric competency exposures from students' clinical notes. The system applied NLP to generate the concepts and related features from notes. We extracted a refined list of concepts associated with corresponding competencies. This system was evaluated through 10-fold cross validation for six geriatric competency domains: "medication management (MedMgmt)", "cognitive and behavioral disorders (CBD)", "falls, balance, gait disorders (Falls)", "self-care capacity (SCC)", "palliative care (PC)", "hospital care for elders (HCE)" - each an American Association of Medical Colleges competency for medical students. The systems could accurately assess MedMgmt, SCC, HCE, and Falls competencies with F-measures of 0.94, 0.86, 0.85, and 0.84, respectively, but did not attain good performance for PC and CBD (0.69 and 0.62 in F-measure, respectively).

Temporary Restoration of Bull Trout Passage at Albeni Falls Dam, 2008 Progress Report.

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

Bellgraph, Brian J.

2009-03-31

The goal of this project is to provide temporary upstream passage of bull trout around Albeni Falls Dam on the Pend Oreille River, Idaho. Our specific objectives are to capture fish downstream of Albeni Falls Dam, tag them with combination acoustic and radio transmitters, release them upstream of Albeni Falls Dam, and determine if genetic information on tagged fish can be used to accurately establish where fish are located during the spawning season. In 2007, radio receiving stations were installed at several locations throughout the Pend Oreille River watershed to detect movements of adult bull trout; however, no bull troutmore » were tagged during that year. In 2008, four bull trout were captured downstream of Albeni Falls Dam, implanted with transmitters, and released upstream of the dam at Priest River, Idaho. The most-likely natal tributaries of bull trout assigned using genetic analyses were Grouse Creek (N = 2); a tributary of the Pack River, Lightning Creek (N = 1); and Rattle Creek (N = 1), a tributary of Lightning Creek. All four bull trout migrated upstream from the release site in Priest River, Idaho, were detected at monitoring stations near Dover, Idaho, and were presumed to reside in Lake Pend Oreille from spring until fall 2008. The transmitter of one bull trout with a genetic assignment to Grouse Creek was found in Grouse Creek in October 2008; however, the fish was not found. The bull trout assigned to Rattle Creek was detected in the Clark Fork River downstream from Cabinet Gorge Dam (approximately 13 km from the mouth of Lightning Creek) in September but was not detected entering Lightning Creek. The remaining two bull trout were not detected in 2008 after detection at the Dover receiving stations. This report details the progress by work element in the 2008 statement of work, including data analyses of fish movements, and expands on the information reported in the quarterly Pisces status reports.« less

Level of Automation and Failure Frequency Effects on Simulated Lunar Lander Performance

NASA Technical Reports Server (NTRS)

Marquez, Jessica J.; Ramirez, Margarita

2014-01-01

A human-in-the-loop experiment was conducted at the NASA Ames Research Center Vertical Motion Simulator, where instrument-rated pilots completed a simulated terminal descent phase of a lunar landing. Ten pilots participated in a 2 x 2 mixed design experiment, with level of automation as the within-subjects factor and failure frequency as the between subjects factor. The two evaluated levels of automation were high (fully automated landing) and low (manual controlled landing). During test trials, participants were exposed to either a high number of failures (75% failure frequency) or low number of failures (25% failure frequency). In order to investigate the pilots' sensitivity to changes in levels of automation and failure frequency, the dependent measure selected for this experiment was accuracy of failure diagnosis, from which D Prime and Decision Criterion were derived. For each of the dependent measures, no significant difference was found for level of automation and no significant interaction was detected between level of automation and failure frequency. A significant effect was identified for failure frequency suggesting failure frequency has a significant effect on pilots' sensitivity to failure detection and diagnosis. Participants were more likely to correctly identify and diagnose failures if they experienced the higher levels of failures, regardless of level of automation

Automatic recognition of coronal type II radio bursts: The ARBIS 2 method and first observations

NASA Astrophysics Data System (ADS)

Lobzin, Vasili; Cairns, Iver; Robinson, Peter; Steward, Graham; Patterson, Garth

Major space weather events such as solar flares and coronal mass ejections are usually accompa-nied by solar radio bursts, which can potentially be used for real-time space weather forecasts. Type II radio bursts are produced near the local plasma frequency and its harmonic by fast electrons accelerated by a shock wave moving through the corona and solar wind with a typi-cal speed of 1000 km s-1 . The coronal bursts have dynamic spectra with frequency gradually falling with time and durations of several minutes. We present a new method developed to de-tect type II coronal radio bursts automatically and describe its implementation in an extended Automated Radio Burst Identification System (ARBIS 2). Preliminary tests of the method with spectra obtained in 2002 show that the performance of the current implementation is quite high, ˜ 80%, while the probability of false positives is reasonably low, with one false positive per 100-200 hr for high solar activity and less than one false event per 10000 hr for low solar activity periods. The first automatically detected coronal type II radio bursts are also presented. ARBIS 2 is now operational with IPS Radio and Space Services, providing email alerts and event lists internationally.

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

Counterflow Dielectrophoresis for Trypanosome Enrichment and Detection in Blood

NASA Astrophysics Data System (ADS)

Menachery, Anoop; Kremer, Clemens; Wong, Pui E.; Carlsson, Allan; Neale, Steven L.; Barrett, Michael P.; Cooper, Jonathan M.

2012-10-01

Human African trypanosomiasis or sleeping sickness is a deadly disease endemic in sub-Saharan Africa, caused by single-celled protozoan parasites. Although it has been targeted for elimination by 2020, this will only be realized if diagnosis can be improved to enable identification and treatment of afflicted patients. Existing techniques of detection are restricted by their limited field-applicability, sensitivity and capacity for automation. Microfluidic-based technologies offer the potential for highly sensitive automated devices that could achieve detection at the lowest levels of parasitemia and consequently help in the elimination programme. In this work we implement an electrokinetic technique for the separation of trypanosomes from both mouse and human blood. This technique utilises differences in polarisability between the blood cells and trypanosomes to achieve separation through opposed bi-directional movement (cell counterflow). We combine this enrichment technique with an automated image analysis detection algorithm, negating the need for a human operator.

Automated measurement of office, home and ambulatory blood pressure in atrial fibrillation.

PubMed

Kollias, Anastasios; Stergiou, George S

2014-01-01

1. Hypertension and atrial fibrillation (AF) often coexist and are strong risk factors for stroke. Current guidelines for blood pressure (BP) measurement in AF recommend repeated measurements using the auscultatory method, whereas the accuracy of the automated devices is regarded as questionable. This review presents the current evidence on the feasibility and accuracy of automated BP measurement in the presence of AF and the potential for automated detection of undiagnosed AF during such measurements. 2. Studies evaluating the use of automated BP monitors in AF are limited and have significant heterogeneity in methodology and protocols. Overall, the oscillometric method is feasible for static (office or home) and ambulatory use and appears to be more accurate for systolic than diastolic BP measurement. 3. Given that systolic hypertension is particularly common and important in the elderly, the automated BP measurement method may be acceptable for self-home and ambulatory monitoring, but not for professional office or clinic measurement. 4. An embedded algorithm for the detection of asymptomatic AF during routine automated BP measurement with high diagnostic accuracy has been developed and appears to be a useful screening tool for elderly hypertensives. © 2013 Wiley Publishing Asia Pty Ltd.

An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

2003-01-01

An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

Automated clinical system for chromosome analysis

NASA Technical Reports Server (NTRS)

Castleman, K. R.; Friedan, H. J.; Johnson, E. T.; Rennie, P. A.; Wall, R. J. (Inventor)

1978-01-01

An automatic chromosome analysis system is provided wherein a suitably prepared slide with chromosome spreads thereon is placed on the stage of an automated microscope. The automated microscope stage is computer operated to move the slide to enable detection of chromosome spreads on the slide. The X and Y location of each chromosome spread that is detected is stored. The computer measures the chromosomes in a spread, classifies them by group or by type and also prepares a digital karyotype image. The computer system can also prepare a patient report summarizing the result of the analysis and listing suspected abnormalities.

Early detection of glaucoma using fully automated disparity analysis of the optic nerve head (ONH) from stereo fundus images

NASA Astrophysics Data System (ADS)

Sharma, Archie; Corona, Enrique; Mitra, Sunanda; Nutter, Brian S.

2006-03-01

Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. Traditional early detection techniques involve observing changes in the ONH through an ophthalmoscope. Stereo fundus photography is also routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the Heidelberg Retina Tomograph (HRT) has not been found to be sufficiently sensitive for early detection. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. A 3-D visualization of the disc and cup is achieved assuming camera geometry. High correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study involving 159 stereo fundus image pairs has already been demonstrated. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this paper, we present a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.

Highly Sensitive and Automated Surface Enhanced Raman Scattering-based Immunoassay for H5N1 Detection with Digital Microfluidics.

PubMed

Wang, Yang; Ruan, Qingyu; Lei, Zhi-Chao; Lin, Shui-Chao; Zhu, Zhi; Zhou, Leiji; Yang, Chaoyong

2018-04-17

Digital microfluidics (DMF) is a powerful platform for a broad range of applications, especially immunoassays having multiple steps, due to the advantages of low reagent consumption and high automatization. Surface enhanced Raman scattering (SERS) has been proven as an attractive method for highly sensitive and multiplex detection, because of its remarkable signal amplification and excellent spatial resolution. Here we propose a SERS-based immunoassay with DMF for rapid, automated, and sensitive detection of disease biomarkers. SERS tags labeled with Raman reporter 4-mercaptobenzoic acid (4-MBA) were synthesized with a core@shell nanostructure and showed strong signals, good uniformity, and high stability. A sandwich immunoassay was designed, in which magnetic beads coated with antibodies were used as solid support to capture antigens from samples to form a beads-antibody-antigen immunocomplex. By labeling the immunocomplex with a detection antibody-functionalized SERS tag, antigen can be sensitively detected through the strong SERS signal. The automation capability of DMF can greatly simplify the assay procedure while reducing the risk of exposure to hazardous samples. Quantitative detection of avian influenza virus H5N1 in buffer and human serum was implemented to demonstrate the utility of the DMF-SERS method. The DMF-SERS method shows excellent sensitivity (LOD of 74 pg/mL) and selectivity for H5N1 detection with less assay time (<1 h) and lower reagent consumption (∼30 μL) compared to the standard ELISA method. Therefore, this DMF-SERS method holds great potentials for automated and sensitive detection of a variety of infectious diseases.

High precision automated face localization in thermal images: oral cancer dataset as test case

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Raman, S. K.; Mukhopadhyay, S.; Patsa, S.; Anjum, N.; Ray, J. G.

2017-02-01

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

Rocket Design for the Future

NASA Technical Reports Server (NTRS)

Follett, William W.; Rajagopal, Raj

2001-01-01

The focus of the AA MDO team is to reduce product development cost through the capture and automation of best design and analysis practices and through increasing the availability of low-cost, high-fidelity analysis. Implementation of robust designs reduces costs associated with the Test-Fall-Fix cycle. RD is currently focusing on several technologies to improve the design process, including optimization and robust design, expert and rule-based systems, and collaborative technologies.

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.

Detection of a meteorite 'stream' - Observations of a second meteorite fall from the orbit of the Innisfree chondrite

NASA Astrophysics Data System (ADS)

Halliday, I.

1987-03-01

The first observational evidence of multiple meteorite falls from the same orbit is adduced from the February 6, 1980 fall of a meteorite precisely 3 yr after the fall of the Innisfree meteorite. Due consideration of the detection probability for two related objects with the meteorite camera network in western Canada suggests that the Innisfree brecciated LL chondrite was a near-surface fragment from a parent object whose radius was of the order of several tens of meters. A meteorite mass of 1.8 kg is predicted for the new object, whose recovery in the vicinity of Ridgedale, Saskatchewan, is now sought for the sake of comparison with the Innisfree chondrite.

Evaluating the Advisory Flags and Machine Scoring Difficulty in the "e-rater"® Automated Scoring Engine. Research Report. ETS RR-16-30

ERIC Educational Resources Information Center

Zhang, Mo; Chen, Jing; Ruan, Chunyi

2016-01-01

Successful detection of unusual responses is critical for using machine scoring in the assessment context. This study evaluated the utility of approaches to detecting unusual responses in automated essay scoring. Two research questions were pursued. One question concerned the performance of various prescreening advisory flags, and the other…

Automated synthesis, insertion and detection of polyps for CT colonography

NASA Astrophysics Data System (ADS)

Sezille, Nicolas; Sadleir, Robert J. T.; Whelan, Paul F.

2003-03-01

CT Colonography (CTC) is a new non-invasive colon imaging technique which has the potential to replace conventional colonoscopy for colorectal cancer screening. A novel system which facilitates automated detection of colorectal polyps at CTC is introduced. As exhaustive testing of such a system using real patient data is not feasible, more complete testing is achieved through synthesis of artificial polyps and insertion into real datasets. The polyp insertion is semi-automatic: candidate points are manually selected using a custom GUI, suitable points are determined automatically from an analysis of the local neighborhood surrounding each of the candidate points. Local density and orientation information are used to generate polyps based on an elliptical model. Anomalies are identified from the modified dataset by analyzing the axial images. Detected anomalies are classified as potential polyps or natural features using 3D morphological techniques. The final results are flagged for review. The system was evaluated using 15 scenarios. The sensitivity of the system was found to be 65% with 34% false positive detections. Automated diagnosis at CTC is possible and thorough testing is facilitated by augmenting real patient data with computer generated polyps. Ultimately, automated diagnosis will enhance standard CTC and increase performance.

Automated detection of case clusters of waterborne acute gastroenteritis from health insurance data - pilot study in three French districts.

PubMed

Rambaud, Loïc; Galey, Catherine; Beaudeau, Pascal

2016-04-01

This pilot study was conducted to assess the utility of using a health insurance database for the automated detection of waterborne outbreaks of acute gastroenteritis (AGE). The weekly number of AGE cases for which the patient consulted a doctor (cAGE) was derived from this database for 1,543 towns in three French districts during the 2009-2012 period. The method we used is based on a spatial comparison of incidence rates and of their time trends between the target town and the district. Each municipality was tested, week by week, for the entire study period. Overall, 193 clusters were identified, 10% of the municipalities were involved in at least one cluster and less than 2% in several. We can infer that nationwide more than 1,000 clusters involving 30,000 cases of cAGE each year may be linked to tap water. The clusters discovered with this automated detection system will be reported to local operators for investigation of the situations at highest risk. This method will be compared with others before automated detection is implemented on a national level.

Automated mitosis detection of stem cell populations in phase-contrast microscopy images.

PubMed

Huh, Seungil; Ker, Dai Fei Elmer; Bise, Ryoma; Chen, Mei; Kanade, Takeo

2011-03-01

Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

Judge Rules Plagiarism-Detection Tool Falls under "Fair Use"

ERIC Educational Resources Information Center

Young, Jeffrey R.

2008-01-01

Judge Claude M. Hilton, of the U.S. District Court in Alexandria, Virginia, in March found that scanning the student papers for the purpose of detecting plagiarism is a "highly transformative" use that falls under the fair-use provision of copyright law. He ruled that the company "makes no use of any work's particular expressive or creative…

Automated 3D-Printed Unibody Immunoarray for Chemiluminescence Detection of Cancer Biomarker Proteins

PubMed Central

Tang, C. K.; Vaze, A.; Rusling, J. F.

2017-01-01

A low cost three-dimensional (3D) printed clear plastic microfluidic device was fabricated for fast, low cost automated protein detection. The unibody device features three reagent reservoirs, an efficient 3D network for passive mixing, and an optically transparent detection chamber housing a glass capture antibody array for measuring chemiluminescence output with a CCD camera. Sandwich type assays were built onto the glass arrays using a multi-labeled detection antibody-polyHRP (HRP = horseradish peroxidase). Total assay time was ~30 min in a complete automated assay employing a programmable syringe pump so that the protocol required minimal operator intervention. The device was used for multiplexed detection of prostate cancer biomarker proteins prostate specific antigen (PSA) and platelet factor 4 (PF-4). Detection limits of 0.5 pg mL−1 were achieved for these proteins in diluted serum with log dynamic ranges of four orders of magnitude. Good accuracy vs ELISA was validated by analyzing human serum samples. This prototype device holds good promise for further development as a point-of-care cancer diagnostics tool. PMID:28067370

A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

PubMed

Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

2014-03-01

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

Automated, per pixel Cloud Detection from High-Resolution VNIR Data

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

The Johns Hopkins Fall Risk Assessment Tool: A Study of Reliability and Validity.

PubMed

Poe, Stephanie S; Dawson, Patricia B; Cvach, Maria; Burnett, Margaret; Kumble, Sowmya; Lewis, Maureen; Thompson, Carol B; Hill, Elizabeth E

Patient falls and fall-related injury remain a safety concern. The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) was developed to facilitate early detection of risk for anticipated physiologic falls in adult inpatients. Psychometric properties in acute care settings have not yet been fully established; this study sought to fill that gap. Results indicate that the JHFRAT is reliable, with high sensitivity and negative predictive validity. Specificity and positive predictive validity were lower than expected.

Automated detection of new impact sites on Martian surface from HiRISE images

NASA Astrophysics Data System (ADS)

Xin, Xin; Di, Kaichang; Wang, Yexin; Wan, Wenhui; Yue, Zongyu

2017-10-01

In this study, an automated method for Martian new impact site detection from single images is presented. It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm. Experimental results using 100 HiRISE images show that the overall detection rate of proposed method is 84.5%, with a true positive rate of 86.9%. The detection rate and true positive rate in the flat regions are 93.0% and 91.5%, respectively.

Systems and Methods for Automated Water Detection Using Visible Sensors

NASA Technical Reports Server (NTRS)

Rankin, Arturo L. (Inventor); Matthies, Larry H. (Inventor); Bellutta, Paolo (Inventor)

2016-01-01

Systems and methods are disclosed that include automated machine vision that can utilize images of scenes captured by a 3D imaging system configured to image light within the visible light spectrum to detect water. One embodiment includes autonomously detecting water bodies within a scene including capturing at least one 3D image of a scene using a sensor system configured to detect visible light and to measure distance from points within the scene to the sensor system, and detecting water within the scene using a processor configured to detect regions within each of the at least one 3D images that possess at least one characteristic indicative of the presence of water.

Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data.

PubMed

Redder, J D; Leth, R A; Møller, J K

2015-11-01

Monitoring of hospital-acquired infection (HAI) by automated compilation of registry data may address the disadvantages of laborious, costly and potentially subjective and often random sampling of data by manual surveillance. To evaluate a system for automated monitoring of hospital-acquired urinary tract (HA-UTI) and bloodstream infections (HA-BSI) and to report incidence rates over a five-year period in a Danish hospital trust. Based primarily on electronically available data relating to microbiology results and antibiotic prescriptions, the automated monitoring of HA-UTIs and HA-BSIs was validated against data from six previous point-prevalence surveys (PPS) from 2010 to 2013 and data from a manual assessment (HA-UTI only) of one department of internal medicine from January 2010. Incidence rates (infections per 1000 bed-days) from 2010 to 2014 were calculated. Compared with the PPSs, the automated monitoring showed a sensitivity of 88% in detecting UTI in general, 78% in detecting HA-UTI, and 100% in detecting BSI in general. The monthly incidence rates varied between 4.14 and 6.61 per 1000 bed-days for HA-UTI and between 0.09 and 1.25 per 1000 bed-days for HA-BSI. Replacing PPSs with automated monitoring of HAIs may provide better and more objective data and constitute a promising foundation for individual patient risk analyses and epidemiological studies. Automated monitoring may be universally applicable in hospitals with electronic databases comprising microbiological findings, admission data, and antibiotic prescriptions. Copyright © 2015 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Mechanism and DNA-based detection of field-evolved resistance to transgenic Bt corn in fall armyworm (Spodoptera frugiperda)

USDA-ARS?s Scientific Manuscript database

Evolution of resistance threatens sustainability of transgenic crops producing insecticidal proteins from the bacterium Bacillus thuringiensis (Bt). The fall armyworm is a devastating pest controlled by transgenic Bt corn producing the Cry1Fa insecticidal protein. However, fall armyworm populations ...

