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

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.

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%.

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.

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

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.

Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

PubMed Central

Bagalà, Fabio; Becker, Clemens; Cappello, Angelo; Chiari, Lorenzo; Aminian, Kamiar; Hausdorff, Jeffrey M.; Zijlstra, Wiebren; Klenk, Jochen

2012-01-01

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design

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.

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.

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.

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.

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.

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.

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

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.

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.

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%.

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

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.

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.

Fall Detection Using Smartphone Audio Features.

PubMed

Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

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.

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.

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.

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.

Fall Down Detection Under Smart Home System.

PubMed

Juang, Li-Hong; Wu, Ming-Ni

2015-10-01

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.

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

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%).

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

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.

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

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.

Automated Fall Detection With Quality Improvement “Rewind” to Reduce Falls in Hospital Rooms

PubMed Central

Rantz, Marilyn J.; Banerjee, Tanvi S.; Cattoor, Erin; Scott, Susan D.; Skubic, Marjorie; Popescu, Mihail

2014-01-01

The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy. PMID:24296567

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.

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.

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.

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.

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.

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.

Automated fall detection on privacy-enhanced video.

PubMed

Edgcomb, Alex; Vahid, Frank

2012-01-01

A privacy-enhanced video obscures the appearance of a person in the video. We consider four privacy enhancements: blurring of the person, silhouetting of the person, covering the person with a graphical box, and covering the person with a graphical oval. We demonstrate that an automated video-based fall detection algorithm can be as accurate on privacy-enhanced video as on raw video. The algorithm operated on video from a stationary in-home camera, using a foreground-background segmentation algorithm to extract a minimum bounding rectangle (MBR) around the motion in the video, and using time series shapelet analysis on the height and width of the rectangle to detect falls. We report accuracy applying fall detection on 23 scenarios depicted as raw video and privacy-enhanced videos involving a sole actor portraying normal activities and various falls. We found that fall detection on privacy-enhanced video, except for the common approach of blurring of the person, was competitive with raw video, and in particular that the graphical oval privacy enhancement yielded the same accuracy as raw video, namely 0.91 sensitivity and 0.92 specificity.

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.

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

[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.

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.

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.

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

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.

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.

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.

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.

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

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

Detecting falls with wearable sensors using machine learning techniques.

PubMed

Özdemir, Ahmet Turan; Barshan, Billur

2014-06-18

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

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.

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

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.

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.

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.

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.

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.

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.

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.

Towards a Single Sensor Passive Solution for Automated Fall Detection

PubMed Central

Belshaw, Michael; Taati, Babak; Snoek, Jasper; Mihailidis, Alex

2012-01-01

Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person within the field-of-view has fallen. The system addresses concerns raised in earlier work and the requirements of a widely deployable in-home solution. The presented prototype utilizes a consumer-grade camera modified with a wide-angle lens. Machine learning techniques applied to carefully engineered features allow the system to classify falls at high accuracy while maintaining invariance to lighting, environment and the presence of multiple moving objects. This paper describes the system, outlines the algorithms used and presents empirical validation of its effectiveness. PMID:22254671

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.

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.

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.

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.

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.

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.

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.

Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network.

PubMed

Tao, Shuai; Kudo, Mineichi; Nonaka, Hidetoshi

2012-12-07

An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the "pixel values" as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc.) performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value), the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.

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.

Meteorite Fall Detection and Analysis via Weather Radar: Worldwide Potential for Citizen Science

NASA Astrophysics Data System (ADS)

Fries, M.; Bresky, C.; Laird, C.; Reddy, V.; Hankey, M.

2017-12-01

Meteorite falls can be detected using weather radars, facilitating rapid recovery of meteorites to minimize terrestrial alteration. Imagery from the US NEXRAD radar network reveals over two dozen meteorite falls where meteorites have been recovered, and about another dozen that remain unrecovered. Discovery of new meteorite falls is well suited to "citizen science" and similar outreach activities, as well as automation of computational components into internet-based search tools. Also, there are many more weather radars employed worldwide than those in the US NEXRAD system. Utilization of weather radars worldwide for meteorite recovery can not only expand citizen science opportunities but can also lead to significant improvement in the number of freshly-fallen meteorites available for research. We will discuss the methodologies behind locating and analyzing meteorite falls using weather radar, and how to make them available for citizen science efforts. An important example is the Aquarius Project, a Chicago-area consortium recently formed with the goal of recovering meteorites from Lake Michigan. This project has extensive student involvement geared toward development of actual hardware for recovering meteorites from the lake floor. Those meteorites were identified in weather radar imagery as they fell into the lake from a large meteor on 06 Feb 2017. Another example of public interaction is the meteor detection systems operated by the American Meteor Society (AMS). The AMS website has been developed to allow public reporting of meteors, effectively enabling citizen science to locate and describe significant meteor events worldwide.

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

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.

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

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.

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.

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.

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.

Estimation of the kinetic energy dissipation in fall-arrest system and manikin during fall impact.

PubMed

Wu, John Z; Powers, John R; Harris, James R; Pan, Christopher S

2011-04-01

Fall-arrest systems (FASs) have been widely applied to provide a safe stop during fall incidents for occupational activities. The mechanical interaction and kinetic energy exchange between the human body and the fall-arrest system during fall impact is one of the most important factors in FAS ergonomic design. In the current study, we developed a systematic approach to evaluate the energy dissipated in the energy absorbing lanyard (EAL) and in the harness/manikin during fall impact. The kinematics of the manikin and EAL during the impact were derived using the arrest-force time histories that were measured experimentally. We applied the proposed method to analyse the experimental data of drop tests at heights of 1.83 and 3.35 m. Our preliminary results indicate that approximately 84-92% of the kinetic energy is dissipated in the EAL system and the remainder is dissipated in the harness/manikin during fall impact. The proposed approach would be useful for the ergonomic design and performance evaluation of an FAS. STATEMENT OF RELEVANCE: Mechanical interaction, especially kinetic energy exchange, between the human body and the fall-arrest system during fall impact is one of the most important factors in the ergonomic design of a fall-arrest system. In the current study, we propose an approach to quantify the kinetic energy dissipated in the energy absorbing lanyard and in the harness/body system during fall impact.

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

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

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

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

.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.

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.

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.

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

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

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…

Horizontal fall arrest systems: rigid systems vs. flexible line systems.

PubMed

Lough, David

2004-09-01

There are many types of flexible and rigid systems on the market, both permanent and temporary. This article does not mean to encompass all possible systems or hazards and only intends to give an outline of what at a minimum should be examined to make an educated purchasing decision. In many instances, the buyer will use the same type of horizontal system for all situations. This is a good idea in some cases because it will reduce the need for training on a number of different systems, reduce system compatibility issues, and may reduce costs for installation, supply, and maintenance. This may not be the best idea if the hazard areas differ a great deal; as we have illustrated, one system may not function for all areas and tasks. The rigid system is typically the best solution simply based on the fact the worker won't fall as far as when he is connected to a flexible system, because of the elimination of any dynamic sag and horizontal energy absorber deployment. In any case, where you stop the worker from falling farther, you decrease the chance there may be an incident where the worker is injured. From a cost standpoint, flexible fall arrest systems typically are cheapest. In the end, safety professionals must balance the cost and effectiveness of the system to prevent an injury.

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.

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.

Utilization of the Seniors Falls Investigation Methodology to identify system-wide causes of falls in community-dwelling seniors.