Wireless Falling Detection System Based on Community.

PubMed

Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

2015-06-01

The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

Pre-impact fall detection system using dynamic threshold and 3D bounding box

NASA Astrophysics Data System (ADS)

Otanasap, Nuth; Boonbrahm, Poonpong

2017-02-01

Fall prevention and detection system have to subjugate many challenges in order to develop an efficient those system. Some of the difficult problems are obtrusion, occlusion and overlay in vision based system. Other associated issues are privacy, cost, noise, computation complexity and definition of threshold values. Estimating human motion using vision based usually involves with partial overlay, caused either by direction of view point between objects or body parts and camera, and these issues have to be taken into consideration. This paper proposes the use of dynamic threshold based and bounding box posture analysis method with multiple Kinect cameras setting for human posture analysis and fall detection. The proposed work only uses two Kinect cameras for acquiring distributed values and differentiating activities between normal and falls. If the peak value of head velocity is greater than the dynamic threshold value, bounding box posture analysis will be used to confirm fall occurrence. Furthermore, information captured by multiple Kinect placed in right angle will address the skeleton overlay problem due to single Kinect. This work contributes on the fusion of multiple Kinect based skeletons, based on dynamic threshold and bounding box posture analysis which is the only research work reported so far.

Detection of falls using accelerometers and mobile phone technology.

PubMed

Lee, Raymond Y W; Carlisle, Alison J

2011-11-01

to study the sensitivity and specificity of fall detection using mobile phone technology. an experimental investigation using motion signals detected by the mobile phone. the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited. each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants. Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively. fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, Gail M.; Johnson, Benjamin T.; Munchak, S. Joseph

2013-01-01

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

A longitudinal evaluation of performance of automated BCR-ABL1 quantitation using cartridge-based detection system.

PubMed

Enjeti, Anoop; Granter, Neil; Ashraf, Asma; Fletcher, Linda; Branford, Susan; Rowlings, Philip; Dooley, Susan

2015-10-01

An automated cartridge-based detection system (GeneXpert; Cepheid) is being widely adopted in low throughput laboratories for monitoring BCR-ABL1 transcript in chronic myelogenous leukaemia. This Australian study evaluated the longitudinal performance specific characteristics of the automated system.The automated cartridge-based system was compared prospectively with the manual qRT-PCR-based reference method at SA Pathology, Adelaide, over a period of 2.5 years. A conversion factor determination was followed by four re-validations. Peripheral blood samples (n = 129) with international scale (IS) values within detectable range were selected for assessment. The mean bias, proportion of results within specified fold difference (2-, 3- and 5-fold), the concordance rate of major molecular remission (MMR) and concordance across a range of IS values on paired samples were evaluated.The initial conversion factor for the automated system was determined as 0.43. Except for the second re-validation, where a negative bias of 1.9-fold was detected, all other biases fell within desirable limits. A cartridge-specific conversion factor and efficiency value was introduced and the conversion factor was confirmed to be stable in subsequent re-validation cycles. Concordance with the reference method/laboratory at >0.1-≤10 IS was 78.2% and at ≤0.001 was 80%, compared to 86.8% in the >0.01-≤0.1 IS range. The overall and MMR concordance were 85.7% and 94% respectively, for samples that fell within ± 5-fold of the reference laboratory value over the entire period of study.Conversion factor and performance specific characteristics for the automated system were longitudinally stable in the clinically relevant range, following introduction by the manufacturer of lot specific efficiency values.

Method and automated apparatus for detecting coliform organisms

NASA Technical Reports Server (NTRS)

Dill, W. P.; Taylor, R. E.; Jeffers, E. L. (Inventor)

1980-01-01

Method and automated apparatus are disclosed for determining the time of detection of metabolically produced hydrogen by coliform bacteria cultured in an electroanalytical cell from the time the cell is inoculated with the bacteria. The detection time data provides bacteria concentration values. The apparatus is sequenced and controlled by a digital computer to discharge a spent sample, clean and sterilize the culture cell, provide a bacteria nutrient into the cell, control the temperature of the nutrient, inoculate the nutrient with a bacteria sample, measures the electrical potential difference produced by the cell, and measures the time of detection from inoculation.

Automated macromolecular crystal detection system and method

DOEpatents

Christian, Allen T [Tracy, CA; Segelke, Brent [San Ramon, CA; Rupp, Bernard [Livermore, CA; Toppani, Dominique [Fontainebleau, FR

2007-06-05

An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image.

Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.

PubMed

Karnowski, T P; Aykac, D; Giancardo, L; Li, Y; Nichols, T; Tobin, K W; Chaum, E

2011-01-01

The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

PubMed Central

2012-01-01

Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems. PMID:22336100

Preclinical Alzheimer disease and risk of falls.

PubMed

Stark, Susan L; Roe, Catherine M; Grant, Elizabeth A; Hollingsworth, Holly; Benzinger, Tammie L; Fagan, Anne M; Buckles, Virginia D; Morris, John C

2013-07-30

We determined the rate of falls among cognitively normal, community-dwelling older adults, some of whom had presumptive preclinical Alzheimer disease (AD) as detected by in vivo imaging of fibrillar amyloid plaques using Pittsburgh compound B (PiB) and PET and/or by assays of CSF to identify Aβ₄₂, tau, and phosphorylated tau. We conducted a 12-month prospective cohort study to examine the cumulative incidence of falls. Participants were evaluated clinically and underwent PiB PET imaging and lumbar puncture. Falls were reported monthly using an individualized calendar journal returned by mail. A Cox proportional hazards model was used to test whether time to first fall was associated with each biomarker and the ratio of CSF tau/Aβ₄₂ and CSF phosphorylated tau/Aβ₄₂, after adjustment for common fall risk factors. The sample (n = 125) was predominately female (62.4%) and white (96%) with a mean age of 74.4 years. When controlled for ability to perform activities of daily living, higher levels of PiB retention (hazard ratio = 2.95 [95% confidence interval 1.01-6.45], p = 0.05) and of CSF biomarker ratios (p < 0.001) were associated with a faster time to first fall. Presumptive preclinical AD is a risk factor for falls in older adults. This study suggests that subtle noncognitive changes that predispose older adults to falls are associated with AD and may precede detectable cognitive changes.

Fall Detection Devices and their Use with Older Adults: A Systematic Review

PubMed Central

Chaudhuri, Shomir; Thompson, Hilaire; Demiris, George

2013-01-01

Background Falls represent a significant threat to the health and independence of adults 65 years of age and older. As a wide variety and large amount of passive monitoring systems are currently and increasingly available to detect when an individual has fallen, there is a need to analyze and synthesize the evidence regarding their ability to accurately detect falls to determine which systems are most effective. Objectives The purpose of this literature review is to systematically assess the current state of design and implementation of fall detection devices. This review also examines the extent to which these devices have been tested in the real world as well as the acceptability of these devices to older adults. Data sources A systematic literature review was conducted in PubMed, CINAHL, EMBASE and PsycINFO from their respective inception dates to June 25, 2013. Study Eligibility Criteria and Interventions Articles were included if they discussed a project or multiple projects involving a system with the purpose of detecting a fall in adults. It was not a requirement for inclusion in this review that the system targets persons over the age of 65. Articles were excluded if they were not written in English or if they looked at fall risk, fall detection in children, fall prevention or a Personal Emergency Response device. Study appraisal and synthesis methods Studies were initially divided into those using sensitivity, specificity or accuracy in their evaluation methods, and those using other methods to evaluate their devices. Studies were further classified into wearable devices and non-wearable devices. Studies were appraised for inclusion of older adults in sample and if evaluation included real world settings. Results This review identified 57 projects that used wearable systems and 35 projects using non-wearable systems, regardless of evaluation technique. Non-wearable systems included cameras, motion sensors, microphones and floor sensors. Of the projects examining wearable systems, only 7.1% reported monitoring older adults in a real world setting. There were no studies of non-wearable devices that used older adults as subjects in either a lab or a real world setting. In general, older adults appear to be interested in using such devices although they express concerns over privacy and understanding exactly what the device is doing at specific times. Limitations This systematic review was limited to articles written in English and did not include gray literature. Manual paper screening and review processes may have been subject to interpretive bias. Conclusions and implications of key findings There exists a large body of working describing various fall detection devices. The challenge in this area is to create highly accurate unobtrusive devices. From this review it appears that the technology is becoming more able to accomplish such a task. There is a need now for more real world tests as well as standardization of the evaluation of these devices. PMID:24406708

A smart phone-based pocket fall accident detection, positioning, and rescue system.

PubMed

Kau, Lih-Jen; Chen, Chih-Sheng

2015-01-01

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

Automated Eddy Current Inspection on Space Shuttle Hardware

NASA Technical Reports Server (NTRS)

Hartmann, John; Felker, Jeremy

2007-01-01

Over the life time of the Space Shuttle program, metal parts used for the Reusable Solid Rocket Motors (RSRMs) have been nondestructively inspected for cracks and surface breaking discontinuities using magnetic particle (steel) and penetrant methods. Although these inspections adequately screened for critical sized cracks in most regions of the hardware, it became apparent after detection of several sub-critical flaws that the processes were very dependent on operator attentiveness and training. Throughout the 1990's, eddy current inspections were added to areas that had either limited visual access or were more fracture critical. In the late 1990's. a project was initiated to upgrade NDE inspections with the overall objective of improving inspection reliability and control. An automated eddy current inspection system was installed in 2001. A figure shows one of the inspection bays with the robotic axis of the system highlighted. The system was programmed to inspect the various case, nozzle, and igniter metal components that make up an RSRM. both steel and aluminum. For the past few years, the automated inspection system has been a part of the baseline inspection process for steel components. Although the majority of the RSRM metal part inventory ts free of detectable surface flaws, a few small, sub-critical manufacturing defects have been detected with the automated system. This paper will summarize the benefits that have been realized with the current automated eddy current system, as well as the flaws that have been detected.

ClinicalTrials.gov as a data source for semi-automated point-of-care trial eligibility screening.

PubMed

Pfiffner, Pascal B; Oh, JiWon; Miller, Timothy A; Mandl, Kenneth D

2014-01-01

Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies. To evaluate the utility of the ClinicalTrials.gov registry as a data source for semi-automated trial eligibility screening. Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in ClinicalTrials.gov. Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status. 24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients. Because ClinicalTrials.gov has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting ClinicalTrials.gov data entry forms to capture key eligibility criteria in a simple, structured format.

Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability.

PubMed

Chavaillaz, Alain; Schwaninger, Adrian; Michel, Stefan; Sauer, Juergen

2018-05-25

The present study evaluated three automation modes for improving performance in an X-ray luggage screening task. 140 participants were asked to detect the presence of prohibited items in X-ray images of cabin luggage. Twenty participants conducted this task without automatic support (control group), whereas the others worked with either indirect cues (system indicated the target presence without specifying its location), or direct cues (system pointed out the exact target location) or adaptable automation (participants could freely choose between no cue, direct and indirect cues). Furthermore, automatic support reliability was manipulated (low vs. high). The results showed a clear advantage for direct cues regarding detection performance and response time. No benefits were observed for adaptable automation. Finally, high automation reliability led to better performance and higher operator trust. The findings overall confirmed that automatic support systems for luggage screening should be designed such that they provide direct, highly reliable cues.

Signal amplification of FISH for automated detection using image cytometry.

PubMed

Truong, K; Boenders, J; Maciorowski, Z; Vielh, P; Dutrillaux, B; Malfoy, B; Bourgeois, C A

1997-05-01

The purpose of this study was to improve the detection of FISH signals, in order that spot counting by a fully automated image cytometer be comparable to that obtained visually under the microscope. Two systems of spot scoring, visual and automated counting, were investigated in parallel on stimulated human lymphocytes with FISH using a biotinylated centromeric probe for chromosome 3. Signal characteristics were first analyzed on images recorded with a coupled charge device (CCD) camera. Number of spots per nucleus were scored visually on these recorded images versus automatically with a DISCOVERY image analyzer. Several fluochromes, amplification and pretreatments were tested. Our results for both visual and automated scoring show that the tyramide amplification system (TSA) gives the best amplification of signal if pepsin treatment is applied prior to FISH. Accuracy of the automated scoring, however, remained low (58% of nuclei containing two spots) compared to the visual scoring because of the high intranuclear variation between FISH spots.

Automated Detection and Classification in High-Resolution Sonar Imagery for Autonomous Underwater Vehicle Operations

DTIC Science & Technology

2008-12-01

n. , ’>, ,. Australian Government Department of Defence Defence Science and Technology Organisation Automated Detection and Classification in... Organisation DSTO-GD-0537 ABSTRACT Autonomous Underwater Vehicles (AUVs) are increasingly being used by military forces to acquire high-resolution sonar...release Published by Maritime Operations Division DsTO Defrnce sdence and Technology Organisation PO Box 1500 Edinburgh South Australia 5111 Australia

Automated Detection of Solar Loops by the Oriented Connectivity Method

NASA Technical Reports Server (NTRS)

Lee, Jong Kwan; Newman, Timothy S.; Gary, G. Allen

2004-01-01

An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

The integration of automated knowledge acquisition with computer-aided software engineering for space shuttle expert systems

NASA Technical Reports Server (NTRS)

Modesitt, Kenneth L.

1990-01-01

A prediction was made that the terms expert systems and knowledge acquisition would begin to disappear over the next several years. This is not because they are falling into disuse; it is rather that practitioners are realizing that they are valuable adjuncts to software engineering, in terms of problem domains addressed, user acceptance, and in development methodologies. A specific problem was discussed, that of constructing an automated test analysis system for the Space Shuttle Main Engine. In this domain, knowledge acquisition was part of requirements systems analysis, and was performed with the aid of a powerful inductive ESBT in conjunction with a computer aided software engineering (CASE) tool. The original prediction is not a very risky one -- it has already been accomplished.

SU-G-201-03: Automation of High Dose Rate Brachytherapy Quality Assurance: Development of a Radioluminescent Detection System for Simultaneous Detection of Activity, Timing, and Positioning

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

Jenkins, C; Xing, L; Fahimian, B

Purpose: Accuracy of positioning, timing and activity is of critical importance for High Dose Rate (HDR) brachytherapy delivery. Respective measurements via film autoradiography, stop-watches and well chambers can be cumbersome, crude or lack dynamic source evaluation capabilities. To address such limitations, a single device radioluminescent detection system enabling automated real-time quantification of activity, position and timing accuracy is presented and experimentally evaluated. Methods: A radioluminescent sheet was fabricated by mixing Gd?O?S:Tb with PDMS and incorporated into a 3D printed device where it was fixated below a CMOS digital camera. An Ir-192 HDR source (VS2000, VariSource iX) with an effective activemore » length of 5 mm was introduced using a 17-gauge stainless steel needle below the sheet. Pixel intensity values for determining activity were taken from an ROI centered on the source location. A calibration curve relating intensity values to activity was generated and used to evaluate automated activity determination with data gathered over 6 weeks. Positioning measurements were performed by integrating images for an entire delivery and fitting peaks to the resulting profile. Timing measurements were performed by evaluating source location and timestamps from individual images. Results: Average predicted activity error over 6 weeks was .35 ± .5%. The distance between four dwell positions was determined by the automated system to be 1.99 ± .02 cm. The result from autoradiography was 2.00 ± .03 cm. The system achieved a time resolution of 10 msec and determined the dwell time to be 1.01 sec ± .02 sec. Conclusion: The system was able to successfully perform automated detection of activity, positioning and timing concurrently under a single setup. Relative to radiochromic and radiographic film-based autoradiography, which can only provide a static evaluation positioning, optical detection of temporary radiation induced luminescence enables dynamic detection of position enabling automated quantification of timing with millisecond accuracy.« less

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Wang, Xingwei; Chen, Xiaodong; Li, Yuhua; Liu, Hong; Li, Shibo; Zheng, Bin

2010-02-01

Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of applying this CAD-guided high-resolution microscopic image scanning system to prescreen and select ROIs that may contain analyzable metaphase chromosome cells. The success and the further improvement of this automated scanning system may have great impact on the future clinical practice in genetic laboratories to detect and diagnose diseases.

Automated Image Analysis Corrosion Working Group Update: February 1, 2018

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

Wendelberger, James G.

These are slides for the automated image analysis corrosion working group update. The overall goals were: automate the detection and quantification of features in images (faster, more accurate), how to do this (obtain data, analyze data), focus on Laser Scanning Confocal Microscope (LCM) data (laser intensity, laser height/depth, optical RGB, optical plus laser RGB).

Open-source software for collision detection in external beam radiation therapy

NASA Astrophysics Data System (ADS)

Suriyakumar, Vinith M.; Xu, Renee; Pinter, Csaba; Fichtinger, Gabor

2017-03-01

PURPOSE: Collision detection for external beam radiation therapy (RT) is important for eliminating the need for dryruns that aim to ensure patient safety. Commercial treatment planning systems (TPS) offer this feature but they are expensive and proprietary. Cobalt-60 RT machines are a viable solution to RT practice in low-budget scenarios. However, such clinics are hesitant to invest in these machines due to a lack of affordable treatment planning software. We propose the creation of an open-source room's eye view visualization module with automated collision detection as part of the development of an open-source TPS. METHODS: An openly accessible linac 3D geometry model is sliced into the different components of the treatment machine. The model's movements are based on the International Electrotechnical Commission standard. Automated collision detection is implemented between the treatment machine's components. RESULTS: The room's eye view module was built in C++ as part of SlicerRT, an RT research toolkit built on 3D Slicer. The module was tested using head and neck and prostate RT plans. These tests verified that the module accurately modeled the movements of the treatment machine and radiation beam. Automated collision detection was verified using tests where geometric parameters of the machine's components were changed, demonstrating accurate collision detection. CONCLUSION: Room's eye view visualization and automated collision detection are essential in a Cobalt-60 treatment planning system. Development of these features will advance the creation of an open-source TPS that will potentially help increase the feasibility of adopting Cobalt-60 RT.

Effects of response bias and judgment framing on operator use of an automated aid in a target detection task.

PubMed

Rice, Stephen; McCarley, Jason S

2011-12-01

Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in operators' cognitive responses to different forms of automation error. The present experiments therefore examined the effects of automation false alarms and misses on human performance under conditions in which the different forms of error were matched in their perceptual characteristics. Young adult participants performed a simulated baggage x-ray screening task while assisted by an automated diagnostic aid. Judgments from the aid were rendered as text messages presented at the onset of each trial, and every trial was followed by a second text message providing response feedback. Thus, misses and false alarms from the aid were matched for their perceptual salience. Experiment 1 found that even under these conditions, false alarms from the aid produced poorer human performance and engendered lower automation use than misses from the aid. Experiment 2, however, found that the asymmetry between misses and false alarms was reduced when the aid's false alarms were framed as neutral messages rather than explicit misjudgments. Results suggest that automation false alarms and misses differ in their inherent cognitive salience and imply that changes in diagnosis framing may allow designers to encourage better use of imperfectly reliable automated aids.

Older adults' perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review.

PubMed

Hawley-Hague, Helen; Boulton, Elisabeth; Hall, Alex; Pfeiffer, Klaus; Todd, Chris

2014-06-01

Over recent years a number of Information and Communication Technologies (ICTs) have emerged aiming at falls prevention, falls detection and alarms for use in case of fall. There are also a range of ICT interventions, which have been created or adapted to be pro-active in preventing falls, such as those which provide strength and balance training to older adults in the prevention of falls. However, there are issues related to the adoption and continued use of these technologies by older adults. This review provides an overview of older adults' perceptions of falls technologies. We undertook systematic searches of MEDLINE, EMBASE, CINAHL and PsychINFO, COMPENDEX and the Cochrane database. Key search terms included 'older adults', 'seniors', 'preference', 'attitudes' and a wide range of technologies, they also included the key word 'fall*'. We considered all studies that included older adults aged 50 and above. Studies had to include technologies related specifically to falls prevention, detection or monitoring. The Joanna Briggs Institute (JBI) tool and the Quality Assessment Tool for Quantitative Studies by the Effective Public Health Practice Project (EPHPP) were used. We identified 76 potentially relevant papers. Some 21 studies were considered for quality review. Twelve qualitative studies, three quantitative studies and 6 mixed methods studies were included. The literature related to technologies aimed at predicting, monitoring and preventing falls suggest that intrinsic factors related to older adults' attitudes around control, independence and perceived need/requirements for safety are important for their motivation to use and continue using technologies. Extrinsic factors such as usability, feedback gained and costs are important elements which support these attitudes and perceptions. Positive messages about the benefits of falls technologies for promoting healthy active ageing and independence are critical, as is ensuring that the technologies are simple, reliable and effective and tailored to individual need. The technologies need to be clearly described in research and older peoples' attitudes towards different sorts of technologies must be clarified if specific recommendations are to be made. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Automation of diagnostic genetic testing: mutation detection by cyclic minisequencing.

PubMed

Alagrund, Katariina; Orpana, Arto K

2014-01-01

The rising role of nucleic acid testing in clinical decision making is creating a need for efficient and automated diagnostic nucleic acid test platforms. Clinical use of nucleic acid testing sets demands for shorter turnaround times (TATs), lower production costs and robust, reliable methods that can easily adopt new test panels and is able to run rare tests in random access principle. Here we present a novel home-brew laboratory automation platform for diagnostic mutation testing. This platform is based on the cyclic minisequecing (cMS) and two color near-infrared (NIR) detection. Pipetting is automated using Tecan Freedom EVO pipetting robots and all assays are performed in 384-well micro plate format. The automation platform includes a data processing system, controlling all procedures, and automated patient result reporting to the hospital information system. We have found automated cMS a reliable, inexpensive and robust method for nucleic acid testing for a wide variety of diagnostic tests. The platform is currently in clinical use for over 80 mutations or polymorphisms. Additionally to tests performed from blood samples, the system performs also epigenetic test for the methylation of the MGMT gene promoter, and companion diagnostic tests for analysis of KRAS and BRAF gene mutations from formalin fixed and paraffin embedded tumor samples. Automation of genetic test reporting is found reliable and efficient decreasing the work load of academic personnel.

Chest wall segmentation in automated 3D breast ultrasound scans.

PubMed

Tan, Tao; Platel, Bram; Mann, Ritse M; Huisman, Henkjan; Karssemeijer, Nico

2013-12-01

In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

PubMed Central

Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

2017-01-01

Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management. PMID:28338047

Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

NASA Astrophysics Data System (ADS)

Seymour, A. C.; Dale, J.; Hammill, M.; Halpin, P. N.; Johnston, D. W.

2017-03-01

Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95-98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

Automated gas chromatography

DOEpatents

Mowry, C.D.; Blair, D.S.; Rodacy, P.J.; Reber, S.D.

1999-07-13

An apparatus and process for the continuous, near real-time monitoring of low-level concentrations of organic compounds in a liquid, and, more particularly, a water stream. A small liquid volume of flow from a liquid process stream containing organic compounds is diverted by an automated process to a heated vaporization capillary where the liquid volume is vaporized to a gas that flows to an automated gas chromatograph separation column to chromatographically separate the organic compounds. Organic compounds are detected and the information transmitted to a control system for use in process control. Concentrations of organic compounds less than one part per million are detected in less than one minute. 7 figs.

PleurAlert: an augmented chest drainage system with electronic sensing, automated alerts and internet connectivity.

PubMed

Leeson, Cory E; Weaver, Robert A; Bissell, Taylor; Hoyer, Rachel; McClain, Corinne; Nelson, Douglas A; Samosky, Joseph T

2012-01-01

We have enhanced a common medical device, the chest tube drainage container, with electronic sensing of fluid volume, automated detection of critical alarm conditions and the ability to automatically send alert text messages to a nurse's cell phone. The PleurAlert system provides a simple touch-screen interface and can graphically display chest tube output over time. Our design augments a device whose basic function dates back 50 years by adding technology to automate and optimize a monitoring process that can be time consuming and inconvenient for nurses. The system may also enhance detection of emergency conditions and speed response time.

Automated gas chromatography

DOEpatents

Mowry, Curtis D.; Blair, Dianna S.; Rodacy, Philip J.; Reber, Stephen D.

1999-01-01

An apparatus and process for the continuous, near real-time monitoring of low-level concentrations of organic compounds in a liquid, and, more particularly, a water stream. A small liquid volume of flow from a liquid process stream containing organic compounds is diverted by an automated process to a heated vaporization capillary where the liquid volume is vaporized to a gas that flows to an automated gas chromatograph separation column to chromatographically separate the organic compounds. Organic compounds are detected and the information transmitted to a control system for use in process control. Concentrations of organic compounds less than one part per million are detected in less than one minute.

Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

PubMed

Scotland, G S; McNamee, P; Fleming, A D; Goatman, K A; Philip, S; Prescott, G J; Sharp, P F; Williams, G J; Wykes, W; Leese, G P; Olson, J A

2010-06-01

To assess the cost-effectiveness of an improved automated grading algorithm for diabetic retinopathy against a previously described algorithm, and in comparison with manual grading. Efficacy of the alternative algorithms was assessed using a reference graded set of images from three screening centres in Scotland (1253 cases with observable/referable retinopathy and 6333 individuals with mild or no retinopathy). Screening outcomes and grading and diagnosis costs were modelled for a cohort of 180 000 people, with prevalence of referable retinopathy at 4%. Algorithm (b), which combines image quality assessment with detection algorithms for microaneurysms (MA), blot haemorrhages and exudates, was compared with a simpler algorithm (a) (using image quality assessment and MA/dot haemorrhage (DH) detection), and the current practice of manual grading. Compared with algorithm (a), algorithm (b) would identify an additional 113 cases of referable retinopathy for an incremental cost of pound 68 per additional case. Compared with manual grading, automated grading would be expected to identify between 54 and 123 fewer referable cases, for a grading cost saving between pound 3834 and pound 1727 per case missed. Extrapolation modelling over a 20-year time horizon suggests manual grading would cost between pound 25,676 and pound 267,115 per additional quality adjusted life year gained. Algorithm (b) is more cost-effective than the algorithm based on quality assessment and MA/DH detection. With respect to the value of introducing automated detection systems into screening programmes, automated grading operates within the recommended national standards in Scotland and is likely to be considered a cost-effective alternative to manual disease/no disease grading.

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

PubMed

Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S

2018-05-01

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001). The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

A self-adapting system for the automated detection of inter-ictal epileptiform discharges.

PubMed

Lodder, Shaun S; van Putten, Michel J A M

2014-01-01

Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search. Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form "IED nominations", each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections. Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20-30 min recordings 1 took approximately 5 min. The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.

[Intelligent videosurveillance and falls detection: Perceptions of professionals and managers].

PubMed

Lapierre, Nolwenn; Carpentier, Isabelle; St-Arnaud, Alain; Ducharme, Francine; Meunier, Jean; Jobidon, Mireille; Rousseau, Jacqueline

2016-02-01

Gerontechnologies can be used to detect accidental falls. However, existing systems do not entirely meet users' expectations. Our team developed an intelligent video-monitoring systems to fill these gaps. Authors advocate consulting potential users at the early stages of the design of gerontechnologies and integrating their suggestions. This study aims to explore health care workers' opinion regarding the intelligent video monitoring to detect falls by older adults living at home. This qualitative study explored the opinions of 31 participants using focus groups. Transcripts were analyzed using predetermined codes based on the competence model. Participants reported several advantages for using the intelligent video monitoring and provided suggestions for improving its use. The participants' suggestions and comments will help to improve the system and match it to users' needs. © CAOT 2015.

Strategic Studies Quarterly. Volume 6, Number 3. Fall 2012

DTIC Science & Technology

2012-01-01

a particular TOR site, the number of Iranian users on the network drops precipitously. It picks up again after TOR developers announce a workaround...benefit, hence value of the network , is then proportional to the area under the curve or natural log of N (lnN). The increase (decrease) in network ...necessary sensors and automation to strengthen and de­ fend network operations at the scale required for a global industry or military operations

Fall 2014 Data-Intensive Systems

DTIC Science & Technology

2014-10-29

Oct 2014 © 2014 Carnegie Mellon University Big Data Systems NoSQL and horizontal scaling are changing architecture principles by creating...University Status LEAP4BD • Ready to pilot QuABase • Prototype is complete – covers 8 NoSQL /NewSQL implementations • Completing validation testing Big...machine learning to automate population of knowledge base • Initial focus on NoSQL /NewSQL technology domain • Extend to create knowledge bases in other

Automated indirect immunofluorescence evaluation of antinuclear autoantibodies on HEp-2 cells.

PubMed

Voigt, Jörn; Krause, Christopher; Rohwäder, Edda; Saschenbrecker, Sandra; Hahn, Melanie; Danckwardt, Maick; Feirer, Christian; Ens, Konstantin; Fechner, Kai; Barth, Erhardt; Martinetz, Thomas; Stöcker, Winfried

2012-01-01

Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability.

Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells

PubMed Central

Voigt, Jörn; Krause, Christopher; Rohwäder, Edda; Saschenbrecker, Sandra; Hahn, Melanie; Danckwardt, Maick; Feirer, Christian; Ens, Konstantin; Fechner, Kai; Barth, Erhardt; Martinetz, Thomas; Stöcker, Winfried

2012-01-01

Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability. PMID:23251220

An automated optofluidic biosensor platform combining interferometric sensors and injection moulded microfluidics.

PubMed

Szydzik, C; Gavela, A F; Herranz, S; Roccisano, J; Knoerzer, M; Thurgood, P; Khoshmanesh, K; Mitchell, A; Lechuga, L M

2017-08-08

A primary limitation preventing practical implementation of photonic biosensors within point-of-care platforms is their integration with fluidic automation subsystems. For most diagnostic applications, photonic biosensors require complex fluid handling protocols; this is especially prominent in the case of competitive immunoassays, commonly used for detection of low-concentration, low-molecular weight biomarkers. For this reason, complex automated microfluidic systems are needed to realise the full point-of-care potential of photonic biosensors. To fulfil this requirement, we propose an on-chip valve-based microfluidic automation module, capable of automating such complex fluid handling. This module is realised through application of a PDMS injection moulding fabrication technique, recently described in our previous work, which enables practical fabrication of normally closed pneumatically actuated elastomeric valves. In this work, these valves are configured to achieve multiplexed reagent addressing for an on-chip diaphragm pump, providing the sample and reagent processing capabilities required for automation of cyclic competitive immunoassays. Application of this technique simplifies fabrication and introduces the potential for mass production, bringing point-of-care integration of complex automated microfluidics into the realm of practicality. This module is integrated with a highly sensitive, label-free bimodal waveguide photonic biosensor, and is demonstrated in the context of a proof-of-concept biosensing assay, detecting the low-molecular weight antibiotic tetracycline.

Are triage questions sufficient to assign fall risk precautions in the ED?

PubMed

Southerland, Lauren T; Slattery, Lauren; Rosenthal, Joseph A; Kegelmeyer, Deborah; Kloos, Anne

2017-02-01

The American College of Emergency Physicians Geriatric Emergency Department (ED) Guidelines and the Center for Disease Control recommend that older adults be assessed for risk of falls. The standard ED assessment is a verbal query of fall risk factors, which may be inadequate. We hypothesized that the addition of a functional balance test endorsed by the Center for Disease Control Stop Elderly Accidents, Deaths, and Injuries Falls Prevention Guidelines, the 4-Stage Balance Test (4SBT), would improve the detection of patients at risk for falls. Prospective pilot study of a convenience sample of ambulatory adults 65 years and older in the ED. All participants received the standard nursing triage fall risk assessment. After patients were stabilized in their ED room, the 4SBT was administered. The 58 participants had an average age of 74.1 years (range, 65-94), 40.0% were women, and 98% were community dwelling. Five (8.6%) presented to the ED for a fall-related chief complaint. The nursing triage screen identified 39.7% (n=23) as at risk for falls, whereas the 4SBT identified 43% (n=25). Combining triage questions with the 4SBT identified 60.3% (n=35) as at high risk for falls, as compared with 39.7% (n=23) with triage questions alone (P<.01). Ten (17%) of the patients at high risk by 4SBT and missed by triage questions were inpatients unaware that they were at risk for falls (new diagnoses). Incorporating a quick functional test of balance into the ED assessment for fall risk is feasible and significantly increases the detection of older adults at risk for falls. Copyright © 2016 Elsevier Inc. All rights reserved.

Automated acoustic analysis in detection of spontaneous swallows in Parkinson's disease.

PubMed

Golabbakhsh, Marzieh; Rajaei, Ali; Derakhshan, Mahmoud; Sadri, Saeed; Taheri, Masoud; Adibi, Peyman

2014-10-01

Acoustic monitoring of swallow frequency has become important as the frequency of spontaneous swallowing can be an index for dysphagia and related complications. In addition, it can be employed as an objective quantification of ingestive behavior. Commonly, swallowing complications are manually detected using videofluoroscopy recordings, which require expensive equipment and exposure to radiation. In this study, a noninvasive automated technique is proposed that uses breath and swallowing recordings obtained via a microphone located over the laryngopharynx. Nonlinear diffusion filters were used in which a scale-space decomposition of recorded sound at different levels extract swallows from breath sounds and artifacts. This technique was compared to manual detection of swallows using acoustic signals on a sample of 34 subjects with Parkinson's disease. A speech language pathologist identified five subjects who showed aspiration during the videofluoroscopic swallowing study. The proposed automated method identified swallows with a sensitivity of 86.67 %, a specificity of 77.50 %, and an accuracy of 82.35 %. These results indicate the validity of automated acoustic recognition of swallowing as a fast and efficient approach to objectively estimate spontaneous swallow frequency.

Automated vehicle for railway track fault detection

NASA Astrophysics Data System (ADS)

Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

2017-11-01

For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

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.

Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.

PubMed

Ulrich, Ethan J; Sunderland, John J; Smith, Brian J; Mohiuddin, Imran; Parkhurst, Jessica; Plichta, Kristin A; Buatti, John M; Beichel, Reinhard R

2018-01-01

Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R 2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ≪ 0.001) compared to manual analysis (mean: 247.8 s). The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts. © 2017 American Association of Physicists in Medicine.

Performance of Copan WASP for Routine Urine Microbiology

PubMed Central

Quiblier, Chantal; Jetter, Marion; Rominski, Mark; Mouttet, Forouhar; Böttger, Erik C.; Keller, Peter M.

2015-01-01

This study compared a manual workup of urine clinical samples with fully automated WASPLab processing. As a first step, two different inocula (1 and 10 μl) and different streaking patterns were compared using WASP and InoqulA BT instrumentation. Significantly more single colonies were produced with the10-μl inoculum than with the 1-μl inoculum, and automated streaking yielded significantly more single colonies than manual streaking on whole plates (P < 0.001). In a second step, 379 clinical urine samples were evaluated using WASP and the manual workup. Average numbers of detected morphologies, recovered species, and CFUs per milliliter of all 379 urine samples showed excellent agreement between WASPLab and the manual workup. The percentage of urine samples clinically categorized as positive or negative did not differ between the automated and manual workflow, but within the positive samples, automated processing by WASPLab resulted in the detection of more potential pathogens. In summary, the present study demonstrates that (i) the streaking pattern, i.e., primarily the number of zigzags/length of streaking lines, is critical for optimizing the number of single colonies yielded from primary cultures of urine samples; (ii) automated streaking by the WASP instrument is superior to manual streaking regarding the number of single colonies yielded (for 32.2% of the samples); and (iii) automated streaking leads to higher numbers of detected morphologies (for 47.5% of the samples), species (for 17.4% of the samples), and pathogens (for 3.4% of the samples). The results of this study point to an improved quality of microbiological analyses and laboratory reports when using automated sample processing by WASP and WASPLab. PMID:26677255

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

PubMed

Liu, Li; Gao, Simon S; Bailey, Steven T; Huang, David; Li, Dengwang; Jia, Yali

2015-09-01

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

Application of Novel Software Algorithms to Spectral-Domain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy.

PubMed

Adhi, Mehreen; Semy, Salim K; Stein, David W; Potter, Daniel M; Kuklinski, Walter S; Sleeper, Harry A; Duker, Jay S; Waheed, Nadia K

2016-05-01

To present novel software algorithms applied to spectral-domain optical coherence tomography (SD-OCT) for automated detection of diabetic retinopathy (DR). Thirty-one diabetic patients (44 eyes) and 18 healthy, nondiabetic controls (20 eyes) who underwent volumetric SD-OCT imaging and fundus photography were retrospectively identified. A retina specialist independently graded DR stage. Trained automated software generated a retinal thickness score signifying macular edema and a cluster score signifying microaneurysms and/or hard exudates for each volumetric SD-OCT. Of 44 diabetic eyes, 38 had DR and six eyes did not have DR. Leave-one-out cross-validation using a linear discriminant at missed detection/false alarm ratio of 3.00 computed software sensitivity and specificity of 92% and 69%, respectively, for DR detection when compared to clinical assessment. Novel software algorithms applied to commercially available SD-OCT can successfully detect DR and may have potential as a viable screening tool for DR in future. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.]. Copyright 2016, SLACK Incorporated.

Decision support system for the detection and grading of hard exudates from color fundus photographs

NASA Astrophysics Data System (ADS)

Jaafar, Hussain F.; Nandi, Asoke K.; Al-Nuaimy, Waleed

2011-11-01

Diabetic retinopathy is a major cause of blindness, and its earliest signs include damage to the blood vessels and the formation of lesions in the retina. Automated detection and grading of hard exudates from the color fundus image is a critical step in the automated screening system for diabetic retinopathy. We propose novel methods for the detection and grading of hard exudates and the main retinal structures. For exudate detection, a novel approach based on coarse-to-fine strategy and a new image-splitting method are proposed with overall sensitivity of 93.2% and positive predictive value of 83.7% at the pixel level. The average sensitivity of the blood vessel detection is 85%, and the success rate of fovea localization is 100%. For exudate grading, a polar fovea coordinate system is adopted in accordance with medical criteria. Because of its competitive performance and ability to deal efficiently with images of variable quality, the proposed technique offers promising and efficient performance as part of an automated screening system for diabetic retinopathy.

Acoustic-sensor-based detection of damage in composite aircraft structures

NASA Astrophysics Data System (ADS)

Foote, Peter; Martin, Tony; Read, Ian

2004-03-01

Acoustic emission detection is a well-established method of locating and monitoring crack development in metal structures. The technique has been adapted to test facilities for non-destructive testing applications. Deployment as an operational or on-line automated damage detection technology in vehicles is posing greater challenges. A clear requirement of potential end-users of such systems is a level of automation capable of delivering low-level diagnosis information. The output from the system is in the form of "go", "no-go" indications of structural integrity or immediate maintenance actions. This level of automation requires significant data reduction and processing. This paper describes recent trials of acoustic emission detection technology for the diagnosis of damage in composite aerospace structures. The technology comprises low profile detection sensors using piezo electric wafers encapsulated in polymer film ad optical sensors. Sensors are bonded to the structure"s surface and enable acoustic events from the loaded structure to be located by triangulation. Instrumentation has been enveloped to capture and parameterise the sensor data in a form suitable for low-bandwidth storage and transmission.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

Wavelet Analysis of SAR Images for Coastal Monitoring

NASA Technical Reports Server (NTRS)

Liu, Antony K.; Wu, Sunny Y.; Tseng, William Y.; Pichel, William G.