PubMed

Zecevic, Aleksandra A; Salmoni, Alan W; Lewko, John H; Vandervoort, Anthoney A; Speechley, Mark

2009-10-01

As a highly heterogeneous group, seniors live in complex environments influenced by multiple physical and social structures that affect their safety. Until now, the major approach to falls research has been person centered. However, in industrial settings, the individuals involved in an accident are seen as the inheritors of system defects. The objective of the present study was to investigate safety deficiencies that contributed to falls in community-dwelling seniors using a systems approach. The investigations were conducted using the Seniors Falls Investigation Methodology (SFIM), an adapted version of a method used to examine transportation accidents, such as airplane crashes. Fifteen seniors, who experienced a fall or near fall, participated in multiple case studies. A cross-case synthesis was used to summarize findings and identify common patterns of causes and safety deficiencies. Falls and near falls are a result of latent unsafe conditions, and unsafe acts and decisions combined in a diverse set of circumstances. If not identified and removed, these unsafe conditions can cause falls for other seniors. This study provided compelling evidence that causes of falling are systemic and develop over time. It demonstrated that the systems approach is needed to expand the focus from the individual to multilayered organizational and supervisory causes. The SFIM demonstrated capability to identify causes of falls that will allow better prevention and management programs, hence advancing seniors' safety. SFIM shows great potential for implementation in organized settings, such as hospitals and long-term care homes.

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%.

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

PubMed Central

Santoyo-Ramón, José Antonio

2018-01-01

This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA). PMID:29642638

Performance of RASS vortex detection/measurement system

DOT National Transportation Integrated Search

1999-06-01

Preliminary tests conducted by WLR Research in the Fall of 1993 showed considerable promise that a Radio Acoustic Sounding System (RASS) was capable of detecting and tracking wake vortices located in the approach glide slope. Initial testing of the R...

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.

Pyroelectric IR sensor arrays for fall detection in the older population

NASA Astrophysics Data System (ADS)

Sixsmith, A.; Johnson, N.; Whatmore, R.

2005-09-01

Uncooled pyroelectric sensor arrays have been studied over many years for their uses in thermal imaging applications. These arrays will only detect changes in IR flux and so systems based upon them are very good at detecting movements of people in the scene without sensing the background, if they are used in staring mode. Relatively-low element count arrays (16 x 16) can be used for a variety of people sensing applications, including people counting (for safety applications), queue monitoring etc. With appropriate signal processing such systems can be also be used for the detection of particular events such as a person falling over. There is a considerable need for automatic fall detection amongst older people, but there are important limitations to some of the current and emerging technologies available for this. Simple sensors, such as 1 or 2 element pyroelectric infra-red sensors provide crude data that is difficult to interpret; the use of devices worn on the person, such as wrist communicator and motion detectors have potential, but are reliant on the person being able and willing to wear the device; video cameras may be seen as intrusive and require considerable human resources to monitor activity while machine-interpretation of camera images is complex and may be difficult in this application area. The use of a pyroelectric thermal array sensor was seen to have a number of potential benefits. The sensor is wall-mounted and does not require the user to wear a device. It enables detailed analysis of a subject's motion to be achieved locally, within the detector, using only a modest processor. This is possible due to the relative ease with which data from the sensor can be interpreted relative to the data generated by alternative sensors such as video devices. In addition to the cost-effectiveness of this solution, it was felt that the lack of detail in the low-level data, together with the elimination of the need to transmit data outside the detector

Ice detection systems : experimental feature : final report.

DOT National Transportation Integrated Search

1986-01-01

In the fall of 1980, an experimental ice detection system was installed on the Fremont Bridge in Portland, Oregon. this bridge, which caries I-405 over the Willamette River, has a history of icing problem when the deck is wet and the temperature hove...

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.

A multi-modal approach for activity classification and fall detection

NASA Astrophysics Data System (ADS)

Castillo, José Carlos; Carneiro, Davide; Serrano-Cuerda, Juan; Novais, Paulo; Fernández-Caballero, Antonio; Neves, José

2014-04-01

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

29 CFR 1915.159 - Personal fall arrest systems (PFAS).

Code of Federal Regulations, 2011 CFR

2011-07-01

..., DEPARTMENT OF LABOR (CONTINUED) OCCUPATIONAL SAFETY AND HEALTH STANDARDS FOR SHIPYARD EMPLOYMENT Personal Protective Equipment (PPE) § 1915.159 Personal fall arrest systems (PFAS). The criteria of this section apply... 29 Labor 7 2011-07-01 2011-07-01 false Personal fall arrest systems (PFAS). 1915.159 Section 1915...

Improving the Capture of Fall Events in Hospitals: Combining a Service for Evaluating Inpatient Falls with an Incident Report System

PubMed Central

Shorr, Ronald I.; Mion, Lorraine C.; Chandler, A. Michelle; Rosenblatt, Linda C.; Lynch, Debra; Kessler, Lori A.

2008-01-01

OBJECTIVES To determine the utility of a fall evaluation service to improve the ascertainment of falls in acute care. DESIGN Six-month observational study. SETTING Sixteen adult nursing units (349 beds) in an urban, academically affiliated, community hospital. PARTICIPANTS Patients admitted to the study units during the study period. INTERVENTION Nursing staff identifying falls were instructed to notify, using a pager, a trained nurse ‘‘fall evaluator.’’ Fall evaluators provided 24-hour-per-day 7-day-per-week coverage throughout the study. Data on patient falls gathered by fall evaluators were compared with falls data obtained through the hospital’s incident reporting system. RESULTS During 51,180 patient-days of observation, 191 falls were identified according to incident reports (3.73 falls/1,000 patient-days), whereas the evaluation service identified 228 falls (4.45 falls/1,000 patient-days). Combining falls reported from both data sources yielded 266 falls (5.20 falls/1,000 patient-days), a 39% relative rate increase compared with incident reports alone (P<.001). For falls with injury, combining data from both sources yielded 79 falls (1.54 injurious falls/1,000 patient-days), compared with 57 falls (1.11 injurious falls/1,000 patient-days) filed in incident reports—a 28% increase (P = .06). In the 16 nursing units, the relative percentage increase of captured fall events using the combined data sources versus the incident reporting system alone ranged from 13% to 125%. CONCLUSION Incident reports significantly underestimate both injurious and noninjurious falls in acute care settings and should not be used as the sole source of data for research or quality improvement initiatives. PMID:18205761

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

29 CFR 1915.159 - Personal fall arrest systems (PFAS).

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 7 2012-07-01 2012-07-01 false Personal fall arrest systems (PFAS). 1915.159 Section 1915... Protective Equipment (PPE) § 1915.159 Personal fall arrest systems (PFAS). The criteria of this section apply to PFAS and their use. Effective January 1, 1998, body belts and non-locking snaphooks are not...

29 CFR 1915.159 - Personal fall arrest systems (PFAS).

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 7 2014-07-01 2014-07-01 false Personal fall arrest systems (PFAS). 1915.159 Section 1915... Protective Equipment (PPE) § 1915.159 Personal fall arrest systems (PFAS). The criteria of this section apply to PFAS and their use. Effective January 1, 1998, body belts and non-locking snaphooks are not...

29 CFR 1915.159 - Personal fall arrest systems (PFAS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 7 2013-07-01 2013-07-01 false Personal fall arrest systems (PFAS). 1915.159 Section 1915... Protective Equipment (PPE) § 1915.159 Personal fall arrest systems (PFAS). The criteria of this section apply to PFAS and their use. Effective January 1, 1998, body belts and non-locking snaphooks are not...

29 CFR 1915.159 - Personal fall arrest systems (PFAS).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 7 2010-07-01 2010-07-01 false Personal fall arrest systems (PFAS). 1915.159 Section 1915... Protective Equipment (PPE) § 1915.159 Personal fall arrest systems (PFAS). The criteria of this section apply to PFAS and their use. Effective January 1, 1998, body belts and non-locking snaphooks are not...

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.

33 CFR 150.619 - What are the fall arrest system requirements?

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false What are the fall arrest system... SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: OPERATIONS Workplace Safety and Health Fall Arrest § 150.619 What are the fall arrest system requirements? (a) The deepwater port operator must ensure that...

33 CFR 150.619 - What are the fall arrest system requirements?

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false What are the fall arrest system... SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: OPERATIONS Workplace Safety and Health Fall Arrest § 150.619 What are the fall arrest system requirements? (a) The deepwater port operator must ensure that...