1998-01-01

The mapping of mesoscale ocean features in the coastal zone is a major potential application for satellite data. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ice edge can be tracked by the wavelet analysis using satellite data from repeating paths. The wavelet transform has been applied to satellite images, such as those from Synthetic Aperture Radar (SAR), Advanced Very High-Resolution Radiometer (AVHRR), and ocean color sensor for feature extraction. In this paper, algorithms and techniques for automated detection and tracking of mesoscale features from satellite SAR imagery employing wavelet analysis have been developed. Case studies on two major coastal oil spills have been investigated using wavelet analysis for tracking along the coast of Uruguay (February 1997), and near Point Barrow, Alaska (November 1997). Comparison of SAR images with SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data for coccolithophore bloom in the East Bering Sea during the fall of 1997 shows a good match on bloom boundary. This paper demonstrates that this technique is a useful and promising tool for monitoring of coastal waters.

Understanding human management of automation errors

PubMed Central

McBride, Sara E.; Rogers, Wendy A.; Fisk, Arthur D.

2013-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance. PMID:25383042

Automation Bias: Decision Making and Performance in High-Tech Cockpits

NASA Technical Reports Server (NTRS)

Mosier, Kathleen L.; Skitka, Linda J.; Heers, Susan; Burdick, Mark; Rosekind, Mark R. (Technical Monitor)

1997-01-01

Automated aids and decision support tools are rapidly becoming indispensible tools in high-technology cockpits, and are assuming increasing control of "cognitive" flight tasks, such as calculating fuel-efficient routes, navigating, or detecting and diagnosing system malfunctions and abnormalities. This study was designed to investigate "automation bias," a recently documented factor in the use of automated aids and decision support systems. The term refers to omission and commission errors resulting from the use of automated cues as a heuristic replacement for vigilant information seeking and processing. Glass-cockpit pilots flew flight scenarios involving automation "events," or opportunities for automation-related omission and commission errors. Pilots who perceived themselves as "accountable" for their performance and strategies of interaction with the automation were more likely to double-check automated functioning against other cues, and less likely to commit errors. Pilots were also likely to erroneously "remember" the presence of expected cues when describing their decision-making processes.

Understanding human management of automation errors.

PubMed

McBride, Sara E; Rogers, Wendy A; Fisk, Arthur D

2014-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance.

Applied Actant Network Theory: Toward the Automated Detection of Technoscientific Emergence from Full Text Publications and Patents (Open Access)

DTIC Science & Technology

2012-11-02

Applied Actant-Network Theory: Toward the Automated Detection of Technoscientific Emergence from Full-Text Publications and Patents David C...Brock**, Olga Babko-Malaya*, James Pustejovsky***, Patrick Thomas****, *BAE Systems Advanced Information Technologies, ** David C. Brock Consulting... Wojick , D. 2008. Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3):495–518. Cook, T. D. and

Long-Term Pavement Performance Automated Faulting Measurement

DOT National Transportation Integrated Search

2015-02-01

This study focused on identifying transverse joint locations on jointed plain concrete pavements using an automated joint detection algorithm and computing faulting at these locations using Long-Term Pavement Performance (LTPP) Program profile data c...

Advanced Technologies and Methodology for Automated Ultrasonic Testing Systems Quantification

DOT National Transportation Integrated Search

2011-04-29

For automated ultrasonic testing (AUT) detection and sizing accuracy, this program developed a methodology for quantification of AUT systems, advancing and quantifying AUT systems imagecapture capabilities, quantifying the performance of multiple AUT...

Embedded DSP-based telehealth radar system for remote in-door fall detection.

PubMed

Garripoli, Carmine; Mercuri, Marco; Karsmakers, Peter; Jack Soh, Ping; Crupi, Giovanni; Vandenbosch, Guy A E; Pace, Calogero; Leroux, Paul; Schreurs, Dominique

2015-01-01

Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women) that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects' weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar's operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas.

Automated Coding Software: Development and Use to Enhance Anti-Fraud Activities*

PubMed Central

Garvin, Jennifer H.; Watzlaf, Valerie; Moeini, Sohrab

2006-01-01

This descriptive research project identified characteristics of automated coding systems that have the potential to detect improper coding and to minimize improper or fraudulent coding practices in the setting of automated coding used with the electronic health record (EHR). Recommendations were also developed for software developers and users of coding products to maximize anti-fraud practices. PMID:17238546

Automated detection of extradural and subdural hematoma for contrast-enhanced CT images in emergency medical care

NASA Astrophysics Data System (ADS)

Hara, Takeshi; Matoba, Naoto; Zhou, Xiangrong; Yokoi, Shinya; Aizawa, Hiroaki; Fujita, Hiroshi; Sakashita, Keiji; Matsuoka, Tetsuya

2007-03-01

We have been developing the CAD scheme for head and abdominal injuries for emergency medical care. In this work, we have developed an automated method to detect typical head injuries, rupture or strokes of brain. Extradural and subdural hematoma region were detected by comparing technique after the brain areas were registered using warping. We employ 5 normal and 15 stroke cases to estimate the performance after creating the brain model with 50 normal cases. Some of the hematoma regions were detected correctly in all of the stroke cases with no false positive findings on normal cases.

Filament Chirality over an Entire Cycle Determined with an Automated Detection Module -- a Neat Surprise!

NASA Astrophysics Data System (ADS)

Martens, Petrus C.; Yeates, A. R.; Mackay, D.; Pillai, K. G.

2013-07-01

Using metadata produced by automated solar feature detection modules developed for SDO (Martens et al. 2012) we have discovered some trends in filament chirality and filament-sigmoid relations that are new and in part contradict the current consensus. Automated detection of solar features has the advantage over manual detection of having the detection criteria applied consistently, and in being able to deal with enormous amounts of data, like the 1 Terabyte per day that SDO produces. Here we use the filament detection module developed by Bernasconi, which has metadata from 2000 on, and the sigmoid sniffer, which has been producing metadata from AIA 94 A images since October 2011. The most interesting result we find is that the hemispheric chirality preference for filaments (dextral in the north, and v.v.), studied in detail for a three year period by Pevtsov et al. (2003) seems to disappear during parts of the decline of cycle 23 and during the extended solar minimum that followed. Moreover the hemispheric chirality rule seems to be much less pronounced during the onset of cycle 24. For sigmoids we find the expected correlation between chirality and handedness (S or Z) shape but not as strong as expected.

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

NASA Astrophysics Data System (ADS)

Amrute, Junedh M.; Athanasiou, Lambros S.; Rikhtegar, Farhad; de la Torre Hernández, José M.; Camarero, Tamara García; Edelman, Elazer R.

2018-03-01

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

Fall prevention: is the STRATIFY tool the right instrument in Italian Hospital inpatient? A retrospective observational study.

PubMed

Castellini, Greta; Demarchi, Antonia; Lanzoni, Monica; Castaldi, Silvana

2017-09-15

Although several risk assessment tools are in use, uncertainties on their accuracy in detecting fall risk already exist. Choosing the most accurate tool for hospital inpatient is still a challenge for the organizations. We aimed to retrospectively assess the appropriateness of a fall risk prevention program with the STRATIFY assessment tool in detecting acute-care inpatient fall risk. Number of falls and near falls, occurred from January 2014 to March 2015, was collected through the incident reporting web-system implemented in the hospital's intranet. We reported whether the fall risk was assessed with the STRATIFY assessment tool and, if so, which was the judgement. Primary outcome was the proportion of inpatients identified as high risk of fall among inpatients who fell (True Positive Rate), and the proportion of inpatients identified as low-risk that experienced a fall howsoever (False Negative Rate). Characteristics of population and fall events were described among subgroups of low risk and high risk inpatients. We collected 365 incident reports from 40 hospital units, 349 (95.6%) were real falls and 16 (4.4%) were near falls. The fall risk assessment score at patient's admission had been reported in 289 (79%) of the overall incident reports. Thus, 74 (20.3%) fallers were actually not assessed with the STRATIFY, even though the majority of them presented risk recommended to be assessed. The True Positive Rate was 35.6% (n = 101, 95% CI 30% - 41.1%). The False Negative Rate was 64.4% (n = 183, 95% CI 58.9%-70%) of fallers, nevertheless they incurred in a fall. The STRATIFY mean score was 1.3 ± 1.4; the median was 1 (IQQ 0-2). The prevention program using only the STRATIFY tool was found to be not adequate to screen our inpatients population. The incorrect identification of patients' needs leads to allocate resources to erroneous priorities and to untargeted interventions, decreasing healthcare performance and quality.

Intrinsic factors associated with pregnancy falls.

PubMed

Wu, Xuefang; Yeoh, Han T

2014-10-01

Approximately 25% to 27% of women sustain a fall during pregnancy, and falls are associated with serious injuries and can affect pregnancy outcomes. The objective of the current study was to identify intrinsic factors associated with pregnancy that may contribute to women's increased risk of falls. A literature search (Medline and Pubmed) identified articles published between January 1980 and June 2013 that measured associations between pregnancy and fall risks, using an existing fall accident investigation framework. The results indicated that physiological, biomechanical, and psychological changes associated with pregnancy may influence the initiation, detection, and recovery phases of falls and increase the risk of falls in this population. Considering the logistic difficulties and ethnic concerns in recruiting pregnant women to participate in this investigation of fall risk factors, identification of these factors could establish effective fall prevention and intervention programs for pregnant women and improve birth outcomes. [Workplace Health Saf 2014;62(10):403-408.]. Copyright 2014, SLACK Incorporated.

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators.

PubMed

Askari, M; Eslami, S; van Rijn, M; Medlock, S; Moll van Charante, E P; van der Velde, N; de Rooij, S E; Abu-Hanna, A

2016-02-01

We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insufficiency in the quality of fall management. Most indicators showed a positive association between increased risk for functional decline and adherence, four of which with statistical significance. This study aims to investigate the quality of detection and management of falls in the elderly population by general practitioners in the Netherlands, using the Assessing Care of Vulnerable Elders (ACOVE) quality indicators. Community-dwelling persons aged 70 years or above, registered in participating general practices, were asked to fill in a questionnaire designed to determine general practitioner (GP) adherence to fall-related indicators. We used logistic regression to estimate the association between increased risk for functional decline-quantified by the Identification of Seniors At Risk for Primary Care score-and adherence. We then cross-validated the self-reported falls with medical records. Of the 950 elders responding to our questionnaire, only 10.6 % reported that their GP proactively asked them about falls. Of the 160 patients who reported two or more falls, or one fall for which they visited the GP, only 23.1 % had fall documentation in their records. Adherence ranged between 13.6 and 48.6 %. There was a significant positive association between the ISAR-PC scores and adherence in four QIs. Documentation of falls was highest (36.7 %) in patients whom the GP had proactively asked about falls. Based on patient self-reports, adherence to the ACOVE fall-related indicators was poor, suggesting that the quality of evaluation and management of falls in community-dwelling older persons in the Netherlands is poor. The documentation of falls and fall-related risk factors was also poor. However, for most QIs, adherence to them increased with the increase in the risk of functional decline.

Preclinical Alzheimer disease and risk of falls

PubMed Central

Roe, Catherine M.; Grant, Elizabeth A.; Hollingsworth, Holly; Benzinger, Tammie L.; Fagan, Anne M.; Buckles, Virginia D.; Morris, John C.

2013-01-01

Objective: We determined the rate of falls among cognitively normal, community-dwelling older adults, some of whom had presumptive preclinical Alzheimer disease (AD) as detected by in vivo imaging of fibrillar amyloid plaques using Pittsburgh compound B (PiB) and PET and/or by assays of CSF to identify Aβ42, tau, and phosphorylated tau. Methods: We conducted a 12-month prospective cohort study to examine the cumulative incidence of falls. Participants were evaluated clinically and underwent PiB PET imaging and lumbar puncture. Falls were reported monthly using an individualized calendar journal returned by mail. A Cox proportional hazards model was used to test whether time to first fall was associated with each biomarker and the ratio of CSF tau/Aβ42 and CSF phosphorylated tau/Aβ42, after adjustment for common fall risk factors. Results: The sample (n = 125) was predominately female (62.4%) and white (96%) with a mean age of 74.4 years. When controlled for ability to perform activities of daily living, higher levels of PiB retention (hazard ratio = 2.95 [95% confidence interval 1.01–6.45], p = 0.05) and of CSF biomarker ratios (p < 0.001) were associated with a faster time to first fall. Conclusions: Presumptive preclinical AD is a risk factor for falls in older adults. This study suggests that subtle noncognitive changes that predispose older adults to falls are associated with AD and may precede detectable cognitive changes. PMID:23803314

NASA Tech Briefs, January 2007

NASA Technical Reports Server (NTRS)

2007-01-01

Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.

The relationship between orthostatic hypotension and falling in older adults.

PubMed

Shaw, Brett H; Claydon, Victoria E

2014-02-01

Falls are devastating events and are the largest contributor towards injury-related hospitalization of older adults. Orthostatic hypotension (OH) represents an intrinsic risk factor for falls in older adults. OH refers to a significant decrease in blood pressure upon assuming an upright posture. Declines in blood pressure can reduce cerebral perfusion; this can impair consciousness, lead to dizziness, and increase the likelihood of a fall. Although theoretical mechanisms linking OH and falls exist, the magnitude of the association remains poorly characterized, possibly because of methodological differences between previous studies. The use of non-invasive beat-to-beat blood pressure monitoring has altered the way in which OH is now defined, and represents a substantial improvement for detecting OH that was previously unavailable in many studies. Additionally, there is a lack of consistency and standardization of orthostatic assessments and analysis techniques for interpreting blood pressure data. This review explores the previous literature examining the relationship between OH and falls. We highlight the impact of broadening the timing, degree, and overall duration of blood pressure measurements on the detection of OH. We discuss the types of orthostatic stress assessments currently used to evaluate OH and the various techniques capable of measuring these often transient blood pressure changes. Overall, we identify future solutions that may better clarify the relationship between OH and falling risk in order to gain a more precise understanding of potential mechanisms for falls in older adults.

Segmentation of images of abdominal organs.

PubMed

Wu, Jie; Kamath, Markad V; Noseworthy, Michael D; Boylan, Colm; Poehlman, Skip

2008-01-01

Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.

Flight deck disturbance management: a simulator study of diagnosis and recovery from breakdowns in pilot-automation coordination.

PubMed

Nikolic, Mark I; Sarter, Nadine B

2007-08-01

To examine operator strategies for diagnosing and recovering from errors and disturbances as well as the impact of automation design and time pressure on these processes. Considerable efforts have been directed at error prevention through training and design. However, because errors cannot be eliminated completely, their detection, diagnosis, and recovery must also be supported. Research has focused almost exclusively on error detection. Little is known about error diagnosis and recovery, especially in the context of event-driven tasks and domains. With a confederate pilot, 12 airline pilots flew a 1-hr simulator scenario that involved three challenging automation-related tasks and events that were likely to produce erroneous actions or assessments. Behavioral data were compared with a canonical path to examine pilots' error and disturbance management strategies. Debriefings were conducted to probe pilots' system knowledge. Pilots seldom followed the canonical path to cope with the scenario events. Detection of a disturbance was often delayed. Diagnostic episodes were rare because of pilots' knowledge gaps and time criticality. In many cases, generic inefficient recovery strategies were observed, and pilots relied on high levels of automation to manage the consequences of an error. Our findings describe and explain the nature and shortcomings of pilots' error management activities. They highlight the need for improved automation training and design to achieve more timely detection, accurate explanation, and effective recovery from errors and disturbances. Our findings can inform the design of tools and techniques that support disturbance management in various complex, event-driven environments.

Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

DOE PAGES

Venkatraman, S.; Doktycz, M. J.; Qi, H.; ...

2006-01-01

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.

PubMed

Rajalakshmi, Ramachandran; Subashini, Radhakrishnan; Anjana, Ranjit Mohan; Mohan, Viswanathan

2018-06-01

To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt TM ) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists' grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

PubMed

Dereymaeker, Anneleen; Pillay, Kirubin; Vervisch, Jan; Van Huffel, Sabine; Naulaers, Gunnar; Jansen, Katrien; De Vos, Maarten

2017-09-01

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

A standardized staining protocol for L1CAM on formalin-fixed, paraffin-embedded tissues using automated platforms.

PubMed

Fogel, Mina; Harari, Ayelet; Müller-Holzner, Elisabeth; Zeimet, Alain G; Moldenhauer, Gerhard; Altevogt, Peter

2014-06-25

The L1 cell adhesion molecule (L1CAM) is overexpressed in many human cancers and can serve as a biomarker for prognosis in most of these cancers (including type I endometrial carcinomas). Here we provide an optimized immunohistochemical staining procedure for a widely used automated platform (VENTANA™), which has recourse to commercially available primary antibody and detection reagents. In parallel, we optimized the staining on a semi-automated BioGenix (i6000) 
immunostainer. These protocols yield good stainings and should represent the basis for a reliable and standardized immunohistochemical detection of L1CAM in a variety of malignancies in different laboratories.

Integrated Multi-process Microfluidic Systems for Automating Analysis

PubMed Central

Yang, Weichun; Woolley, Adam T.

2010-01-01

Microfluidic technologies have been applied extensively in rapid sample analysis. Some current challenges for standard microfluidic systems are relatively high detection limits, and reduced resolving power and peak capacity compared to conventional approaches. The integration of multiple functions and components onto a single platform can overcome these separation and detection limitations of microfluidics. Multiplexed systems can greatly increase peak capacity in multidimensional separations and can increase sample throughput by analyzing many samples simultaneously. On-chip sample preparation, including labeling, preconcentration, cleanup and amplification, can all serve to speed up and automate processes in integrated microfluidic systems. This paper summarizes advances in integrated multi-process microfluidic systems for automated analysis, their benefits and areas for needed improvement. PMID:20514343

Airborne Particulate Threat Assessment

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

Patrick Treado; Oksana Klueva; Jeffrey Beckstead

Aerosol threat detection requires the ability to discern between threat agents and ambient background particulate matter (PM) encountered in the environment. To date, Raman imaging technology has been demonstrated as an effective strategy for the assessment of threat agents in the presence of specific, complex backgrounds. Expanding our understanding of the composition of ambient particulate matter background will improve the overall performance of Raman Chemical Imaging (RCI) detection strategies for the autonomous detection of airborne chemical and biological hazards. Improving RCI detection performance is strategic due to its potential to become a widely exploited detection approach by several U.S. governmentmore » agencies. To improve the understanding of the ambient PM background with subsequent improvement in Raman threat detection capability, ChemImage undertook the Airborne Particulate Threat Assessment (APTA) Project in 2005-2008 through a collaborative effort with the National Energy Technology Laboratory (NETL), under cooperative agreement number DE-FC26-05NT42594. During Phase 1 of the program, a novel PM classification based on molecular composition was developed based on a comprehensive review of the scientific literature. In addition, testing protocols were developed for ambient PM characterization. A signature database was developed based on a variety of microanalytical techniques, including scanning electron microscopy, FT-IR microspectroscopy, optical microscopy, fluorescence and Raman chemical imaging techniques. An automated particle integrated collector and detector (APICD) prototype was developed for automated collection, deposition and detection of biothreat agents in background PM. During Phase 2 of the program, ChemImage continued to refine the understanding of ambient background composition. Additionally, ChemImage enhanced the APICD to provide improved autonomy, sensitivity and specificity. Deliverables included a Final Report detailing our findings and APICD Gen II subsystems for automated collection, deposition and detection of ambient particulate matter. Key findings from the APTA Program include: Ambient biological PM taxonomy; Demonstration of key subsystems needed for autonomous bioaerosol detection; System design; Efficient electrostatic collection; Automated bioagent recognition; Raman analysis performance validating Td<9 sec; Efficient collection surface regeneration; and Development of a quantitative bioaerosol defection model. The objective of the APTA program was to advance the state of our knowledge of ambient background PM composition. Operation of an automated aerosol detection system was enhanced by a more accurate assessment of background variability, especially for sensitive and specific sensing strategies like Raman detection that are background-limited in performance. Based on this improved knowledge of background, the overall threat detection performance of Raman sensors was improved.« less

Automated Assessment of Medical Students’ Clinical Exposures according to AAMC Geriatric Competencies

PubMed Central

Chen, Yukun; Wrenn, Jesse; Xu, Hua; Spickard, Anderson; Habermann, Ralf; Powers, James; Denny, Joshua C.

2014-01-01

Competence is essential for health care professionals. Current methods to assess competency, however, do not efficiently capture medical students’ experience. In this preliminary study, we used machine learning and natural language processing (NLP) to identify geriatric competency exposures from students’ clinical notes. The system applied NLP to generate the concepts and related features from notes. We extracted a refined list of concepts associated with corresponding competencies. This system was evaluated through 10-fold cross validation for six geriatric competency domains: “medication management (MedMgmt)”, “cognitive and behavioral disorders (CBD)”, “falls, balance, gait disorders (Falls)”, “self-care capacity (SCC)”, “palliative care (PC)”, “hospital care for elders (HCE)” – each an American Association of Medical Colleges competency for medical students. The systems could accurately assess MedMgmt, SCC, HCE, and Falls competencies with F-measures of 0.94, 0.86, 0.85, and 0.84, respectively, but did not attain good performance for PC and CBD (0.69 and 0.62 in F-measure, respectively). PMID:25954341

A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data

Treesearch

Liya Thomas; R. Edward Thomas

2011-01-01

We have developed an automated defect detection system and a state-of-the-art Graphic User Interface (GUI) for hardwood logs. The algorithm identifies defects at least 0.5 inch high and at least 3 inches in diameter on barked hardwood log and stem surfaces. To summarize defect features and to build a knowledge base, hundreds of defects were measured, photographed, and...

An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

DTIC Science & Technology

2018-01-01

collected data. These statistical techniques are under the area of descriptive statistics, which is a methodology to condense the data in quantitative ...ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...report when it is no longer needed. Do not return it to the originator. ARL-TR-8270 ● JAN 2017 US Army Research Laboratory An

Topography-Assisted Electromagnetic Platform for Blood-to-PCR in a Droplet

PubMed Central

Chiou, Chi-Han; Shin, Dong Jin; Zhang, Yi; Wang, Tza-Huei

2013-01-01

This paper presents an electromagnetically actuated platform for automated sample preparation and detection of nucleic acids. The proposed platform integrates nucleic acid extraction using silica-coated magnetic particles with real-time polymerase chain reaction (PCR) on a single cartridge. Extraction of genomic material was automated by manipulating magnetic particles in droplets using a series of planar coil electromagnets assisted by topographical features, enabling efficient fluidic processing over a variety of buffers and reagents. The functionality of the platform was demonstrated by performing nucleic acid extraction from whole blood, followed by real-time PCR detection of KRAS oncogene. Automated sample processing from whole blood to PCR-ready droplet was performed in 15 minutes. We took a modular approach of decoupling the modules of magnetic manipulation and optical detection from the device itself, enabling a low-complexity cartridge that operates in tandem with simple external instruments. PMID:23835223

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers.

PubMed

Sharma, Niraj K; Pedreira, Carlos; Centeno, Maria; Chaudhary, Umair J; Wehner, Tim; França, Lucas G S; Yadee, Tinonkorn; Murta, Teresa; Leite, Marco; Vos, Sjoerd B; Ourselin, Sebastien; Diehl, Beate; Lemieux, Louis

2017-07-01

To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers' spike identification and individual spike class labels visually and quantitatively. The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. WC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs. WC can be used to provide quantitative analysis of epileptic spikes. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

PubMed Central

2013-01-01

The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation.

PubMed

Hodneland, Erlend; Kögel, Tanja; Frei, Dominik Michael; Gerdes, Hans-Hermann; Lundervold, Arvid

2013-08-09

: The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

Improved automated lumen contour detection by novel multifrequency processing algorithm with current intravascular ultrasound system.

PubMed

Kume, Teruyoshi; Kim, Byeong-Keuk; Waseda, Katsuhisa; Sathyanarayana, Shashidhar; Li, Wenguang; Teo, Tat-Jin; Yock, Paul G; Fitzgerald, Peter J; Honda, Yasuhiro

2013-02-01

The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry. Copyright © 2011 Wiley Periodicals, Inc.

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

NASA Astrophysics Data System (ADS)

Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

2013-10-01

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Automated detection of geological landforms on Mars using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Palafox, Leon F.; Hamilton, Christopher W.; Scheidt, Stephen P.; Alvarez, Alexander M.

2017-04-01

The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

Automated detection of geological landforms on Mars using Convolutional Neural Networks.

PubMed

Palafox, Leon F; Hamilton, Christopher W; Scheidt, Stephen P; Alvarez, Alexander M

2017-04-01

The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

Assessment of Fall Characteristics From Depth Sensor Videos.

PubMed

O'Connor, Jennifer J; Phillips, Lorraine J; Folarinde, Bunmi; Alexander, Gregory L; Rantz, Marilyn

2017-07-01

Falls are a major source of death and disability in older adults; little data, however, are available about the etiology of falls in community-dwelling older adults. Sensor systems installed in independent and assisted living residences of 105 older adults participating in an ongoing technology study were programmed to record live videos of probable fall events. Sixty-four fall video segments from 19 individuals were viewed and rated using the Falls Video Assessment Questionnaire. Raters identified that 56% (n = 36) of falls were due to an incorrect shift of body weight and 27% (n = 17) from losing support of an external object, such as an unlocked wheelchair or rolling walker. In 60% of falls, mobility aids were in the room or in use at the time of the fall. Use of environmentally embedded sensors provides a mechanism for real-time fall detection and, ultimately, may supply information to clinicians for fall prevention interventions. [Journal of Gerontological Nursing, 43(7), 13-19.]. Copyright 2017, SLACK Incorporated.

Experimental studies on the effect of automation on pilot situational awareness in the datalink ATC environment

NASA Technical Reports Server (NTRS)

Hahn, Edward C.; Hansman, R. J., Jr.

1992-01-01

An experiment to study how automation, when used in conjunction with datalink for the delivery of ATC clearance amendments, affects the situational awareness of aircrews was conducted. The study was focused on the relationship of situational awareness to automated Flight Management System (FMS) programming of datalinked clearances and the readback of ATC clearances. Situational awareness was tested by issuing nominally unacceptable ATC clearances and measuring whether the error was detected by the subject pilots. The experiment also varied the mode of clearance delivery: Verbal, Textual, and Graphical. The error detection performance and pilot preference results indicate that the automated programming of the FMS may be superior to manual programming. It is believed that automated FMS programming may relieve some of the cognitive load, allowing pilots to concentrate on the strategic implications of a clearance amendment. Also, readback appears to have value, but the small sample size precludes a definite conclusion. Furthermore, because textual and graphical modes of delivery offer different but complementary advantages for cognitive processing, a combination of these modes of delivery may be advantageous in a datalink presentation.

An Experimental Study of the Effects of Automation on Pilot Situational Awareness in the Datalink ATC Environment

NASA Technical Reports Server (NTRS)

Hahn, Edward C.; Hansman, R. John, Jr.

1992-01-01

An experiment to study how automation, when used in conjunction with datalink for the delivery of air traffic control (ATC) clearance amendments, affects the situational awareness of aircrews was conducted. The study was focused on the relationship of situational awareness to automated Flight Management System (FMS) programming and the readback of ATC clearances. Situational awareness was tested by issuing nominally unacceptable ATC clearances and measuring whether the error was detected by the subject pilots. The experiment also varied the mode of clearance delivery: Verbal, Textual, and Graphical. The error detection performance and pilot preference results indicate that the automated programming of the FMS may be superior to manual programming. It is believed that automated FMS programming may relieve some of the cognitive load, allowing pilots to concentrate on the strategic implications of a clearance amendment. Also, readback appears to have value, but the small sample size precludes a definite conclusion. Furthermore, because textual and graphical modes of delivery offer different but complementary advantages for cognitive processing, a combination of these modes of delivery may be advantageous in a datalink presentation.

Automated analysis of oxidative metabolites

NASA Technical Reports Server (NTRS)

Furner, R. L. (Inventor)

1974-01-01

An automated system for the study of drug metabolism is described. The system monitors the oxidative metabolites of aromatic amines and of compounds which produce formaldehyde on oxidative dealkylation. It includes color developing compositions suitable for detecting hyroxylated aromatic amines and formaldehyde.

Micro-controller based fall detector to assist recovering patients or senior citizens

NASA Astrophysics Data System (ADS)

Páez, Francisco; Asplund, Lars

2010-09-01

Senior citizens and patients recovering from surgery or using strong medications with severe side effects tend to fall unexpectedly. The consequences of such an uncontrolled fall could be worse than the original malady, especially when there is no communication with the care-takers. We describe a fall-detector device capable of distinguishing falls from normal daily activities. Based on three-axis accelerometer and advanced data processing, the microcontroller emits an alarm requesting help in the case of a physical fall. We design and construct the fall-detector prototype for either inside or outside use. In order to determine the device performance, fifty instances of each fall event have been evaluated; all of them detected as fall event. In the case of daily activities, the only movement that produces an alarm is the transition from standing up to lying in 5% of the occurrences.

The use of automated bioacoustic recorders to replace human wildlife surveys: an example using nightjars.

PubMed

Zwart, Mieke C; Baker, Andrew; McGowan, Philip J K; Whittingham, Mark J

2014-01-01

To be able to monitor and protect endangered species, we need accurate information on their numbers and where they live. Survey methods using automated bioacoustic recorders offer significant promise, especially for species whose behaviour or ecology reduces their detectability during traditional surveys, such as the European nightjar. In this study we examined the utility of automated bioacoustic recorders and the associated classification software as a way to survey for wildlife, using the nightjar as an example. We compared traditional human surveys with results obtained from bioacoustic recorders. When we compared these two methods using the recordings made at the same time as the human surveys, we found that recorders were better at detecting nightjars. However, in practice fieldworkers are likely to deploy recorders for extended periods to make best use of them. Our comparison of this practical approach with human surveys revealed that recorders were significantly better at detecting nightjars than human surveyors: recorders detected nightjars during 19 of 22 survey periods, while surveyors detected nightjars on only six of these occasions. In addition, there was no correlation between the amount of vocalisation captured by the acoustic recorders and the abundance of nightjars as recorded by human surveyors. The data obtained from the recorders revealed that nightjars were most active just before dawn and just after dusk, and least active during the middle of the night. As a result, we found that recording at both dusk and dawn or only at dawn would give reasonably high levels of detection while significantly reducing recording time, preserving battery life. Our analyses suggest that automated bioacoustic recorders could increase the detection of other species, particularly those that are known to be difficult to detect using traditional survey methods. The accuracy of detection is especially important when the data are used to inform conservation.

Indium-111-radiolabeled guinea pig peripheral leukocytes. In vivo distribution and response to leukotriene B4

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

Sweatman, W.J.; Brandon, D.R.; Cranstone, S.

The preparation of indium-111 tropolonate-radiolabeled guinea pig peripheral mixed white cells (greater than 80% neutrophils) is described. Autologous rather than homologous cells are required to provide a population of labeled, functional cells on reintroduction to the animals. Surgery has been shown to result in a profound neutropenia from which the animals must recover before removal of blood for cell preparation. The response of radiolabeled cells parallels that of the unlabeled cell population to a chemotaxin, leukotriene B4. This material causes a profound neutropenia of rapid onset accompanied by a parallel fall in blood radioactivity. The fall in circulating radiolabel ismore » accompanied by an increase in radioactivity in the thoracic region. These changes have been monitored externally using an automated isotope monitoring system.« less

Design on wireless auto-measurement system for lead rail straightness measurement based on PSD

NASA Astrophysics Data System (ADS)

Yan, Xiugang; Zhang, Shuqin; Dong, Dengfeng; Cheng, Zhi; Wu, Guanghua; Wang, Jie; Zhou, Weihu

2016-10-01

Straightness detection is not only one of the key technologies for the product quality and installation accuracy of all types of lead rail, but also an important dimensional measurement technology. The straightness measuring devices now available have disadvantages of low automation level, limiting by measuring environment, and low measurement efficiency. In this paper, a wireless measurement system for straightness detection based on position sensitive detector (PSD) is proposed. The system has some advantage of high automation-level, convenient, high measurement efficiency, easy to transplanting and expanding, and can detect straightness of lead rail in real-time.

Defect analysis and detection of micro nano structured optical thin film

NASA Astrophysics Data System (ADS)

Xu, Chang; Shi, Nuo; Zhou, Lang; Shi, Qinfeng; Yang, Yang; Li, Zhuo

2017-10-01

This paper focuses on developing an automated method for detecting defects on our wavelength conversion thin film. We analyzes the operating principle of our wavelength conversion Micro/Nano thin film which absorbing visible light and emitting infrared radiation, indicates the relationship between the pixel's pattern and the radiation of the thin film, and issues the principle of defining blind pixels and their categories due to the calculated and experimental results. An effective method is issued for the automated detection based on wavelet transform and template matching. The results reveal that this method has desired accuracy and processing speed.

System for particle concentration and detection

DOEpatents

Morales, Alfredo M.; Whaley, Josh A.; Zimmerman, Mark D.; Renzi, Ronald F.; Tran, Huu M.; Maurer, Scott M.; Munslow, William D.

2013-03-19

A new microfluidic system comprising an automated prototype insulator-based dielectrophoresis (iDEP) triggering microfluidic device for pathogen monitoring that can eventually be run outside the laboratory in a real world environment has been used to demonstrate the feasibility of automated trapping and detection of particles. The system broadly comprised an aerosol collector for collecting air-borne particles, an iDEP chip within which to temporarily trap the collected particles and a laser and fluorescence detector with which to induce a fluorescence signal and detect a change in that signal as particles are trapped within the iDEP chip.

Automated and miniaturized detection of biological threats with a centrifugal microfluidic system

NASA Astrophysics Data System (ADS)

Mark, D.; van Oordt, T.; Strohmeier, O.; Roth, G.; Drexler, J.; Eberhard, M.; Niedrig, M.; Patel, P.; Zgaga-Griesz, A.; Bessler, W.; Weidmann, M.; Hufert, F.; Zengerle, R.; von Stetten, F.

2012-06-01

The world's growing mobility, mass tourism, and the threat of terrorism increase the risk of the fast spread of infectious microorganisms and toxins. Today's procedures for pathogen detection involve complex stationary devices, and are often too time consuming for a rapid and effective response. Therefore a robust and mobile diagnostic system is required. We present a microstructured LabDisk which performs complex biochemical analyses together with a mobile centrifugal microfluidic device which processes the LabDisk. This portable system will allow fully automated and rapid detection of biological threats at the point-of-need.

Petri net-based modelling of human-automation conflicts in aviation.

PubMed

Pizziol, Sergio; Tessier, Catherine; Dehais, Frédéric

2014-01-01

Analyses of aviation safety reports reveal that human-machine conflicts induced by poor automation design are remarkable precursors of accidents. A review of different crew-automation conflicting scenarios shows that they have a common denominator: the autopilot behaviour interferes with the pilot's goal regarding the flight guidance via 'hidden' mode transitions. Considering both the human operator and the machine (i.e. the autopilot or the decision functions) as agents, we propose a Petri net model of those conflicting interactions, which allows them to be detected as deadlocks in the Petri net. In order to test our Petri net model, we designed an autoflight system that was formally analysed to detect conflicting situations. We identified three conflicting situations that were integrated in an experimental scenario in a flight simulator with 10 general aviation pilots. The results showed that the conflicts that we had a-priori identified as critical had impacted the pilots' performance. Indeed, the first conflict remained unnoticed by eight participants and led to a potential collision with another aircraft. The second conflict was detected by all the participants but three of them did not manage the situation correctly. The last conflict was also detected by all the participants but provoked typical automation surprise situation as only one declared that he had understood the autopilot behaviour. These behavioural results are discussed in terms of workload and number of fired 'hidden' transitions. Eventually, this study reveals that both formal and experimental approaches are complementary to identify and assess the criticality of human-automation conflicts. Practitioner Summary: We propose a Petri net model of human-automation conflicts. An experiment was conducted with general aviation pilots performing a scenario involving three conflicting situations to test the soundness of our formal approach. This study reveals that both formal and experimental approaches are complementary to identify and assess the criticality conflicts.

Automated analysis of cell migration and nuclear envelope rupture in confined environments.

PubMed

Elacqua, Joshua J; McGregor, Alexandra L; Lammerding, Jan

2018-01-01

Recent in vitro and in vivo studies have highlighted the importance of the cell nucleus in governing migration through confined environments. Microfluidic devices that mimic the narrow interstitial spaces of tissues have emerged as important tools to study cellular dynamics during confined migration, including the consequences of nuclear deformation and nuclear envelope rupture. However, while image acquisition can be automated on motorized microscopes, the analysis of the corresponding time-lapse sequences for nuclear transit through the pores and events such as nuclear envelope rupture currently requires manual analysis. In addition to being highly time-consuming, such manual analysis is susceptible to person-to-person variability. Studies that compare large numbers of cell types and conditions therefore require automated image analysis to achieve sufficiently high throughput. Here, we present an automated image analysis program to register microfluidic constrictions and perform image segmentation to detect individual cell nuclei. The MATLAB program tracks nuclear migration over time and records constriction-transit events, transit times, transit success rates, and nuclear envelope rupture. Such automation reduces the time required to analyze migration experiments from weeks to hours, and removes the variability that arises from different human analysts. Comparison with manual analysis confirmed that both constriction transit and nuclear envelope rupture were detected correctly and reliably, and the automated analysis results closely matched a manual analysis gold standard. Applying the program to specific biological examples, we demonstrate its ability to detect differences in nuclear transit time between cells with different levels of the nuclear envelope proteins lamin A/C, which govern nuclear deformability, and to detect an increase in nuclear envelope rupture duration in cells in which CHMP7, a protein involved in nuclear envelope repair, had been depleted. The program thus presents a versatile tool for the study of confined migration and its effect on the cell nucleus.

Report A: Fish distribution and population dynamics in Rock Creek, Klickitat County, Washington

USGS Publications Warehouse

Allen, Brady; Munz, Carrie S.; Harvey, Elaine

2013-01-01

The U.S. Geological Survey collaborated with the Yakama Nation starting in fall of 2009 to study the fish populations in Rock Creek, a Washington State tributary of the Columbia River 21 kilometers upstream of John Day Dam. Prior to this study, very little was known about the ESA-listed (threatened) Mid-Columbia River steelhead (Oncorhynchus mykiss) population in this arid watershed with intermittent stream flow. The objectives of the study were to quantify fish habitat, document fish distribution, abundance, and movement, and identify areas of high salmonid productivity. To accomplish these objectives, we electrofished in the spring and fall, documenting the distribution and relative abundance of all fish species to evaluate the influence of biotic factors on salmonid productivity and survival. We surveyed the distribution of perennial pools and established a network of automated temperature recording devices from river kilometer (rkm) 2 to 23 in Rock Creek and rkm 0 to 8 in Squaw Creek, a major tributary entering Rock Creek at rkm 13, to better understand the abiotic factors influencing the salmonid populations. Salmonid abundance estimates were conducted using a mark-recapture method in a systematic subsample of the perennial pools. The proportion and timing of salmonids migrating from these pools were assessed by building, installing, and operating two passive integrated transponder (PIT) tag interrogation systems at rkm 5 and at the confluence with Squaw Creek (rkm 13). From fall 2009 to fall 2012, we PIT-tagged 3,088 O. mykiss and 151 coho salmon (O. kisutch) during electrofishing efforts. In the lowest flow periods of 2010 to 2012, we found that an average of 36% of the surveyed streambed length was dry, and 17% remained as perennial pools. The maximum temperature recorded in those pools was 24.4°C, but most pools had a maximum temperature that was less than 21°C. O. mykiss were present in most pools, and non-native fish species, such as smallmouth bass (Micropterus dolomieu), were typically found downstream of rkm 5. Coho salmon were present in nearly every pool that was sampled in 2011, but were rare in 2009, 2010, and 2012. About 27% of the PIT-tagged O. mykiss and 38% of the PIT-tagged coho were detected outmigrating to the Columbia River. Of those fish, 92% (n=695) were detected leaving Rock Creek as smolts in April and May. As of November 2013, 9 O. mykiss and 4 coho that we tagged in Rock Creek as juveniles have returned as adults to Bonneville Dam. Also, an additional 34 PIT-tagged adult steelhead, and 6 PIT-tagged coho that were tagged by other groups have been detected in Rock Creek, of which, 22 were of known origin (tagged as juveniles). Of these, 85% were tagged or released in the Snake River. The PIT-tag interrogation systems will be operated for several more years to allow time for the fish tagged as juveniles to return as adults and complete their life cycles. The Yakama Nation will use the information collected from this study to prioritize and gauge the effectiveness of ongoing and future restoration actions.