33 CFR 150.619 - What are the fall arrest system requirements?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false What are the fall arrest system... SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: OPERATIONS Workplace Safety and Health Fall Arrest § 150.619 What are the fall arrest system requirements? (a) The deepwater port operator must ensure that...

33 CFR 150.619 - What are the fall arrest system requirements?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false What are the fall arrest system... SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: OPERATIONS Workplace Safety and Health Fall Arrest § 150.619 What are the fall arrest system requirements? (a) The deepwater port operator must ensure that...

33 CFR 150.619 - What are the fall arrest system requirements?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false What are the fall arrest system... SECURITY (CONTINUED) DEEPWATER PORTS DEEPWATER PORTS: OPERATIONS Workplace Safety and Health Fall Arrest § 150.619 What are the fall arrest system requirements? (a) The deepwater port operator must ensure that...

Development and evaluation of an automated fall risk assessment system.

PubMed

Lee, Ju Young; Jin, Yinji; Piao, Jinshi; Lee, Sun-Mi

2016-04-01

Fall risk assessment is the first step toward prevention, and a risk assessment tool with high validity should be used. This study aimed to develop and validate an automated fall risk assessment system (Auto-FallRAS) to assess fall risks based on electronic medical records (EMRs) without additional data collected or entered by nurses. This study was conducted in a 1335-bed university hospital in Seoul, South Korea. The Auto-FallRAS was developed using 4211 fall-related clinical data extracted from EMRs. Participants included fall patients and non-fall patients (868 and 3472 for the development study; 752 and 3008 for the validation study; and 58 and 232 for validation after clinical application, respectively). The system was evaluated for predictive validity and concurrent validity. The final 10 predictors were included in the logistic regression model for the risk-scoring algorithm. The results of the Auto-FallRAS were shown as high/moderate/low risk on the EMR screen. The predictive validity analyzed after clinical application of the Auto-FallRAS was as follows: sensitivity = 0.95, NPV = 0.97 and Youden index = 0.44. The validity of the Morse Fall Scale assessed by nurses was as follows: sensitivity = 0.68, NPV = 0.88 and Youden index = 0.28. This study found that the Auto-FallRAS results were better than were the nurses' predictions. The advantage of the Auto-FallRAS is that it automatically analyzes information and shows patients' fall risk assessment results without requiring additional time from nurses. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

History of falls, gait, balance, and fall risks in older cancer survivors living in the community.

PubMed

Huang, Min H; Shilling, Tracy; Miller, Kara A; Smith, Kristin; LaVictoire, Kayle

2015-01-01

Older cancer survivors may be predisposed to falls because cancer-related sequelae affect virtually all body systems. The use of a history of falls, gait speed, and balance tests to assess fall risks remains to be investigated in this population. This study examined the relationship of previous falls, gait, and balance with falls in community-dwelling older cancer survivors. At the baseline, demographics, health information, and the history of falls in the past year were obtained through interviewing. Participants performed tests including gait speed, Balance Evaluation Systems Test, and short-version of Activities-specific Balance Confidence scale. Falls were tracked by mailing of monthly reports for 6 months. A "faller" was a person with ≥1 fall during follow-up. Univariate analyses, including independent sample t-tests and Fisher's exact tests, compared baseline demographics, gait speed, and balance between fallers and non-fallers. For univariate analyses, Bonferroni correction was applied for multiple comparisons. Baseline variables with P<0.15 were included in a forward logistic regression model to identify factors predictive of falls with age as covariate. Sensitivity and specificity of each predictor of falls in the model were calculated. Significance level for the regression analysis was P<0.05. During follow-up, 59% of participants had one or more falls. Baseline demographics, health information, history of falls, gaits speed, and balance tests did not differ significantly between fallers and non-fallers. Forward logistic regression revealed that a history of falls was a significant predictor of falls in the final model (odds ratio =6.81; 95% confidence interval =1.594-29.074) (P<0.05). Sensitivity and specificity for correctly identifying a faller using the positive history of falls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls may

Design a Learning-Oriented Fall Event Reporting System Based on Kirkpatrick Model.

PubMed

Zhou, Sicheng; Kang, Hong; Gong, Yang

2017-01-01

Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. We summarized these limitations through literature review, and developed an improved web-based fall event reporting system. The Kirkpatrick model, widely used in the business area for training program evaluation, has been integrated during the design of our system. Different from traditional event reporting systems that only collect and store the reports, our system automatically annotates and analyzes the reported events, and provides users with timely knowledge support specific to the reported event. The paper illustrates the design of our system and how its features are intended to reduce patient falls by learning from previous errors.

Fall Protection Introduction, #33462

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

Chochoms, Michael

The proper use of fall prevention and fall protection controls can reduce the risk of deaths and injuries caused by falls. This course, Fall Protection Introduction (#33462), is designed as an introduction to various types of recognized fall prevention and fall protection systems at Los Alamos National Laboratory (LANL), including guardrail systems, safety net systems, fall restraint systems, and fall arrest systems. Special emphasis is given to the components, inspection, care, and storage of personal fall arrest systems (PFASs). This course also presents controls for falling object hazards and emergency planning considerations for persons who have fallen.

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

PubMed

Silva de Lima, Ana Lígia; Evers, Luc J W; Hahn, Tim; Bataille, Lauren; Hamilton, Jamie L; Little, Max A; Okuma, Yasuyuki; Bloem, Bastiaan R; Faber, Marjan J

2017-08-01

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Effectiveness of an automatic manual wheelchair braking system in the prevention of falls.

PubMed

Martorello, Laura; Swanson, Edward

2006-01-01

The purpose of this study was to evaluate the effectiveness of an automatic manual wheelchair braking system in the reduction of falls for patients at high risk of falls while transferring to and from a manual wheelchair. The study design was a normative survey carried out through the use of a written questionnaire sent to 60 skilled nursing facilities to collect data from the medical charts, which identified patients at high risk for falls who used an automatic wheelchair braking system. The facilities participating in the study identified a frequency of falls of high-risk patients while transferring to and from the wheelchair ranging from 2 to 10 per year, with a median fall rate per facility of 4 falls. One year after the installation of the automatic wheelchair braking system, participating facilities demonstrated a reduction of zero to three falls during transfers by high-risk patients, with a median fall rate of zero falls. This represents a statistically significant reduction of 78% in the fall rate of high-risk patients while transferring to and from the wheelchair, t (18) = 6.39, p < .0001. Incident reports of falls to and from manual wheelchairs were reviewed retrospectively for a 1-year period. This study suggests that high-risk fallers transferring to or from manual wheelchairs sustained significantly fewer falls when the Steddy Mate automatic braking system for manual wheelchairs was installed. The application of the automatic braking system allows clients, families/caregivers, and facility personnel an increased safety factor for the reduction of falls from the wheelchair.

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

Joint Transform Correlation for face tracking: elderly fall detection application

NASA Astrophysics Data System (ADS)

Katz, Philippe; Aron, Michael; Alfalou, Ayman

2013-03-01

In this paper, an iterative tracking algorithm based on a non-linear JTC (Joint Transform Correlator) architecture and enhanced by a digital image processing method is proposed and validated. This algorithm is based on the computation of a correlation plane where the reference image is updated at each frame. For that purpose, we use the JTC technique in real time to track a patient (target image) in a room fitted with a video camera. The correlation plane is used to localize the target image in the current video frame (frame i). Then, the reference image to be exploited in the next frame (frame i+1) is updated according to the previous one (frame i). In an effort to validate our algorithm, our work is divided into two parts: (i) a large study based on different sequences with several situations and different JTC parameters is achieved in order to quantify their effects on the tracking performances (decimation, non-linearity coefficient, size of the correlation plane, size of the region of interest...). (ii) the tracking algorithm is integrated into an application of elderly fall detection. The first reference image is a face detected by means of Haar descriptors, and then localized into the new video image thanks to our tracking method. In order to avoid a bad update of the reference frame, a method based on a comparison of image intensity histograms is proposed and integrated in our algorithm. This step ensures a robust tracking of the reference frame. This article focuses on face tracking step optimisation and evalutation. A supplementary step of fall detection, based on vertical acceleration and position, will be added and studied in further work.