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy.

PubMed

Martins, Cristina; Moreira da Silva, Nadia; Silva, Guilherme; Rozanski, Verena E; Silva Cunha, Joao Paulo

2016-08-01

Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

Image-based fall detection and classification of a user with a walking support system

NASA Astrophysics Data System (ADS)

Taghvaei, Sajjad; Kosuge, Kazuhiro

2017-10-01

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

The PARAChute Project: Remote Monitoring of Posture and Gait for Fall Prevention

NASA Astrophysics Data System (ADS)

Hewson, David J.; Duchêne, Jacques; Charpillet, François; Saboune, Jamal; Michel-Pellegrino, Valérie; Amoud, Hassan; Doussot, Michel; Paysant, Jean; Boyer, Anne; Hogrel, Jean-Yves

2007-12-01

Falls in the elderly are a major public health problem due to both their frequency and their medical and social consequences. In France alone, more than two million people aged over 65 years old fall each year, leading to more than 9 000 deaths, in particular in those over 75 years old (more than 8 000 deaths). This paper describes the PARAChute project, which aims to develop a methodology that will enable the detection of an increased risk of falling in community-dwelling elderly. The methods used for a remote noninvasive assessment for static and dynamic balance assessments and gait analysis are described. The final result of the project has been the development of an algorithm for movement detection during gait and a balance signature extracted from a force plate. A multicentre longitudinal evaluation of balance has commenced in order to validate the methodologies and technologies developed in the project.

Hardware Design of the Energy Efficient Fall Detection Device

NASA Astrophysics Data System (ADS)

Skorodumovs, A.; Avots, E.; Hofmanis, J.; Korāts, G.

2016-04-01

Health issues for elderly people may lead to different injuries obtained during simple activities of daily living. Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. In the project "Wireless Sensor Systems in Telecare Application for Elderly People", we have developed a robust fall detection algorithm for a wearable wireless sensor. To optimise the algorithm for hardware performance and test it in field, we have designed an accelerometer based wireless fall detector. Our main considerations were: a) functionality - so that the algorithm can be applied to the chosen hardware, and b) power efficiency - so that it can run for a very long time. We have picked and tested the parts, built a prototype, optimised the firmware for lowest consumption, tested the performance and measured the consumption parameters. In this paper, we discuss our design choices and present the results of our work.

Systems and methods for data quality control and cleansing

DOEpatents

Wenzel, Michael; Boettcher, Andrew; Drees, Kirk; Kummer, James

2016-05-31

A method for detecting and cleansing suspect building automation system data is shown and described. The method includes using processing electronics to automatically determine which of a plurality of error detectors and which of a plurality of data cleansers to use with building automation system data. The method further includes using processing electronics to automatically detect errors in the data and cleanse the data using a subset of the error detectors and a subset of the cleansers.

Functional Specifications to an Automated Retinal Scanner for Use in Plotting the Vascular Map

DTIC Science & Technology

1988-12-01

available an aid in the early detection and continuing treatment of diabetes . Therefore, it is the distinct wish of the author that this system provide some...choroid, it may be possible to detect diabetes earlier and stem the tide of retinopathy in those patients so afflicted. Additionally, retinal...Subject Terms (continue on reverse i necessary and identify t block number) Retinal Imaging, Automation, Infrared, Diabetic Retinopathy , Field I Group I

A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds

PubMed Central

Dorninger, Peter; Pfeifer, Norbert

2008-01-01

Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects. PMID:27873931

Glaucoma risk index: automated glaucoma detection from color fundus images.

PubMed

Bock, Rüdiger; Meier, Jörg; Nyúl, László G; Hornegger, Joachim; Michelson, Georg

2010-06-01

Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Focused ultrasound: concept for automated transcutaneous control of hemorrhage in austere settings.

PubMed

Kucewicz, John C; Bailey, Michael R; Kaczkowski, Peter J; Carter, Stephen J

2009-04-01

High intensity focused ultrasound (HIFU) is being developed for a range of clinical applications. Of particular interest to NASA and the military is the use of HIFU for traumatic injuries because HIFU has the unique ability to transcutaneously stop bleeding. Automation of this technology would make possible its use in remote, austere settings by personnel not specialized in medical ultrasound. Here a system to automatically detect and target bleeding is tested and reported. The system uses Doppler ultrasound images from a clinical ultrasound scanner for bleeding detection and hardware for HIFU therapy. The system was tested using a moving string to simulate blood flow and targeting was visualized by Schlieren imaging to show the focusing of the HIFU acoustic waves. When instructed by the operator, a Doppler ultrasound image is acquired and processed to detect and localize the moving string, and the focus of the HIFU array is electronically adjusted to target the string. Precise and accurate targeting was verified in the Schlieren images. An automated system to detect and target simulated bleeding has been built and tested. The system could be combined with existing algorithms to detect, target, and treat clinical bleeding.

Automation for deep space vehicle monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1991-01-01

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Automated Power-Distribution System

NASA Technical Reports Server (NTRS)

Thomason, Cindy; Anderson, Paul M.; Martin, James A.

1990-01-01

Automated power-distribution system monitors and controls electrical power to modules in network. Handles both 208-V, 20-kHz single-phase alternating current and 120- to 150-V direct current. Power distributed to load modules from power-distribution control units (PDCU's) via subsystem distributors. Ring busses carry power to PDCU's from power source. Needs minimal attention. Detects faults and also protects against them. Potential applications include autonomous land vehicles and automated industrial process systems.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

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.

a Novel Method for Automation of 3d Hydro Break Line Generation from LIDAR Data Using Matlab

NASA Astrophysics Data System (ADS)

Toscano, G. J.; Gopalam, U.; Devarajan, V.

2013-08-01

Water body detection is necessary to generate hydro break lines, which are in turn useful in creating deliverables such as TINs, contours, DEMs from LiDAR data. Hydro flattening follows the detection and delineation of water bodies (lakes, rivers, ponds, reservoirs, streams etc.) with hydro break lines. Manual hydro break line generation is time consuming and expensive. Accuracy and processing time depend on the number of vertices marked for delineation of break lines. Automation with minimal human intervention is desired for this operation. This paper proposes using a novel histogram analysis of LiDAR elevation data and LiDAR intensity data to automatically detect water bodies. Detection of water bodies using elevation information was verified by checking against LiDAR intensity data since the spectral reflectance of water bodies is very small compared with that of land and vegetation in near infra-red wavelength range. Detection of water bodies using LiDAR intensity data was also verified by checking against LiDAR elevation data. False detections were removed using morphological operations and 3D break lines were generated. Finally, a comparison of automatically generated break lines with their semi-automated/manual counterparts was performed to assess the accuracy of the proposed method and the results were discussed.

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automated Information System (AIS) Alarm System

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

Hunteman, W.

1997-05-01

The Automated Information Alarm System is a joint effort between Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Sandia National Laboratory to demonstrate and implement, on a small-to-medium sized local area network, an automated system that detects and automatically responds to attacks that use readily available tools and methodologies. The Alarm System will sense or detect, assess, and respond to suspicious activities that may be detrimental to information on the network or to continued operation of the network. The responses will allow stopping, isolating, or ejecting the suspicious activities. The number of sensors, the sensitivity of the sensors, themore » assessment criteria, and the desired responses may be set by the using organization to meet their local security policies.« less

Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

NASA Astrophysics Data System (ADS)

Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

2014-03-01

Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices

PubMed Central

Golden, J.P.; Verbarg, J.; Howell, P.B.; Shriver-Lake, L.C.; Ligler, F.S.

2012-01-01

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose–response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. PMID:22960010

Automated processing integrated with a microflow cytometer for pathogen detection in clinical matrices.

PubMed

Golden, J P; Verbarg, J; Howell, P B; Shriver-Lake, L C; Ligler, F S

2013-02-15

A spinning magnetic trap (MagTrap) for automated sample processing was integrated with a microflow cytometer capable of simultaneously detecting multiple targets to provide an automated sample-to-answer diagnosis in 40 min. After target capture on fluorescently coded magnetic microspheres, the magnetic trap automatically concentrated the fluorescently coded microspheres, separated the captured target from the sample matrix, and exposed the bound target sequentially to biotinylated tracer molecules and streptavidin-labeled phycoerythrin. The concentrated microspheres were then hydrodynamically focused in a microflow cytometer capable of 4-color analysis (two wavelengths for microsphere identification, one for light scatter to discriminate single microspheres and one for phycoerythrin bound to the target). A three-fold decrease in sample preparation time and an improved detection limit, independent of target preconcentration, was demonstrated for detection of Escherichia coli 0157:H7 using the MagTrap as compared to manual processing. Simultaneous analysis of positive and negative controls, along with the assay reagents specific for the target, was used to obtain dose-response curves, demonstrating the potential for quantification of pathogen load in buffer and serum. Published by Elsevier B.V.

Advances toward fully automated in vivo assessment of oral epithelial dysplasia by nuclear endomicroscopy-A pilot study.

PubMed

Liese, Jan; Winter, Karsten; Glass, Änne; Bertolini, Julia; Kämmerer, Peer Wolfgang; Frerich, Bernhard; Schiefke, Ingolf; Remmerbach, Torsten W

2017-11-01

Uncertainties in detection of oral epithelial dysplasia (OED) frequently result from sampling error especially in inflammatory oral lesions. Endomicroscopy allows non-invasive, "en face" imaging of upper oral epithelium, but parameters of OED are unknown. Mucosal nuclei were imaged in 34 toluidine blue-stained oral lesions with a commercial endomicroscopy. Histopathological diagnosis showed four biopsies in "dys-/neoplastic," 23 in "inflammatory," and seven in "others" disease groups. Strength of different assessment strategies of nuclear scoring, nuclear count, and automated nuclear analysis were measured by area under ROC curve (AUC) to identify histopathological "dys-/neoplastic" group. Nuclear objects from automated image analysis were visually corrected. Best-performing parameters of nuclear-to-image ratios were the count of large nuclei (AUC=0.986) and 6-nearest neighborhood relation (AUC=0.896), and best parameters of nuclear polymorphism were the count of atypical nuclei (AUC=0.996) and compactness of nuclei (AUC=0.922). Excluding low-grade OED, nuclear scoring and count reached 100% sensitivity and 98% specificity for detection of dys-/neoplastic lesions. In automated analysis, combination of parameters enhanced diagnostic strength. Sensitivity of 100% and specificity of 87% were seen for distances of 6-nearest neighbors and aspect ratios even in uncorrected objects. Correction improved measures of nuclear polymorphism only. The hue of background color was stronger than nuclear density (AUC=0.779 vs 0.687) to detect dys-/neoplastic group indicating that macroscopic aspect is biased. Nuclear-to-image ratios are applicable for automated optical in vivo diagnostics for oral potentially malignant disorders. Nuclear endomicroscopy may promote non-invasive, early detection of dys-/neoplastic lesions by reducing sampling error. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Testing the effectiveness of automated acoustic sensors for monitoring vocal activity of Marbled Murrelets Brachyramphus marmoratus

USGS Publications Warehouse

Cragg, Jenna L.; Burger, Alan E.; Piatt, John F.

2015-01-01

Cryptic nest sites and secretive breeding behavior make population estimates and monitoring of Marbled Murrelets Brachyramphus marmoratus difficult and expensive. Standard audio-visual and radar protocols have been refined but require intensive field time by trained personnel. We examined the detection range of automated sound recorders (Song Meters; Wildlife Acoustics Inc.) and the reliability of automated recognition models (“recognizers”) for identifying and quantifying Marbled Murrelet vocalizations during the 2011 and 2012 breeding seasons at Kodiak Island, Alaska. The detection range of murrelet calls by Song Meters was estimated to be 60 m. Recognizers detected 20 632 murrelet calls (keer and keheer) from a sample of 268 h of recordings, yielding 5 870 call series, which compared favorably with human scanning of spectrograms (on average detecting 95% of the number of call series identified by a human observer, but not necessarily the same call series). The false-negative rate (percentage of murrelet call series that the recognizers failed to detect) was 32%, mainly involving weak calls and short call series. False-positives (other sounds included by recognizers as murrelet calls) were primarily due to complex songs of other bird species, wind and rain. False-positives were lower in forest nesting habitat (48%) and highest in shrubby vegetation where calls of other birds were common (97%–99%). Acoustic recorders tracked spatial and seasonal trends in vocal activity, with higher call detections in high-quality forested habitat and during late July/early August. Automated acoustic monitoring of Marbled Murrelet calls could provide cost-effective, valuable information for assessing habitat use and temporal and spatial trends in nesting activity; reliability is dependent on careful placement of sensors to minimize false-positives and on prudent application of digital recognizers with visual checking of spectrograms.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

PubMed

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

PubMed

Niemeijer, Meindert; van Ginneken, Bram; Russell, Stephen R; Suttorp-Schulten, Maria S A; Abràmoff, Michael D

2007-05-01

To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes.

A Self-Adapting System for the Automated Detection of Inter-Ictal Epileptiform Discharges

PubMed Central

Lodder, Shaun S.; van Putten, Michel J. A. M.

2014-01-01

Purpose Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search. Methods Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form “IED nominations”, each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections. Key Findings Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20–30 min recordings 1took approximately 5 min. Significance The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents. PMID:24454813

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

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

Linguraru, Marius George; Panjwani, Neil; Fletcher, Joel G.

2011-12-15

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm. Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided dosesmore » over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm. Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan. Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.« less

PowerGuard® manufacturing innovation and expansion

NASA Astrophysics Data System (ADS)

Dinwoodie, Thomas; Kleiner, Tim; O'Brien, Colleen; Quiroz, Maurice

1999-03-01

PowerLight Corporation, with support from the DOE's PVMaT program, has undertaken a comprehensive agenda to automate the manufacture of its PowerGuard PV roof tile system. The advanced manufacturing will lead to substantially reduced costs, quality improvements, and increased production capacity. Over the three years of the PVMaT contract, system costs are expected to fall 2.65/Wp, with annual production capability increasing from 5 to 16 MW. PowerLight is on schedule with meeting its objectives under this program.

CT-guided automated detection of lung tumors on PET images

NASA Astrophysics Data System (ADS)

Cui, Yunfeng; Zhao, Binsheng; Akhurst, Timothy J.; Yan, Jiayong; Schwartz, Lawrence H.

2008-03-01

The calculation of standardized uptake values (SUVs) in tumors on serial [ 18F]2-fluoro-2-deoxy-D-glucose ( 18F-FDG) positron emission tomography (PET) images is often used for the assessment of therapy response. We present a computerized method that automatically detects lung tumors on 18F-FDG PET/Computed Tomography (CT) images using both anatomic and metabolic information. First, on CT images, relevant organs, including lung, bone, liver and spleen, are automatically identified and segmented based on their locations and intensity distributions. Hot spots (SUV >= 1.5) on 18F-FDG PET images are then labeled using the connected component analysis. The resultant "hot objects" (geometrically connected hot spots in three dimensions) that fall into, reside at the edges or are in the vicinity of the lungs are considered as tumor candidates. To determine true lesions, further analyses are conducted, including reduction of tumor candidates by the masking out of hot objects within CT-determined normal organs, and analysis of candidate tumors' locations, intensity distributions and shapes on both CT and PET. The method was applied to 18F-FDG-PET/CT scans from 9 patients, on which 31 target lesions had been identified by a nuclear medicine radiologist during a Phase II lung cancer clinical trial. Out of 31 target lesions, 30 (97%) were detected by the computer method. However, sensitivity and specificity were not estimated because not all lesions had been marked up in the clinical trial. The method effectively excluded the hot spots caused by mediastinum, liver, spleen, skeletal muscle and bone metastasis.

Procedure for Automated Eddy Current Crack Detection in Thin Titanium Plates

NASA Technical Reports Server (NTRS)

Wincheski, Russell A.

2012-01-01

This procedure provides the detailed instructions for conducting Eddy Current (EC) inspections of thin (5-30 mils) titanium membranes with thickness and material properties typical of the development of Ultra-Lightweight diaphragm Tanks Technology (ULTT). The inspection focuses on the detection of part-through, surface breaking fatigue cracks with depths between approximately 0.002" and 0.007" and aspect ratios (a/c) of 0.2-1.0 using an automated eddy current scanning and image processing technique.

Automated accident detection at intersections.

DOT National Transportation Integrated Search

2004-03-01

This research aims to provide a timely and accurate accident detection method at intersections, which is : very important for the Traffic Management System(TMS). This research uses acoustic signals to detect : accident at intersections. A system is c...

Automation in future air traffic management: effects of decision aid reliability on controller performance and mental workload.

PubMed

Metzger, Ulla; Parasuraman, Raja

2005-01-01

Future air traffic management concepts envisage shared decision-making responsibilities between controllers and pilots, necessitating that controllers be supported by automated decision aids. Even as automation tools are being introduced, however, their impact on the air traffic controller is not well understood. The present experiments examined the effects of an aircraft-to-aircraft conflict decision aid on performance and mental workload of experienced, full-performance level controllers in a simulated Free Flight environment. Performance was examined with both reliable (Experiment 1) and inaccurate automation (Experiment 2). The aid improved controller performance and reduced mental workload when it functioned reliably. However, detection of a particular conflict was better under manual conditions than under automated conditions when the automation was imperfect. Potential or actual applications of the results include the design of automation and procedures for future air traffic control systems.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Automated Monitoring of Pipeline Rights-of-Way

NASA Technical Reports Server (NTRS)

Frost, Chard Ritchie

2010-01-01

NASA Ames Research Center and the Pipeline Research Council International, Inc. have partnered in the formation of a research program to identify and develop the key technologies required to enable automated detection of threats to gas and oil transmission and distribution pipelines. This presentation describes the Right-of-way Automated Monitoring (RAM) program and highlights research successes to date, continuing challenges to implementing the RAM objectives, and the program's ongoing work and plans.

Improved Cloud Detection Utilizing Defense Meteorological Satellite Program near Infrared Measurements

DTIC Science & Technology

1982-01-27

Visible 3. 3 Ea r th Location, Colocation, and Normalization 4. IMAGE ANALYSIS 4. 1 Interactive Capabilities 4.2 Examples 5. AUTOMATED CLOUD...computer Interactive Data Access System (McIDAS) before image analysis and algorithm development were done. Earth-location is an automated procedure to...the factor l / s in (SSE) toward the gain settings given in Table 5. 4. IMAGE ANALYSIS 4.1 Interactive Capabilities The development of automated

Machine-vision-based roadway health monitoring and assessment : development of a shape-based pavement-crack-detection approach.

DOT National Transportation Integrated Search

2016-01-01

State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level : pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and : manag...

Decision Making In A High-Tech World: Automation Bias and Countermeasures

NASA Technical Reports Server (NTRS)

Mosier, Kathleen L.; Skitka, Linda J.; Burdick, Mark R.; Heers, Susan T.; Rosekind, Mark R. (Technical Monitor)

1996-01-01

Automated decision aids and decision support systems have become essential tools in many high-tech environments. In aviation, for example, flight management systems computers not only fly the aircraft, but also calculate fuel efficient paths, detect and diagnose system malfunctions and abnormalities, and recommend or carry out decisions. Air Traffic Controllers will soon be utilizing decision support tools to help them predict and detect potential conflicts and to generate clearances. Other fields as disparate as nuclear power plants and medical diagnostics are similarly becoming more and more automated. Ideally, the combination of human decision maker and automated decision aid should result in a high-performing team, maximizing the advantages of additional cognitive and observational power in the decision-making process. In reality, however, the presence of these aids often short-circuits the way that even very experienced decision makers have traditionally handled tasks and made decisions, and introduces opportunities for new decision heuristics and biases. Results of recent research investigating the use of automated aids have indicated the presence of automation bias, that is, errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers inappropriately follow an automated directive, or automation omission errors, i.e., errors made when humans fail to take action or notice a problem because an automated aid fails to inform them, can result from this tendency. Evidence of the tendency to make automation-related omission and commission errors has been found in pilot self reports, in studies using pilots in flight simulations, and in non-flight decision making contexts with student samples. Considerable research has found that increasing social accountability can successfully ameliorate a broad array of cognitive biases and resultant errors. To what extent these effects generalize to performance situations is not yet empirically established. The two studies to be presented represent concurrent efforts, with student and professional pilot samples, to determine the effects of accountability pressures on automation bias and on the verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves as accountable for their strategies of interaction with the automation were significantly more likely to verify its correctness, and committed significantly fewer automation-related errors than those who did not report this perception.

Development of automated high throughput single molecular microfluidic detection platform for signal transduction analysis

NASA Astrophysics Data System (ADS)

Huang, Po-Jung; Baghbani Kordmahale, Sina; Chou, Chao-Kai; Yamaguchi, Hirohito; Hung, Mien-Chie; Kameoka, Jun

2016-03-01

Signal transductions including multiple protein post-translational modifications (PTM), protein-protein interactions (PPI), and protein-nucleic acid interaction (PNI) play critical roles for cell proliferation and differentiation that are directly related to the cancer biology. Traditional methods, like mass spectrometry, immunoprecipitation, fluorescence resonance energy transfer, and fluorescence correlation spectroscopy require a large amount of sample and long processing time. "microchannel for multiple-parameter analysis of proteins in single-complex (mMAPS)"we proposed can reduce the process time and sample volume because this system is composed by microfluidic channels, fluorescence microscopy, and computerized data analysis. In this paper, we will present an automated mMAPS including integrated microfluidic device, automated stage and electrical relay for high-throughput clinical screening. Based on this result, we estimated that this automated detection system will be able to screen approximately 150 patient samples in a 24-hour period, providing a practical application to analyze tissue samples in a clinical setting.

Automated detection system of single nucleotide polymorphisms using two kinds of functional magnetic nanoparticles

NASA Astrophysics Data System (ADS)

Liu, Hongna; Li, Song; Wang, Zhifei; Li, Zhiyang; Deng, Yan; Wang, Hua; Shi, Zhiyang; He, Nongyue

2008-11-01

Single nucleotide polymorphisms (SNPs) comprise the most abundant source of genetic variation in the human genome wide codominant SNPs identification. Therefore, large-scale codominant SNPs identification, especially for those associated with complex diseases, has induced the need for completely high-throughput and automated SNP genotyping method. Herein, we present an automated detection system of SNPs based on two kinds of functional magnetic nanoparticles (MNPs) and dual-color hybridization. The amido-modified MNPs (NH 2-MNPs) modified with APTES were used for DNA extraction from whole blood directly by electrostatic reaction, and followed by PCR, was successfully performed. Furthermore, biotinylated PCR products were captured on the streptavidin-coated MNPs (SA-MNPs) and interrogated by hybridization with a pair of dual-color probes to determine SNP, then the genotype of each sample can be simultaneously identified by scanning the microarray printed with the denatured fluorescent probes. This system provided a rapid, sensitive and highly versatile automated procedure that will greatly facilitate the analysis of different known SNPs in human genome.

Automated calculation of the Tei index from signal averaged left ventricular acoustic quantification wave forms.

PubMed

Spencer, Kirk T; Weinert, Lynn; Avi, Victor Mor; Decara, Jeanne; Lang, Roberto M

2002-12-01

The Tei index is a combined measurement of systolic and diastolic left ventricular (LV) performance and may be more useful for the diagnosis of global cardiac dysfunction than either systolic or diastolic measures alone. We sought to determine whether the Tei index could be accurately calculated from LV area waveforms generated with automated border detection. Twenty-four patients were studied in 3 groups: systolic dysfunction, diastolic dysfunction, and normal. The Tei index was calculated both from Doppler tracings and from analysis of LV area waveforms. Excellent agreement was found between Doppler-derived timing intervals and the Tei index with those obtained from averaged LV area waveforms. A significant difference was seen in the Tei index, computed with both Doppler and automated border detection techniques, between the normal group and those with LV systolic dysfunction and subjects with isolated diastolic dysfunction. This study validates the use of LV area waveforms for the automated calculation of the Tei index.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

PubMed

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Automated detection of jet contrails using the AVHRR split window

NASA Technical Reports Server (NTRS)

Engelstad, M.; Sengupta, S. K.; Lee, T.; Welch, R. M.

1992-01-01

This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.

[Advances in automatic detection technology for images of thin blood film of malaria parasite].

PubMed

Juan-Sheng, Zhang; Di-Qiang, Zhang; Wei, Wang; Xiao-Guang, Wei; Zeng-Guo, Wang

2017-05-05

This paper reviews the computer vision and image analysis studies aiming at automated diagnosis or screening of malaria in microscope images of thin blood film smears. On the basis of introducing the background and significance of automatic detection technology, the existing detection technologies are summarized and divided into several steps, including image acquisition, pre-processing, morphological analysis, segmentation, count, and pattern classification components. Then, the principles and implementation methods of each step are given in detail. In addition, the promotion and application in automatic detection technology of thick blood film smears are put forwarded as questions worthy of study, and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

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

DeGange, A.R.; Douglas, D.C.; Monson, D.H.

Sea otter (Enhydra lutris) abundance and distribution in the Gulf of Alaska west of Prince William Sound were surveyed by helicopter in the spring of 1989 at the time of the Exxon Valdez oil spill and the following fall. Estimated population sizes did not significantly decline between spring and fall for areas with comparable survey data. No significant (p>0.05) shifts of sea otter distributions in heavily, lightly and unoiled areas were detected between spring and fall surveys.

Automation based on knowledge modeling theory and its applications in engine diagnostic systems using Space Shuttle Main Engine vibrational data. M.S. Thesis

NASA Technical Reports Server (NTRS)

Kim, Jonnathan H.

1995-01-01

Humans can perform many complicated tasks without explicit rules. This inherent and advantageous capability becomes a hurdle when a task is to be automated. Modern computers and numerical calculations require explicit rules and discrete numerical values. In order to bridge the gap between human knowledge and automating tools, a knowledge model is proposed. Knowledge modeling techniques are discussed and utilized to automate a labor and time intensive task of detecting anomalous bearing wear patterns in the Space Shuttle Main Engine (SSME) High Pressure Oxygen Turbopump (HPOTP).

Investigating passage of ESA-listed juvenile fall Chinook salmon at Lower Granite Dam during winter when the fish bypass system is not operated

USGS Publications Warehouse

Kock, Tobias J.; Tiffan, Kenneth F.; Connor, William P.

2007-01-01

During the winter of 2006-07, we radio and passive integrated transponder (PIT) tagged, and released 99 juvenile fall Chinook salmon to evaluate over-wintering behavior and dam passage in the lower Snake River, Washington. All fish were released 10 km upstream of Lower Granite Dam at Granite Point in early November, 2006. Fixed radio telemetry detection sites located in the forebay and tailrace areas of Lower Granite, Little Goose, Lower Monumental, Ice Harbor, Bonneville dams, and at Lyle, Washington were used to monitor fish movements and dam passage through early-May 2007. Of the 99 fish released during our study, 80 passed Lower Granite Dam and were detected at downstream detection sites, 37 passed Little Goose Dam, 41 passed Lower Monumental Dam, 31 passed Ice Harbor Dam, 18 passed Lyle, WA, and 13 passed Bonneville Dam. Of the fish that passed Lower Granite Dam in the fall, 63 fish did so during the extended bypass period from November 1 through December 16. Of these fish, 53 were also detected by the PIT-tag interrogation system. Fifteen of the fish that passed Lower Granite Dam in the fall continued to pass lower Snake River dams and exit the system by the end of January. The remaining fish either died, their tags failed, or they resided in Little Goose Reservoir until spring when relatively few continued their seaward migration. Passage of tagged fish past lower Snake River dams generally declined during the winter as temperatures decreased, but increased again in the spring as temperatures and flows increased. Fish residence times in reservoirs and forebays was lengthy during the winter (up to 160 d), and varied by reservoir and time of year. We observed no diel trends in fish passage. Very few fish were detected at PIT-tag interrogation sites in the spring compared to detection by radio telemetry detection sites indicating that fish may have passed via spill. We believe that passage of overwintering juvenile fall Chinook salmon during winter is due more to chance than directed downstream movement. Since the primary route of passage during the winter is through powerhouse turbines, the potential exists for increased mortality for over-wintering juvenile fall Chinook salmon in the Snake River. Our findings that some fish can pass undetected during the winter likely bias traditional smolt-to-adult return rate calculations that are typically used to measure the success of juvenile transportation studies.

A system for ubiquitous fall monitoring at home via a wireless sensor network.

PubMed

Fernandez-Luque, Francisco J; Zapata, Juan; Ruiz, Ramon

2010-01-01

Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic. Falls are the most common causes of serious injuries and a major health threats in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to the elderly person who has suffered an accident at home. In this paper, we present a support system for detecting falls of an elder person by a static wireless nonintrusive sensorial infrastructure based on heterogenous sensor nodes. This previous infrastructure, named AID (Alarm Intelligent Device), is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. We have developed, different to other contributions, a specific low-power multi-hop network consists of nodes (Motes) that wirelessly communicate to each other and are capable of hopping radio messages to a base station where they are passed to a PC (or other possible client). The goal of this project is 1) to provide alerts to caregivers in the event of an accident, acute illness or strange (possibly dangerous) activities, and 2) to enable that authorized and authenticated caregivers by means of a itinerant wearable mote can be inserted into mesh and interact with it. In this paper, we describe an ubiquitous assistential monitoring system at home.

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

2005-01-01

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

PubMed

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Use of tracheal auscultation for the assessment of bronchial responsiveness in asthmatic children.

PubMed Central

Sprikkelman, A. B.; Grol, M. H.; Lourens, M. S.; Gerritsen, J.; Heymans, H. S.; van Aalderen, W. M.

1996-01-01

BACKGROUND: It can be difficult to assess bronchial responsiveness in children because of their inability to perform spirometric tests reliably. In bronchial challenges lung sounds could be used to detect the required 20% fall in the forced expiratory volume in one second (FEV1). A study was undertaken to determine whether a change in lung sounds corresponded with a 20% fall in FEV1 after methacholine challenge, and whether the occurrence of wheeze was the most important change. METHODS: Fifteen children with asthma (eight boys) of mean age 10.8 years (range 8-15) were studied. All had normal chest auscultation before the methacholine challenge test. Lung sounds were recorded over the trachea for one minute and stored on tape. They were analysed directly and also scored blindly from the tape recording by a second investigator. Wheeze, cough, increase in respiratory rate, and prolonged expiration were assessed. RESULTS: The total cumulative methacholine dose causing a fall in FEV1 of 20% or more (PD20) was detected in 12 children by a change in lung sounds - in four by wheeze and in eight by cough, increased respiratory rate, and/or prolonged expiration. In two subjects altered lung sounds were detectable one dose step before PD20 was reached. In three cases in whom no fall in FEV1 occurred, no change in lung sounds could be detected at the highest methacholine dose. CONCLUSION: Changes in lung sounds correspond well with a 20% fall in FEV1 after methacholine challenge. Wheeze is an insensitive indicator for assessing bronchial responsiveness. Cough, increase in respiratory rate, and prolonged expiration occurs more frequently. PMID:8779140

Human-system Interfaces to Automatic Systems: Review Guidance and Technical Basis

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

OHara, J.M.; Higgins, J.C.

Automation has become ubiquitous in modern complex systems and commercial nuclear power plants are no exception. Beyond the control of plant functions and systems, automation is applied to a wide range of additional functions including monitoring and detection, situation assessment, response planning, response implementation, and interface management. Automation has become a 'team player' supporting plant personnel in nearly all aspects of plant operation. In light of the increasing use and importance of automation in new and future plants, guidance is needed to enable the NRC staff to conduct safety reviews of the human factors engineering (HFE) aspects of modern automation.more » The objective of the research described in this report was to develop guidance for reviewing the operator's interface with automation. We first developed a characterization of the important HFE aspects of automation based on how it is implemented in current systems. The characterization included five dimensions: Level of automation, function of automation, modes of automation, flexibility of allocation, and reliability of automation. Next, we reviewed literature pertaining to the effects of these aspects of automation on human performance and the design of human-system interfaces (HSIs) for automation. Then, we used the technical basis established by the literature to develop design review guidance. The guidance is divided into the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, we identified insights into the automaton design process, operator training, and operations.« less

Visual texture for automated characterisation of geological features in borehole televiewer imagery

NASA Astrophysics Data System (ADS)

Al-Sit, Waleed; Al-Nuaimy, Waleed; Marelli, Matteo; Al-Ataby, Ali

2015-08-01

Detailed characterisation of the structure of subsurface fractures is greatly facilitated by digital borehole logging instruments, the interpretation of which is typically time-consuming and labour-intensive. Despite recent advances towards autonomy and automation, the final interpretation remains heavily dependent on the skill, experience, alertness and consistency of a human operator. Existing computational tools fail to detect layers between rocks that do not exhibit distinct fracture boundaries, and often struggle characterising cross-cutting layers and partial fractures. This paper presents a novel approach to the characterisation of planar rock discontinuities from digital images of borehole logs. Multi-resolution texture segmentation and pattern recognition techniques utilising Gabor filters are combined with an iterative adaptation of the Hough transform to enable non-distinct, partial, distorted and steep fractures and layers to be accurately identified and characterised in a fully automated fashion. This approach has successfully detected fractures and layers with high detection accuracy and at a relatively low computational cost.

Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot

NASA Astrophysics Data System (ADS)

Jovančević, Igor; Larnier, Stanislas; Orteu, Jean-José; Sentenac, Thierry

2015-11-01

This paper deals with an automated preflight aircraft inspection using a pan-tilt-zoom camera mounted on a mobile robot moving autonomously around the aircraft. The general topic is image processing framework for detection and exterior inspection of different types of items, such as closed or unlatched door, mechanical defect on the engine, the integrity of the empennage, or damage caused by impacts or cracks. The detection step allows to focus on the regions of interest and point the camera toward the item to be checked. It is based on the detection of regular shapes, such as rounded corner rectangles, circles, and ellipses. The inspection task relies on clues, such as uniformity of isolated image regions, convexity of segmented shapes, and periodicity of the image intensity signal. The approach is applied to the inspection of four items of Airbus A320: oxygen bay handle, air-inlet vent, static ports, and fan blades. The results are promising and demonstrate the feasibility of an automated exterior inspection.

An automated detection for axonal boutons in vivo two-photon imaging of mouse

NASA Astrophysics Data System (ADS)

Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

2017-02-01

Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

PubMed Central

Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.

2009-01-01

BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728

Synthesis of visibility detection systems.

DOT National Transportation Integrated Search

2012-10-01

Visibility is a critical component to the task of driving on all types of roads. The visibility detection and warning systems provide real-time, automated detection as well as appropriate responses to counteract reduced visibility conditions due to f...

Involvement of the end user: exploration of older people’s needs and preferences for a wearable fall detection device – a qualitative descriptive study

PubMed Central

Thilo, Friederike JS; Bilger, Selina; Halfens, Ruud JG; Schols, Jos MGA; Hahn, Sabine

2017-01-01

Purpose To explore the needs and preferences of community-dwelling older people, by involving them in the device design and mock-up development stage of a fall detection device, consisting of a body-worn sensor linked to a smartphone application. Patients and methods A total of 22 community-dwelling persons 75 years of age and older were involved in the development of a fall detection device. Three semistructured focus group interviews were conducted. The interview data were analyzed using qualitative descriptive analysis with deductive coding. Results The mock-up of a waterproof, body-worn, automatic and manual alerting device, which served both as a day-time wearable sensor and a night-time wearable sensor, was welcomed. Changes should be considered regarding shape, color and size along with alternate ways of integrating the sensor with items already in use in daily life, such as jewelry and personal watches. The reliability of the sensor is key for the participants. Issues important to the alerting process were discussed, for instance, who should be contacted and why. Several participants were concerned with the mandatory use of the smartphone and assumed that it would be difficult to use. They criticized the limited distance between the sensor and the smartphone for reliable fall detection, as it might restrict activity and negatively influence their degree of independence in daily life. Conclusion This study supports that involving end users in the design and mock-up development stage is welcomed by older people and allows their needs and preferences concerning the fall detection device to be explored. Based on these findings, the development of a “need-driven” prototype is possible. As participants are doubtful regarding smartphone usage, careful training and support of community-dwelling older people during real field testing will be crucial. PMID:28053509

Simple Fall Criteria for MEMS Sensors: Data Analysis and Sensor Concept

PubMed Central

Ibrahim, Alwathiqbellah; Younis, Mohammad I.

2014-01-01

This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept. PMID:25006997

Laboratory review: the role of gait analysis in seniors' mobility and fall prevention.

PubMed

Bridenbaugh, Stephanie A; Kressig, Reto W

2011-01-01

Walking is a complex motor task generally performed automatically by healthy adults. Yet, by the elderly, walking is often no longer performed automatically. Older adults require more attention for motor control while walking than younger adults. Falls, often with serious consequences, can be the result. Gait impairments are one of the biggest risk factors for falls. Several studies have identified changes in certain gait parameters as independent predictors of fall risk. Such gait changes are often too discrete to be detected by clinical observation alone. At the Basel Mobility Center, we employ the GAITRite electronic walkway system for spatial-temporal gait analysis. Although we have a large range of indications for gait analyses and several areas of clinical research, our focus is on the association between gait and cognition. Gait analysis with walking as a single-task condition alone is often insufficient to reveal underlying gait disorders present during normal, everyday activities. We use a dual-task paradigm, walking while simultaneously performing a second cognitive task, to assess the effects of divided attention on motor performance and gait control. Objective quantification of such clinically relevant gait changes is necessary to determine fall risk. Early detection of gait disorders and fall risk permits early intervention and, in the best-case scenario, fall prevention. We and others have shown that rhythmic movement training such as Jaques-Dalcroze eurhythmics, tai chi and social dancing can improve gait regularity and automaticity, thus increasing gait safety and reducing fall risk. Copyright © 2010 S. Karger AG, Basel.

Automated pattern analysis: A newsilent partner in insect acoustic detection studies

USDA-ARS?s Scientific Manuscript database

This seminar reviews methods that have been developed for automated analysis of field-collected sounds used to estimate pest populations and guide insect pest management decisions. Several examples are presented of successful usage of acoustic technology to map insect distributions in field environ...

Automated inspection of bread and loaves

NASA Astrophysics Data System (ADS)

Batchelor, Bruce G.