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial.

PubMed

Gschwind, Yves J; Eichberg, Sabine; Marston, Hannah R; Ejupi, Andreas; Rosario, Helios de; Kroll, Michael; Drobics, Mario; Annegarn, Janneke; Wieching, Rainer; Lord, Stephen R; Aal, Konstantin; Delbaere, Kim

2014-08-20

Falls are very common, especially in adults aged 65 years and older. Within the current international European Commission's Seventh Framework Program (FP7) project 'iStoppFalls' an Information and Communication Technology (ICT) based system has been developed to regularly assess a person's risk of falling in their own home and to deliver an individual and tailored home-based exercise and education program for fall prevention. The primary aims of iStoppFalls are to assess the feasibility and acceptability of the intervention program, and its effectiveness to improve balance, muscle strength and quality of life in older people. This international, multicenter study is designed as a single-blinded, two-group randomized controlled trial. A total of 160 community-dwelling older people aged 65 years and older will be recruited in Germany (n = 60), Spain (n = 40), and Australia (n = 60) between November 2013 and May 2014. Participants in the intervention group will conduct a 16-week exercise program using the iStoppFalls system through their television set at home. Participants are encouraged to exercise for a total duration of 180 minutes per week. The training program consists of a variety of balance and strength exercises in the form of video games using exergame technology. Educational material about a healthy lifestyle will be provided to each participant. Final reassessments will be conducted after 16 weeks. The assessments include physical and cognitive tests as well as questionnaires assessing health, fear of falling, quality of life and psychosocial determinants. Falls will be followed up for six months by monthly falls calendars. We hypothesize that the regular use of this newly developed ICT-based system for fall prevention at home is feasible for older people. By using the iStoppFalls sensor-based exercise program, older people are expected to improve in balance and strength outcomes. In addition, the exercise training may have a positive impact on quality of

History of falls, gait, balance, and fall risks in older cancer survivors living in the community

PubMed Central

Huang, Min H; Shilling, Tracy; Miller, Kara A; Smith, Kristin; LaVictoire, Kayle

2015-01-01

Older cancer survivors may be predisposed to falls because cancer-related sequelae affect virtually all body systems. The use of a history of falls, gait speed, and balance tests to assess fall risks remains to be investigated in this population. This study examined the relationship of previous falls, gait, and balance with falls in community-dwelling older cancer survivors. At the baseline, demographics, health information, and the history of falls in the past year were obtained through interviewing. Participants performed tests including gait speed, Balance Evaluation Systems Test, and short-version of Activities-specific Balance Confidence scale. Falls were tracked by mailing of monthly reports for 6 months. A “faller” was a person with ≥1 fall during follow-up. Univariate analyses, including independent sample t-tests and Fisher’s exact tests, compared baseline demographics, gait speed, and balance between fallers and non-fallers. For univariate analyses, Bonferroni correction was applied for multiple comparisons. Baseline variables with P<0.15 were included in a forward logistic regression model to identify factors predictive of falls with age as covariate. Sensitivity and specificity of each predictor of falls in the model were calculated. Significance level for the regression analysis was P<0.05. During follow-up, 59% of participants had one or more falls. Baseline demographics, health information, history of falls, gaits speed, and balance tests did not differ significantly between fallers and non-fallers. Forward logistic regression revealed that a history of falls was a significant predictor of falls in the final model (odds ratio =6.81; 95% confidence interval =1.594–29.074) (P<0.05). Sensitivity and specificity for correctly identifying a faller using the positive history of falls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls

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.

Wearable Fall Detector using Integrated Sensors and Energy Devices

PubMed Central

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

2015-01-01

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

Wearable Fall Detector using Integrated Sensors and Energy Devices

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Wearable Fall Detector using Integrated Sensors and Energy Devices.

PubMed

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

2015-11-24

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

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study.

PubMed

Di Rosa, Mirko; Hausdorff, Jeff M; Stara, Vera; Rossi, Lorena; Glynn, Liam; Casey, Monica; Burkard, Stefan; Cherubini, Antonio

2017-06-01

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting. Copyright © 2017 Elsevier B.V. All rights reserved.

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial

PubMed Central

2014-01-01

Background Falls are very common, especially in adults aged 65 years and older. Within the current international European Commission’s Seventh Framework Program (FP7) project ‘iStoppFalls’ an Information and Communication Technology (ICT) based system has been developed to regularly assess a person’s risk of falling in their own home and to deliver an individual and tailored home-based exercise and education program for fall prevention. The primary aims of iStoppFalls are to assess the feasibility and acceptability of the intervention program, and its effectiveness to improve balance, muscle strength and quality of life in older people. Methods/Design This international, multicenter study is designed as a single-blinded, two-group randomized controlled trial. A total of 160 community-dwelling older people aged 65 years and older will be recruited in Germany (n = 60), Spain (n = 40), and Australia (n = 60) between November 2013 and May 2014. Participants in the intervention group will conduct a 16-week exercise program using the iStoppFalls system through their television set at home. Participants are encouraged to exercise for a total duration of 180 minutes per week. The training program consists of a variety of balance and strength exercises in the form of video games using exergame technology. Educational material about a healthy lifestyle will be provided to each participant. Final reassessments will be conducted after 16 weeks. The assessments include physical and cognitive tests as well as questionnaires assessing health, fear of falling, quality of life and psychosocial determinants. Falls will be followed up for six months by monthly falls calendars. Discussion We hypothesize that the regular use of this newly developed ICT-based system for fall prevention at home is feasible for older people. By using the iStoppFalls sensor-based exercise program, older people are expected to improve in balance and strength outcomes. In addition, the exercise

Shuttlecock detection system for fully-autonomous badminton robot with two high-speed video cameras

NASA Astrophysics Data System (ADS)

Masunari, T.; Yamagami, K.; Mizuno, M.; Une, S.; Uotani, M.; Kanematsu, T.; Demachi, K.; Sano, S.; Nakamura, Y.; Suzuki, S.

2017-02-01

Two high-speed video cameras are successfully used to detect the motion of a flying shuttlecock of badminton. The shuttlecock detection system is applied to badminton robots that play badminton fully autonomously. The detection system measures the three dimensional position and velocity of a flying shuttlecock, and predicts the position where the shuttlecock falls to the ground. The badminton robot moves quickly to the position where the shuttle-cock falls to, and hits the shuttlecock back into the opponent's side of the court. In the game of badminton, there is a large audience, and some of them move behind a flying shuttlecock, which are a kind of background noise and makes it difficult to detect the motion of the shuttlecock. The present study demonstrates that such noises can be eliminated by the method of stereo imaging with two high-speed cameras.

Numerical Estimation of Balanced and Falling States for Constrained Legged Systems

NASA Astrophysics Data System (ADS)

Mummolo, Carlotta; Mangialardi, Luigi; Kim, Joo H.

2017-08-01

Instability and risk of fall during standing and walking are common challenges for biped robots. While existing criteria from state-space dynamical systems approach or ground reference points are useful in some applications, complete system models and constraints have not been taken into account for prediction and indication of fall for general legged robots. In this study, a general numerical framework that estimates the balanced and falling states of legged systems is introduced. The overall approach is based on the integration of joint-space and Cartesian-space dynamics of a legged system model. The full-body constrained joint-space dynamics includes the contact forces and moments term due to current foot (or feet) support and another term due to altered contact configuration. According to the refined notions of balanced, falling, and fallen, the system parameters, physical constraints, and initial/final/boundary conditions for balancing are incorporated into constrained nonlinear optimization problems to solve for the velocity extrema (representing the maximum perturbation allowed to maintain balance without changing contacts) in the Cartesian space at each center-of-mass (COM) position within its workspace. The iterative algorithm constructs the stability boundary as a COM state-space partition between balanced and falling states. Inclusion in the resulting six-dimensional manifold is a necessary condition for a state of the given system to be balanced under the given contact configuration, while exclusion is a sufficient condition for falling. The framework is used to analyze the balance stability of example systems with various degrees of complexities. The manifold for a 1-degree-of-freedom (DOF) legged system is consistent with the experimental and simulation results in the existing studies for specific controller designs. The results for a 2-DOF system demonstrate the dependency of the COM state-space partition upon joint-space configuration (elbow-up vs

[Establishment and application effect of fall prevention and control information system in elderly community].