1993-08-01

The prospects for building practical automated inspection machines, capable of detecting the following faults in ordinary, everyday loaves are reviewed: (1) foreign bodies, using X-rays, (2) texture changes, using glancing illumination, mathematical morphology and Neural Net learning techniques, and (3) shape deformations, using structured lighting and simple geometry.

Development of a Risk Assessment Tool to Predict Fall-Related Severe Injuries Occurring in a Hospital

PubMed Central

Toyabe, Shin-ichi

2014-01-01

Inpatient falls are the most common adverse events that occur in a hospital, and about 3 to 10% of falls result in serious injuries such as bone fractures and intracranial haemorrhages. We previously reported that bone fractures and intracranial haemorrhages were two major fall-related injuries and that risk assessment score for osteoporotic bone fracture was significantly associated not only with bone fractures after falls but also with intracranial haemorrhage after falls. Based on the results, we tried to establish a risk assessment tool for predicting fall-related severe injuries in a hospital. Possible risk factors related to fall-related serious injuries were extracted from data on inpatients that were admitted to a tertiary-care university hospital by using multivariate Cox’ s regression analysis and multiple logistic regression analysis. We found that fall risk score and fracture risk score were the two significant factors, and we constructed models to predict fall-related severe injuries incorporating these factors. When the prediction model was applied to another independent dataset, the constructed model could detect patients with fall-related severe injuries efficiently. The new assessment system could identify patients prone to severe injuries after falls in a reproducible fashion. PMID:25168984

Automated volumetric segmentation of retinal fluid on optical coherence tomography

PubMed Central

Wang, Jie; Zhang, Miao; Pechauer, Alex D.; Liu, Liang; Hwang, Thomas S.; Wilson, David J.; Li, Dengwang; Jia, Yali

2016-01-01

We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation. PMID:27446676

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Hydrometeor Size Distribution Measurements by Imaging the Attenuation of a Laser Spot

NASA Technical Reports Server (NTRS)

Lane, John

2013-01-01

The optical extinction of a laser due to scattering of particles is a well-known phenomenon. In a laboratory environment, this physical principle is known as the Beer-Lambert law, and is often used to measure the concentration of scattering particles in a fluid or gas. This method has been experimentally shown to be a usable means to measure the dust density from a rocket plume interaction with the lunar surface. Using the same principles and experimental arrangement, this technique can be applied to hydrometeor size distributions, and for launch-pad operations, specifically as a passive hail detection and measurement system. Calibration of a hail monitoring system is a difficult process. In the past, it has required comparison to another means of measuring hydrometeor size and density. Using a technique recently developed for estimating the density of surface dust dispersed during a rocket landing, measuring the extinction of a laser passing through hail (or dust in the rocket case) yields an estimate of the second moment of the particle cloud, and hydrometeor size distribution in the terrestrial meteorological case. With the exception of disdrometers, instruments that measure rain and hail fall make indirect measurements of the drop-size distribution. Instruments that scatter microwaves off of hydrometeors, such as the WSR-88D (Weather Surveillance Radar 88 Doppler), vertical wind profilers, and microwave disdrometers, measure the sixth moment of the drop size distribution (DSD). By projecting a laser onto a target, changes in brightness of the laser spot against the target background during rain and hail yield a measurement of the DSD's second moment by way of the Beer-Lambert law. In order to detect the laser attenuation within the 8-bit resolution of most camera image arrays, a minimum path length is required. Depending on the intensity of the hail fall rate for moderate to heavy rainfall, a laser path length of 100 m is sufficient to measure variations in optical extinction using a digital camera. For hail fall only, the laser path may be shorter because of greater scattering due to the properties of hailstones versus raindrops. A photodetector may replace the camera in automated installations. Laser-based rain and hail measurement systems are available, but they are based on measuring the interruption of a thin laser beam, thus counting individual hydrometeors. These systems are true disdrometers since they also measure size and velocity. The method reported here is a simple method, requiring far less processing, but it is not a disdrometer.

Automated determination of arterial input function for DCE-MRI of the prostate

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Chang, Ming-Ching; Gupta, Sandeep

2011-03-01

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.

A comparison of damage profiling of automated tap testers on aircraft CFRP panel

NASA Astrophysics Data System (ADS)

Mohd Aris, K. D.; Shariff, M. F.; Abd Latif, B. R.; Mohd Haris, M. Y.; Baidzawi, I. J.

2017-12-01

The use of composite materials nevertheless is getting more prominent. The combination of reinforcing fibers and matrices will produce the desired strength orientation, tailorability and not to mention the complex shape that is hard to form on metallic structure. The weight percentage of composite materials used in aerospace, civil, marine etc. has increased tremendously. Since composite are stacked together, the possibility of delamination and/disbond defects are highly present either in the monolithic or sandwich structures. Tap test is the cheapest form of nondestructive test to identify the presence of this damage. However, its inconsistency and wide area of coverage can reduce its effectivity since it is carried out manually. The indigenous automated tap tester known as KETOK was used to detect the damage due to trapped voids and air pockets. The mechanism of detection is through controlling the tapping on the surface automatically at a constant rate. Another manual tap tester RD-3 from Wichitech Industries Inc. was used as reference. The acquired data was translated into damage profiling and both results were compared. The results have shown that the indigenous automated tester can profile the damage better when compared with the existing tap tester. As a conclusion, the indigenous automated tap tester has a potential to be used as an IN-SITU damage detection tool to detect delamination and disbond damage on composite panel. However, more conclusive tests need to be done in order to make the unit available to conventional users.

Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images.

PubMed

Seghier, Mohamed L; Kolanko, Magdalena A; Leff, Alexander P; Jäger, Hans R; Gregoire, Simone M; Werring, David J

2011-03-23

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

The Distribution and Behaviour of Photospheric Magnetic Features

NASA Astrophysics Data System (ADS)

Parnell, C. E.; Lamb, D. A.; DeForest, C. E.

2014-12-01

Over the past two decades enormous amounts of data on the magnetic fields of the solar photosphere have been produced by both ground-based (Kitt Peak & SOLIS), as well as space-based instruments (MDI, Hinode & HMI). In order to study the behaviour and distribution of photospheric magnetic features, efficient automated detection routines need to be utilised to identify and track magnetic features. In this talk, I will discuss the pros and cons of different automated magnetic feature identification and tracking routines with a special focus on the requirements of these codes to deal with the large data sets produced by HMI. By patching together results from Hinode and MDI (high-res & full-disk), the fluxes of magnetic features were found to follow a power-law over 5 orders of magnitude. At the strong flux tail of this distribution, the power law was found to fall off at solar minimum, but was maintained over all fluxes during solar maximum. However, the point of deflection in the power-law distribution occurs at a patching point between instruments and so questions remain over the reasons for the deflection. The feature fluxes determined from the superb high-resolution HMI data covers almost all of the 5 orders of magnitude. Considering both solar mimimum and solar maximum HMI data sets, we investigate whether the power-law over 5 orders of magnitude in flux still holds. Furthermore, we investigate the behaviour of magnetic features in order to probe the nature of their origin. In particular, we analyse small-scale flux emergence events using HMI data to investigate the existence of a small-scale dynamo just below the solar photosphere.

Automation bias: decision making and performance in high-tech cockpits.

PubMed

Mosier, K L; Skitka, L J; Heers, S; Burdick, M

1997-01-01

Automated aids and decision support tools are rapidly becoming indispensable tools in high-technology cockpits and are assuming increasing control of"cognitive" flight tasks, such as calculating fuel-efficient routes, navigating, or detecting and diagnosing system malfunctions and abnormalities. This study was designed to investigate automation bias, a recently documented factor in the use of automated aids and decision support systems. The term refers to omission and commission errors resulting from the use of automated cues as a heuristic replacement for vigilant information seeking and processing. Glass-cockpit pilots flew flight scenarios involving automation events or opportunities for automation-related omission and commission errors. Although experimentally manipulated accountability demands did not significantly impact performance, post hoc analyses revealed that those pilots who reported an internalized perception of "accountability" for their performance and strategies of interaction with the automation were significantly more likely to double-check automated functioning against other cues and less likely to commit errors than those who did not share this perception. Pilots were also lilkely to erroneously "remember" the presence of expected cues when describing their decision-making processes.

Progress of artificial pancreas devices towards clinical use: the first outpatient studies.

PubMed

Russell, Steven J

2015-04-01

This article describes recent progress in the automated control of glycemia in type 1 diabetes with artificial pancreas devices that combine continuous glucose monitoring with automated decision-making and insulin delivery. After a gestation period of closely supervised feasibility studies in research centers, the last 2 years have seen publication of studies testing these devices in outpatient environments, and many more such studies are ongoing. The most basic form of automation, suspension of insulin delivery for actual or predicted hypoglycemia, has been shown to be effective and well tolerated, and a first-generation device has actually reached the market. Artificial pancreas devices that actively dose insulin fall into two categories, those that dose insulin alone and those that also use glucagon to prevent and treat hypoglycemia (bihormonal artificial pancreas). Initial outpatient clinical trials have shown that both strategies can improve glycemic management in comparison with patient-controlled insulin pump therapy, but only the bihormonal strategy has been tested without restrictions on exercise. Artificial pancreas technology has the potential to reduce acute and chronic complications of diabetes and mitigate the burden of diabetes self-management. Successful outpatient studies bring these technologies one step closer to availability for patients.

Improving the Operations of the Earth Observing One Mission via Automated Mission Planning

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Tran, Daniel; Rabideau, Gregg; Schaffer, Steve; Mandl, Daniel; Frye, Stuart

2010-01-01

We describe the modeling and reasoning about operations constraints in an automated mission planning system for an earth observing satellite - EO-1. We first discuss the large number of elements that can be naturally represented in an expressive planning and scheduling framework. We then describe a number of constraints that challenge the current state of the art in automated planning systems and discuss how we modeled these constraints as well as discuss tradeoffs in representation versus efficiency. Finally we describe the challenges in efficiently generating operations plans for this mission. These discussions involve lessons learned from an operations model that has been in use since Fall 2004 (called R4) as well as a newer more accurate operations model operational since June 2009 (called R5). We present analysis of the R5 software documenting a significant (greater than 50%) increase in the number of weekly observations scheduled by the EO-1 mission. We also show that the R5 mission planning system produces schedules within 15% of an upper bound on optimal schedules. This operational enhancement has created value of millions of dollars US over the projected remaining lifetime of the EO-1 mission.

Automated sinkhole detection using a DEM subsetting technique and fill tools at Mammoth Cave National Park

NASA Astrophysics Data System (ADS)

Wall, J.; Bohnenstiehl, D. R.; Levine, N. S.

2013-12-01

An automated workflow for sinkhole detection is developed using Light Detection and Ranging (Lidar) data from Mammoth Cave National Park (MACA). While the park is known to sit within a karst formation, the generally dense canopy cover and the size of the park (~53,000 acres) creates issues for sinkhole inventorying. Lidar provides a useful remote sensing technology for peering beneath the canopy in hard to reach areas of the park. In order to detect sinkholes, a subsetting technique is used to interpolate a Digital Elevation Model (DEM) thereby reducing edge effects. For each subset, standard GIS fill tools are used to fill depressions within the DEM. The initial DEM is then subtracted from the filled DEM resulting in detected depressions or sinkholes. Resulting depressions are then described in terms of size and geospatial trend.

ABO Mistyping of cis-AB Blood Group by the Automated Microplate Technique.

PubMed

Chun, Sejong; Ryu, Mi Ra; Cha, Seung-Yeon; Seo, Ji-Young; Cho, Duck

2018-01-01

The cis -AB phenotype, although rare, is the relatively most frequent of ABO subgroups in Koreans. To prevent ABO mistyping of cis -AB samples, our hospital has applied a combination of the manual tile method with automated devices. Herein, we report cases of ABO mistyping detected by the combination testing system. Cases that showed discrepant results by automated devices and the manual tile method were evaluated. These samples were also tested by the standard tube method. The automated devices used in this study were a QWALYS-3 and Galileo NEO. Exons 6 and 7 of the ABO gene were sequenced. 13 cases that had the cis -AB allele showed results suggestive of the cis -AB subgroup by manual methods, but were interpreted as AB by either automated device. This happened in 87.5% of these cases by QWALYS-3 and 70.0% by Galileo NEO. Genotyping results showed that 12 cases were ABO*cis-AB01/ABO*O01 or ABO*cis-AB01/ABO*O02 , and one case was ABO*cis-AB01/ ABO*A102. Cis -AB samples were mistyped as AB by the automated microplate technique in some cases. We suggest that the manual tile method can be a simple supplemental test for the detection of the cis -AB phenotype, especially in countries with relatively high cis- AB prevalence.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans

PubMed Central

Smits, Loek P.; van Wijk, Diederik F.; Duivenvoorden, Raphael; Xu, Dongxiang; Yuan, Chun; Stroes, Erik S.; Nederveen, Aart J.

2016-01-01

Purpose To study the interscan reproducibility of manual versus automated segmentation of carotid artery plaque components, and the agreement between both methods, in high and lower quality MRI scans. Methods 24 patients with 30–70% carotid artery stenosis were planned for 3T carotid MRI, followed by a rescan within 1 month. A multicontrast protocol (T1w,T2w, PDw and TOF sequences) was used. After co-registration and delineation of the lumen and outer wall, segmentation of plaque components (lipid-rich necrotic cores (LRNC) and calcifications) was performed both manually and automated. Scan quality was assessed using a visual quality scale. Results Agreement for the detection of LRNC (Cohen’s kappa (k) is 0.04) and calcification (k = 0.41) between both manual and automated segmentation methods was poor. In the high-quality scans (visual quality score ≥ 3), the agreement between manual and automated segmentation increased to k = 0.55 and k = 0.58 for, respectively, the detection of LRNC and calcification larger than 1 mm2. Both manual and automated analysis showed good interscan reproducibility for the quantification of LRNC (intraclass correlation coefficient (ICC) of 0.94 and 0.80 respectively) and calcified plaque area (ICC of 0.95 and 0.77, respectively). Conclusion Agreement between manual and automated segmentation of LRNC and calcifications was poor, despite a good interscan reproducibility of both methods. The agreement between both methods increased to moderate in high quality scans. These findings indicate that image quality is a critical determinant of the performance of both manual and automated segmentation of carotid artery plaque components. PMID:27930665

Evaluation of three automated nucleic acid extraction systems for identification of respiratory viruses in clinical specimens by multiplex real-time PCR.

PubMed

Kim, Yoonjung; Han, Mi-Soon; Kim, Juwon; Kwon, Aerin; Lee, Kyung-A

2014-01-01

A total of 84 nasopharyngeal swab specimens were collected from 84 patients. Viral nucleic acid was extracted by three automated extraction systems: QIAcube (Qiagen, Germany), EZ1 Advanced XL (Qiagen), and MICROLAB Nimbus IVD (Hamilton, USA). Fourteen RNA viruses and two DNA viruses were detected using the Anyplex II RV16 Detection kit (Seegene, Republic of Korea). The EZ1 Advanced XL system demonstrated the best analytical sensitivity for all the three viral strains. The nucleic acids extracted by EZ1 Advanced XL showed higher positive rates for virus detection than the others. Meanwhile, the MICROLAB Nimbus IVD system was comprised of fully automated steps from nucleic extraction to PCR setup function that could reduce human errors. For the nucleic acids recovered from nasopharyngeal swab specimens, the QIAcube system showed the fewest false negative results and the best concordance rate, and it may be more suitable for detecting various viruses including RNA and DNA virus strains. Each system showed different sensitivity and specificity for detection of certain viral pathogens and demonstrated different characteristics such as turnaround time and sample capacity. Therefore, these factors should be considered when new nucleic acid extraction systems are introduced to the laboratory.

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network

NASA Astrophysics Data System (ADS)

Hiramatsu, Yuya; Muramatsu, Chisako; Kobayashi, Hironobu; Hara, Takeshi; Fujita, Hiroshi

2017-03-01

Breast cancer screening with mammography and ultrasonography is expected to improve sensitivity compared with mammography alone, especially for women with dense breast. An automated breast volume scanner (ABVS) provides the operator-independent whole breast data which facilitate double reading and comparison with past exams, contralateral breast, and multimodality images. However, large volumetric data in screening practice increase radiologists' workload. Therefore, our goal is to develop a computer-aided detection scheme of breast masses in ABVS data for assisting radiologists' diagnosis and comparison with mammographic findings. In this study, false positive (FP) reduction scheme using deep convolutional neural network (DCNN) was investigated. For training DCNN, true positive and FP samples were obtained from the result of our initial mass detection scheme using the vector convergence filter. Regions of interest including the detected regions were extracted from the multiplanar reconstraction slices. We investigated methods to select effective FP samples for training the DCNN. Based on the free response receiver operating characteristic analysis, simple random sampling from the entire candidates was most effective in this study. Using DCNN, the number of FPs could be reduced by 60%, while retaining 90% of true masses. The result indicates the potential usefulness of DCNN for FP reduction in automated mass detection on ABVS images.

Rapid, automated, parallel quantitative immunoassays using highly integrated microfluidics and AlphaLISA

PubMed Central

Tak For Yu, Zeta; Guan, Huijiao; Ki Cheung, Mei; McHugh, Walker M.; Cornell, Timothy T.; Shanley, Thomas P.; Kurabayashi, Katsuo; Fu, Jianping

2015-01-01

Immunoassays represent one of the most popular analytical methods for detection and quantification of biomolecules. However, conventional immunoassays such as ELISA and flow cytometry, even though providing high sensitivity and specificity and multiplexing capability, can be labor-intensive and prone to human error, making them unsuitable for standardized clinical diagnoses. Using a commercialized no-wash, homogeneous immunoassay technology (‘AlphaLISA’) in conjunction with integrated microfluidics, herein we developed a microfluidic immunoassay chip capable of rapid, automated, parallel immunoassays of microliter quantities of samples. Operation of the microfluidic immunoassay chip entailed rapid mixing and conjugation of AlphaLISA components with target analytes before quantitative imaging for analyte detections in up to eight samples simultaneously. Aspects such as fluid handling and operation, surface passivation, imaging uniformity, and detection sensitivity of the microfluidic immunoassay chip using AlphaLISA were investigated. The microfluidic immunoassay chip could detect one target analyte simultaneously for up to eight samples in 45 min with a limit of detection down to 10 pg mL−1. The microfluidic immunoassay chip was further utilized for functional immunophenotyping to examine cytokine secretion from human immune cells stimulated ex vivo. Together, the microfluidic immunoassay chip provides a promising high-throughput, high-content platform for rapid, automated, parallel quantitative immunosensing applications. PMID:26074253

Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

NASA Astrophysics Data System (ADS)

Janugani, S.; Jayaram, V.; Cabrera, S. D.; Rosiles, J. G.; Gill, T. E.; Rivera Rivera, N.

2009-05-01

In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.

Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images

NASA Astrophysics Data System (ADS)

Rogowska, Jadwiga; Brezinski, Mark E.

2002-02-01

Osteoarthritis, whose hallmark is the progressive loss of joint cartilage, is a major cause of morbidity worldwide. Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the assessment of articular cartilage. Among the most important parameters to be assessed is cartilage width. However, detection of the bone cartilage interface is critical for the assessment of cartilage width. At present, the quantitative evaluations of cartilage thickness are being done using manual tracing of cartilage-bone borders. Since data is being obtained near video rate with OCT, automated identification of the bone-cartilage interface is critical. In order to automate the process of boundary detection on OCT images, there is a need for developing new image processing techniques. In this paper we describe the image processing techniques for speckle removal, image enhancement and segmentation of cartilage OCT images. In particular, this paper focuses on rabbit cartilage since this is an important animal model for testing both chondroprotective agents and cartilage repair techniques. In this study, a variety of techniques were examined. Ultimately, by combining an adaptive filtering technique with edge detection (vertical gradient, Sobel edge detection), cartilage edges can be detected. The procedure requires several steps and can be automated. Once the cartilage edges are outlined, the cartilage thickness can be measured.