PubMed

Zhao, Ting; Pi, Hong-Ying; Ku, Hong-An; Pan, Li; Gong, Zhu-Yun

2018-02-08

To investigate establishing, applying and evaluating the fall prevention and control information system in elderly community. Relying on internet technology and informatization means, the fall comprehensive prevention and control strategy of elderly was guided into online from offline. The fall prevention and control information system which was a collection of risk assessment, remote education and feedback was established. One hundred and twenty-six elderly (over 60 years old) in community were screened in this study and 84 high-risk elders who were involved in the remote continuous comprehensive intervention were screened out. Intervening measures included distributing propaganda album, making mission slides and video used to play with the interpretation remotely. Then fall related situation before and after intervention was analyzed and the effectiveness of system evaluated. After remote intervention, the fall incidence of high-risk group decreased from 21.43% to 4.76%( P <0.01). The body balance and gait stability improved clearly( P <0. 01). The rate of taking proper prevention and control behavior significantly improved( P <0.01). They believed in themselves not to fall down with more confidence when taking complex behaviors( P <0.01). The security of environment at home significantly enhanced( P <0. 01). Fall prevention and control information system in elderly community was innovative and convenient. The system could roundly assess the status related to fall and accurately screen out high-risk group. The system could implement the remote continuous comprehensive intervention so that the incident of fall was decrease. In conclusion, the system is stable and effective, can be further popularization and application as a successful pilot.

Detection of compensatory balance responses using wearable electromyography sensors for fall-risk assessment.

PubMed

Nouredanesh, Mina; Kukreja, Sunil L; Tung, James

2016-08-01

Loss of balance is prevalent in older adults and populations with gait and balance impairments. The present paper aims to develop a method to automatically distinguish compensatory balance responses (CBRs) from normal gait, based on activity patterns of muscles involved in maintaining balance. In this study, subjects were perturbed by lateral pushes while walking and surface electromyography (sEMG) signals were recorded from four muscles in their right leg. To extract sEMG time domain features, several filtering characteristics and segmentation approaches are examined. The performance of three classification methods, i.e., k-nearest neighbor, support vector machines, and random forests, were investigated for accurate detection of CBRs. Our results show that features extracted in the 50-200Hz band, segmented using peak sEMG amplitudes, and a random forest classifier detected CBRs with an accuracy of 92.35%. Moreover, our results support the important role of biceps femoris and rectus femoris muscles in stabilization and consequently discerning CBRs. This study contributes towards the development of wearable sensor systems to accurately and reliably monitor gait and balance control behavior in at-home settings (unsupervised conditions), over long periods of time, towards personalized fall risk assessment tools.

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 incidents unraveled: a series of 26 video-based real-life fall events in three frail older persons

PubMed Central

2013-01-01

Background For prevention and detection of falls, it is essential to unravel the way in which older people fall. This study aims to provide a description of video-based real-life fall events and to examine real-life falls using the classification system by Noury and colleagues, which divides a fall into four phases (the prefall, critical, postfall and recovery phase). Methods Observational study of three older persons at high risk for falls, residing in assisted living or residential care facilities: a camera system was installed in each participant’s room covering all areas, using a centralized PC platform in combination with standard Internet Protocol (IP) cameras. After a fall, two independent researchers analyzed recorded images using the camera position with the clearest viewpoint. Results A total of 30 falls occurred of which 26 were recorded on camera over 17 months. Most falls happened in the morning or evening (62%), when no other persons were present (88%). Participants mainly fell backward (initial fall direction and landing configuration) on the pelvis or torso and none could get up unaided. In cases where a call alarm was used (54%), an average of 70 seconds (SD=64; range 15–224) was needed to call for help. Staff responded to the call after an average of eight minutes (SD=8.4; range 2–33). Mean time on the ground was 28 minutes (SD=25.4; range 2–59) without using a call alarm compared to 11 minutes (SD=9.2; range 3–38) when using a call alarm (p=0.445). The real life falls were comparable with the prefall and recovery phase of Noury’s classification system. The critical phase, however, showed a prolonged duration in all falls. We suggest distinguishing two separate phases: a prolonged loss of balance phase and the actual descending phase after failure to recover balance, resulting in the impact of the body on the ground. In contrast to the theoretical description, the postfall phase was not typically characterized by inactivity; this

Body mass index, falls, and injurious falls among U.S. adults: Findings from the 2014 Behavioral Risk Factor Surveillance System.

PubMed

Ylitalo, Kelly R; Karvonen-Gutierrez, Carrie A

2016-10-01

Falls are an important health concern because they are associated with loss of independence and disability, particularly among women. We determined the age- and sex-specific prevalence of injurious falls among adults in the United States and examined the impact of obesity on fall risk. Self-reported falls, injurious falls, and health histories were obtained from 280,035 adults aged 45-79years in the 2014 Behavioral Risk Factor Surveillance System. Body mass index was categorized as underweight (<18.5kg/m 2 ), normal weight (18.5-24.9kg/m 2 ), overweight 25-29.9kg/m 2 ), class I obesity (30.0-34.9kg/m 2 ), or class II/III obesity (≥35.0kg/m 2 ) based on self-reported height and weight. Data were analyzed using weighted age- and sex-specific prevalence rates and Poisson regression. Overall, 11.0% reported ≥1 injurious fall in the previous 12months. Mid-life women 55-59years reported the highest prevalence of injurious falls (15.4%). Among mid-life women, overweight was associated with injurious falls (RR=1.17; 95% CI: 1.08, 1.28), but overweight was not associated with falling among other age-sex groups. Class II/III obesity was associated with injurious falls among all age-sex groups. After considering the mediators like health conditions (depression, cardiovascular disease, diabetes, arthritis) and behaviors (physical activity, sleep), the association of class II/III obesity and injurious fall risk persisted only among mid-life women (RR=1.23; 95% CI: 1.12, 1.36). Not only are mid-life women at high risk for falls, but the class II/III obesity is a risk factor for injurious falls. Targeting mid-life women for fall and injury prevention is an important aim for practitioners, particularly given unique correlates of falling for this group. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

On Robustness of Deadlock Detection Algorithms for Distributed Computing Systems.

DTIC Science & Technology

1982-02-01

temrs : nake it much,- ore Eff’: -ult -,o detect, avcii :r -revenn -hsr fn -,he earlier muJtiroaming centralized computing systems. :eadlock :)rever...failure of site C would not have been critical after the B ^ad ’ teen sent. The effect of a type c site (site _ in our examrle’ falling would have no

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.

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

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.

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.

Segmental Dynamics of Forward Fall Arrests: System Identification Approach

PubMed Central

Kim, Kyu-Jung; Ashton-Miller, James A.

2009-01-01

Background Fall-related injuries are multifaceted problems, necessitating thorough biodynamic simulation to identify critical biomechanical factors. Methods A 2-degree-of-freedom discrete impact model was constructed through system identification and validation processes using the experimental data to understand dynamic interactions of various biomechanical parameters in bimanual forward fall arrests. Findings The bimodal reaction force response from the identified models had small identification errors for the first and second force peaks less than 3.5% and high coherence between the measured and identified model responses (R2=0.95). Model validation with separate experimental data also demonstrated excellent validation accuracy and coherence, less than 7% errors and R2=0.87, respectively. The first force peak was usually greater than the second force peak and strongly correlated with the impact velocity of the upper extremity, while the second force peak was associated with the impact velocity of the body. The impact velocity of the upper extremity relative to the body could be a major risk factor to fall-related injuries as observed from model simulations that a 75% faster arm movement relative to the falling speed of the body alone could double the first force peak from soft landing, thereby readily exceeding the fracture strength of the distal radius. Interpretation Considering that the time-critical nature of falling often calls for a fast arm movement, the use of the upper extremity in forward fall arrests is not biomechanically justified unless sufficient reaction time and coordinated protective motion of the upper extremity are available. PMID:19250726

ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial.

PubMed

Gschwind, Yves J; Eichberg, Sabine; Ejupi, Andreas; de Rosario, Helios; Kroll, Michael; Marston, Hannah R; Drobics, Mario; Annegarn, Janneke; Wieching, Rainer; Lord, Stephen R; Aal, Konstantin; Vaziri, Daryoush; Woodbury, Ashley; Fink, Dennis; Delbaere, Kim

2015-01-01

Falls and fall-related injuries are a serious public health issue. Exercise programs can effectively reduce fall risk in older people. The iStoppFalls project developed an Information and Communication Technology-based system to deliver an unsupervised exercise program in older people's homes. The primary aims of the iStoppFalls randomized controlled trial were to assess the feasibility (exercise adherence, acceptability and safety) of the intervention program and its effectiveness on common fall risk factors. A total of 153 community-dwelling people aged 65+ years took part in this international, multicentre, randomized controlled trial. Intervention group participants conducted the exercise program for 16 weeks, with a recommended duration of 120 min/week for balance exergames and 60 min/week for strength exercises. All intervention and control participants received educational material including advice on a healthy lifestyle and fall prevention. Assessments included physical and cognitive tests, and questionnaires for health, fear of falling, number of falls, quality of life and psychosocial outcomes. The median total exercise duration was 11.7 h (IQR = 22.0) over the 16-week intervention period. There were no adverse events. Physiological fall risk (Physiological Profile Assessment, PPA) reduced significantly more in the intervention group compared to the control group (F1,127 = 4.54, p = 0.035). There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min/week; n = 18, 25.4 %), low-adherence (<90 min/week; n = 53, 74.6 %) and control group (F2,125 = 3.12, n = 75, p = 0.044). Post hoc analysis revealed a significantly larger effect in favour of the high-adherence group compared to the control group for fall risk (p = 0.031), postural sway (p = 0.046), stepping reaction time (p = 0.041), executive functioning (p = 0.044), and quality of life (p for

Mitigating fall risk: A community fall reduction program.

PubMed

Reinoso, Humberto; McCaffrey, Ruth G; Taylor, David W M

One fourth of all American's over 65 years of age fall each year. Falls are a common and often devastating event that can pose a serious health risk for older adults. Healthcare providers are often unable to spend the time required to assist older adults with fall risk issues. Without a team approach to fall prevention the system remains focused on fragmented levels of health promotion and risk prevention. The specific aim of this project was to engage older adults from the community in a fall risk assessment program, using the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program, and provide feedback on individual participants' risks that participants could share with their primary care physician. Older adults who attended the risk screening were taking medications that are known to increase falls. They mentioned that their health care providers do not screen for falls and appreciated a community based screening. Copyright © 2017 Elsevier Inc. All rights reserved.

75 FR 22844 - Construction Fall Protection Systems Criteria and Practices and Training Requirements; Extension...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-30

...] Construction Fall Protection Systems Criteria and Practices and Training Requirements; Extension of the Office of Management and Budget's (OMB) Approval of Information Collection (Paperwork) Requirements AGENCY... requirements contained in the construction standards on Fall Protection Systems Criteria and Practices (29 CFR...

78 FR 38311 - Broad River Electric Cooperative and Cherokee Falls Associates; Aquenergy Systems, Inc.; Notice...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

... Electric Cooperative and Cherokee Falls Associates; Aquenergy Systems, Inc.; Notice of Application for... Electric Cooperative and Cherokee Falls Associates (transferors) and Aquenergy Systems, Inc. (transferee... 15222. Transferee: Mr. Steve Champagne, Executive Vice President and General Counsel, Aquenergy Systems...

Selective attentional processing to fall-relevant stimuli among older adults who fear falling.

PubMed

Brown, Lesley A; White, Patti; Doan, Jonathan B; de Bruin, Natalie

2011-05-01

Fear of falling is known to affect more than half of community-dwelling older adults over 60 years of age. This fear is associated with physical and psychological effects that increase the risk of falling. The authors' theory is that attentional processing biases may exist in this population that serve to perpetuate fear of falling and subsequently increase fall risk. As a starting point in testing this proposition, the authors examined selective attentional processing bias to fall-relevant stimuli among older adults. Thirty older adult participants (M(age) = 70.8 ± 5.8), self-categorized to be Fearful of Falling (FF, n = 15) or Non-Fearful of Falling (NF, n = 15) completed a visual dot-probe paradigm to determine detection latencies to fall-threatening and general-threat stimuli. Attentional processing was defined using three index scores: attentional bias, congruency index, and incongruency index. Bias indicates capture of attention, whereas congruency and incongruency imply vigilance and disengagement difficulty, respectively. Both groups showed an attentional bias to fall-threat words but those who were fearful of falling also showed an incongruency effect for fall-threat words. These findings confirm that selective attentional processing profiles for fall-relevant stimuli differ between older adults who exhibit fear of falling and those who do not have this fear. Moreover, in accordance with current interpretations of selective attentional processing, the incongruency effect noted among fall-fearful older adults presents a possibility for a difficulty disengaging from fall-threatening stimuli.

Detecting Human Motion: Introducing Step, Fall and ADL Algorithms

NASA Astrophysics Data System (ADS)

Vermeiren, Dries; Weyn, Maarten; de Ron, Geert

Telecare is the term given to offering remote care to elderly and vulnerable people, providing them with the care and reassurance needed to allow them to keep living at home. As telecare is gaining research interests, we'll introduce a system which can be used to monitor the steps, falls and daily activities of high risk populations in this paper. Using this system it is possible for a patient to rehabilitate at home or for elderly to keep living independently in their own house while they are still monitored. This leads to a huge cost reduction in health services and moreover it will make patients satisfied for being able to live at home as long as possible and in all comfort.

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.

An online detection system for aggregate sizes and shapes based on digital image processing

NASA Astrophysics Data System (ADS)

Yang, Jianhong; Chen, Sijia

2017-02-01

Traditional aggregate size measuring methods are time-consuming, taxing, and do not deliver online measurements. A new online detection system for determining aggregate size and shape based on a digital camera with a charge-coupled device, and subsequent digital image processing, have been developed to overcome these problems. The system captures images of aggregates while falling and flat lying. Using these data, the particle size and shape distribution can be obtained in real time. Here, we calibrate this method using standard globules. Our experiments show that the maximum particle size distribution error was only 3 wt%, while the maximum particle shape distribution error was only 2 wt% for data derived from falling aggregates, having good dispersion. In contrast, the data for flat-lying aggregates had a maximum particle size distribution error of 12 wt%, and a maximum particle shape distribution error of 10 wt%; their accuracy was clearly lower than for falling aggregates. However, they performed well for single-graded aggregates, and did not require a dispersion device. Our system is low-cost and easy to install. It can successfully achieve online detection of aggregate size and shape with good reliability, and it has great potential for aggregate quality assurance.

[Evaluation of postural control systems in elderly patients with repeated falls].

PubMed

González Ramírez, Alfonso; Lázaro del Nogal, Montserrat; Ribera Casado, José Manuel

2008-01-01

a) to describe postural control disorders in elderly patients with recurrent falls; b) to analyze the influence of sensory deficits on centre of gravity control mechanisms; and c) to assess the functional consequences of balance disorders and falls in this group of patients. patients aged more than 65 years old referred to a falls unit with two or more falls in the previous 6 months were included in this study. The protocol included posturographic studies with a Neurocom Balance Master. To evaluate motor control, Rhythmic Weight Shift (RWS test) was performed. To assess sensorial control, Modified Clinical Test of Sensory Interaction on Balance (MCT test) was used. Other tests performed were the Sit to Stand (SS test), Walk across (WA test) and Step up over (SO test). a total of 109 patients (85.3% women) were studied. Mean age was 78.01 years (SD: 5.38). Disorders in one or more afferent sensorial systems were found in 51.7% of the patients (27.5% visual deficiencies, 17.6% vestibular alterations, and 6.6% somatosensorial deficits). Two afferent systems were compromised in 25.3%, and all three were compromised in 11.1% of the patients. No significant differences were found in directional control (RWS) when compared with the number of altered systems. posturographic studies provide sensitive information on static and dynamic centre of gravity control systems, eventual sensory deficits, and patients' ability to carry out basic activities of daily living. In our sample, the most frequent deficit was visual impairment. This information is essential to establish a correct management programme.

Modeling Parkinson's disease falls associated with brainstem cholinergic systems decline.

PubMed

Kucinski, Aaron; Sarter, Martin

2015-04-01

In addition to the primary disease-defining symptoms, approximately half of patients with Parkinson's disease (PD) suffer from postural instability, impairments in gait control and a propensity for falls. Consistent with evidence from patients, we previously demonstrated that combined striatal dopamine (DA) and basal forebrain (BF) cholinergic cell loss causes falls in rats traversing dynamic surfaces. Because evidence suggests that degeneration of brainstem cholinergic neurons arising from the pedunculopontine nucleus (PPN) also contributes to impaired gait and falls, here we assessed the effects of selective cholinergic PPN lesions in combination with striatal DA loss or BF cholinergic cells loss as well as losses in all 3 regions. Results indicate that all combination losses that included the BF cholinergic system slowed traversal and increased slips and falls. However, the performance of rats with losses in all 3 regions (PPN, BF, and DA) was not more severely impaired than following combined BF cholinergic and striatal DA lesions. These results confirm the hypothesis that BF cholinergic-striatal disruption of attentional-motor interactions is a primary source of falls. Additional losses of PPN cholinergic neurons may worsen posture and gait control in situations not captured by the current testing conditions. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

[Neurological diseases detected in the Lille Multidisciplinary Falls Consultation].

PubMed

Guillochon, A; Crinquette, C; Gaxatte, C; Pardessus, V; Bombois, S; Deramecourt, V; Boulanger, E; Puisieux, F

2010-02-01

People with neurological disorders including stroke, dementia, Parkinson's disease, and polyneuropathy are known to have an increased risk of falls. To evaluate the prevalence and nature of neurological risk factors among the patients attending the Multidisciplinary Falls Consultation of the University Hospital of Lille (France), and to analyze the characteristic features of patients termed "neurological fallers" with neurological risk factors. The study included 266 consecutive patients who were initially assessed by a geriatrician, a neurologist and a physiatrist, and again, six months later, by the same geriatrician. Two out of three patients had neurological signs that can be regarded as neurological risk factors of falling. These neurological signs had not been diagnosed before the consultation in 85% of cases. The most common conditions were deficit of lower extremity proprioception (59% of patients) and cognitive impairment (43%). The most frequently evoked neurological diseases were dementia (40% of patients), polyneuropathy (17%) and stroke (8%). Compared with other patients, "neurological fallers" were more frequently living in a nursing home, had lower ADL and MMSE scores at baseline, had experienced more falls in the six preceding months, had a lower probability of having a timed Up-and-Go test less than 20 seconds and a single limb stance equal to 5 seconds. In the follow-up, "neurological fallers" reported hospitalizations more often. The findings show that a large proportion of old persons presenting at the Multidisciplinary Falls Consultation have unrecognized neurological disorders. Comprehensive neurological examination including an evaluation of cognition is required in every elderly faller. Copyright 2009 Elsevier Masson SAS. All rights reserved.

State Fall Prevention Coalitions as Systems Change Agents: An Emphasis on Policy.

PubMed

Schneider, Ellen C; Smith, Matthew Lee; Ory, Marcia G; Altpeter, Mary; Beattie, Bonita Lynn; Scheirer, Mary Ann; Shubert, Tiffany E

2016-03-01

Falls among older adults are an escalating public health issue, which requires a multidisciplinary and multilevel approach to affect systems change to effectively address this problem. The National Council on Aging established the Falls Free® Initiative, enfolding and facilitating statewide Fall Prevention Coalitions. Fall Free® activities included developing the State Policy Toolkit for Advancing Falls Prevention to promote sustainable change by supporting the dissemination and adoption of evidence-based strategies. To (1) determine if the policies being implemented were recommended and supported by the Toolkit, (2) identify the perceived barriers and facilitators to implementing policies, and (3) identify Coalitions' current and future fall prevention policy activities. A 63-item online survey was distributed to State Coalition Leads. Descriptive statistics (frequencies and counts) were used to describe Coalition characteristics and activities. Coalitions had several similarities, and varied greatly in their number of member organizations and members as well as meeting frequencies. Key activities included building partnerships, disseminating programs, and pursuing at least one of the eight National Council on Aging-recommended policy goals. The most commonly reported facilitator was active support from the Coalition Leads, whereas the lack of funding was the most cited barrier. This study serves as the first national census of empirical evidence regarding Falls Coalitions' composition, goals, and activities. Results indicate that Coalitions are actively pursuing evidence-based policies but could benefit from additional technical assistance and resources. Findings support the value of Toolkit recommendations by documenting what is feasible and being implemented. Knowledge about facilitators and barriers will inform future efforts to foster sustainable systems change in states with active Coalitions and encourage Coalitions in other states. © 2015 Society for

Impact of Diabetic Complications on Balance and Falls: Contribution of the Vestibular System

PubMed Central

Lin, James; Staecker, Hinrich; Whitney, Susan L.; Kluding, Patricia M.

2016-01-01

Diabetes causes many complications, including retinopathy and peripheral neuropathy, which are well understood as contributing to gait instability and falls. A less understood complication of diabetes is the effect on the vestibular system. The vestibular system contributes significantly to balance in static and dynamic conditions by providing spatially orienting information. It is noteworthy that diabetes has been reported to affect vestibular function in both animal and clinical studies. Pathophysiological changes in peripheral and central vestibular structures due to diabetes have been noted. Vestibular dysfunction is associated with impaired balance and a higher risk of falls. As the prevalence of diabetes increases, so does the potential for falls due to diabetic complications. The purpose of this perspective article is to present evidence on the pathophysiology of diabetes-related complications and their influence on balance and falls, with specific attention to emerging evidence of vestibular dysfunction due to diabetes. Understanding this relationship may be useful for screening (by physical therapists) for possible vestibular dysfunction in people with diabetes and for further developing and testing the efficacy of interventions to reduce falls in this population. PMID:26251477

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.

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%.

Systems and Methods for Imaging of Falling Objects

NASA Technical Reports Server (NTRS)

Fallgatter, Cale (Inventor); Garrett, Tim (Inventor)

2014-01-01

Imaging of falling objects is described. Multiple images of a falling object can be captured substantially simultaneously using multiple cameras located at multiple angles around the falling object. An epipolar geometry of the captured images can be determined. The images can be rectified to parallelize epipolar lines of the epipolar geometry. Correspondence points between the images can be identified. At least a portion of the falling object can be digitally reconstructed using the identified correspondence points to create a digital reconstruction.

29 CFR Appendix B to Subpart I of... - General Testing Conditions and Additional Guidelines for Personal Fall Protection Systems (Non...

Code of Federal Regulations, 2014 CFR

2014-07-01

... Personal Fall Protection Systems (Non-Mandatory) B Appendix B to Subpart I of Part 1915 Labor Regulations... Guidelines for Personal Fall Protection Systems (Non-Mandatory) 1. Personal fall arrest systems—(a) General... create the free fall distance should be the one supplied with the system, or in its absence, the least...

29 CFR Appendix B to Subpart I of... - General Testing Conditions and Additional Guidelines for Personal Fall Protection Systems (Non...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Personal Fall Protection Systems (Non-Mandatory) B Appendix B to Subpart I of Part 1915 Labor Regulations... Guidelines for Personal Fall Protection Systems (Non-Mandatory) 1. Personal fall arrest systems—(a) General... create the free fall distance should be the one supplied with the system, or in its absence, the least...

29 CFR Appendix B to Subpart I of... - General Testing Conditions and Additional Guidelines for Personal Fall Protection Systems (Non...

Code of Federal Regulations, 2013 CFR

2013-07-01

... Personal Fall Protection Systems (Non-Mandatory) B Appendix B to Subpart I of Part 1915 Labor Regulations... Guidelines for Personal Fall Protection Systems (Non-Mandatory) 1. Personal fall arrest systems—(a) General... create the free fall distance should be the one supplied with the system, or in its absence, the least...

Transfer Patterns of Students, University of Hawaii System, Fall 1975.

ERIC Educational Resources Information Center

Hawaii Univ., Honolulu. Management Systems Office.

In fall 1975, 4,702 students transferred into the University of Hawaii (UH) System, representing a 15.5 percent increase over the number of transfers in 1974. Of the total, 56 percent transferred from within the UH System, 6 percent transferred from other Hawaii institutions, and 36 percent transferred from out-of-state institutions. The total…

EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off.

PubMed

Huikang Wang; Luzheng Bi; Teng Teng

2017-07-01

This paper proposes a novel method of electroencephalography (EEG)-based driver emergency braking intention detection system for brain-controlled driving considering one electrode falling-off. First, whether one electrode falls off is discriminated based on EEG potentials. Then, the missing signals are estimated by using the signals collected from other channels based on multivariate linear regression. Finally, a linear decoder is applied to classify driver intentions. Experimental results show that the falling-off discrimination accuracy is 99.63% on average and the correlation coefficient and root mean squared error (RMSE) between the estimated and experimental data are 0.90 and 11.43 μV, respectively, on average. Given one electrode falls off, the system accuracy of the proposed intention prediction method is significantly higher than that of the original method (95.12% VS 79.11%) and is close to that (95.95%) of the original system under normal situations (i. e., no electrode falling-off).

Impact sites representing potential bruising locations associated with rearward falls in children.

PubMed

Dsouza, Raymond; Bertocci, Gina

2016-04-01

Children presenting multiple unexplained bruises can be an early sign of physical abuse. Bruising locations on the body can be an effective indicator of abusive versus accidental trauma. Additionally, childhood falls are often used as falsely reported events in child abuse, however, characterization of potential bruising locations associated with these falls does not exist. In our study we used a 12-month old pediatric anthropomorphic test device (ATD) adapted with a custom developed force sensing skin to predict potential bruising locations during rearward falls from standing. The surrogate bruising detection system measured and displayed recorded force data on a computerized body image mapping system when sensors were activated. Simulated rearward fall experiments were performed onto two different impact surfaces (padded carpet and linoleum tile over concrete) with two different initial positions (standing upright and posteriorly inclined) so that the ATD would fall rearward upon release. Findings indicated impact locations, and thus the potential for bruising in the posterior plane primarily within the occipital head and posterior torso regions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

49 CFR 214.103 - Fall protection, generally.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Fall protection, generally. 214.103 Section 214... Fall protection, generally. (a) Except as provided in paragraphs (b) through (d) of this section, when... shall use a personal fall arrest system or safety net system. All fall protection systems required by...

49 CFR 214.103 - Fall protection, generally.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Fall protection, generally. 214.103 Section 214... Fall protection, generally. (a) Except as provided in paragraphs (b) through (d) of this section, when... shall use a personal fall arrest system or safety net system. All fall protection systems required by...

49 CFR 214.103 - Fall protection, generally.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Fall protection, generally. 214.103 Section 214... Fall protection, generally. (a) Except as provided in paragraphs (b) through (d) of this section, when... shall use a personal fall arrest system or safety net system. All fall protection systems required by...

49 CFR 214.103 - Fall protection, generally.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Fall protection, generally. 214.103 Section 214... Fall protection, generally. (a) Except as provided in paragraphs (b) through (d) of this section, when... shall use a personal fall arrest system or safety net system. All fall protection systems required by...

49 CFR 214.103 - Fall protection, generally.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Fall protection, generally. 214.103 Section 214... Fall protection, generally. (a) Except as provided in paragraphs (b) through (d) of this section, when... shall use a personal fall arrest system or safety net system. All fall protection systems required by...

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

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.

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

Parkinsonian signs are a risk factor for falls.

PubMed

Dahodwala, Nabila; Nwadiogbu, Chinwe; Fitts, Whitney; Partridge, Helen; Karlawish, Jason

2017-06-01

Parkinsonian signs are common, non-specific findings in older adults and associated with increased rates of dementia and mortality. It is important to understand which motor outcomes are associated with parkinsonian signs. To determine the role of parkinsonian signs on fall rates among older adults. We conducted a longitudinal study of primary care patients from the University of Pennsylvania Health System. Adults over 55 years were assessed at baseline through surveys and a neurological examination. We recorded falls over the following 2 years. Parkinsonian signs were defined as the presence of 2 of 4 cardinal signs. Incident falls were compared between subjects with and without parkinsonian signs, and modified Poisson regression used to adjust for potential confounders in the relationship between parkinsonian signs and falls. 982 subjects with a mean age of 68 (s.d. 8.8) years participated. 29% of participants fell and 12% exhibited parkinsonian signs at baseline. The unadjusted RR for falls among individuals with parkinsonian signs was 1.36 (95% CI 1.05-1.76, p=0.02). After adjusting for age, cognitive function, urinary incontinence, depression, diabetes, stroke and arthritis, individuals with parkinsonian signs were still 38% more likely to fall than those without parkinsonian signs (RR 1.38, 95% CI 1.04-1.82; p=0.03). Falls among those with parkinsonian signs were more likely to lead to injury (53% vs 37%; p=0.04). Parkinsonian signs are a significant, independent risk factor for falls. Early detection of this clinical state is important in order to implement fall prevention programs among primary care patients. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Masculinity and preventing falls: insights from the fall experiences of men aged 70 years and over.

PubMed

Liddle, J L M; Lovarini, Meryl; Clemson, Lindy M; Jang, Haeyoung; Lord, Stephen R; Sherrington, Catherine; Willis, Karen

2018-01-11

To explore men's fall experiences through the lens of masculine identities so as to assist health professionals better engage men in fall prevention programs. Twenty-five men, aged 70-93 years who had experienced a recent fall, participated in a qualitative semi-structured interview. Men's willingness to engage in fall prevention programs taking account of individual contexts and expressions of masculinity, were conceptualised using constant comparative methods. Men's willingness to engage in fall prevention programs was related to their perceptions of the preventability of falls; personal relevance of falls; and age, health, and capability as well as problem-solving styles to prevent falls. Fall prevention advice was rarely given when men accessed the health system at the time of a fall. Contrary to dominant expectations about masculine identity, many men acknowledged fall vulnerability indicating they would attend or consider attending, a fall prevention program. Health professionals can better engage men by providing consistent messages that falls can be prevented; tailoring advice, understanding men are at different stages in their awareness of fall risk and preferences for action; and by being aware of their own assumptions that can act as barriers to speaking with men about fall prevention. Implications for rehabilitation Men accessing the health system at the time of the fall, and during rehabilitation following a fall represent prime opportunities for health professionals to speak with men about preventing falls and make appropriate referrals to community programs. Tailored advice will take account of individual men's perceptions of preventability; personal relevance; perceptions of age, health and capability; and problem-solving styles.

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

PubMed Central

Özdemir, Ahmet Turan

2016-01-01

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

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

PubMed

Özdemir, Ahmet Turan

2016-07-25

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

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

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

PubMed

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

2014-01-01

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

A Multicomponent Fall Prevention Strategy Reduces Falls at an Academic Medical Center.

PubMed

France, Dan; Slayton, Jenny; Moore, Sonya; Domenico, Henry; Matthews, Julia; Steaban, Robin L; Choma, Neesha

2017-09-01

While the reduction in fall rates has not kept pace with the reduction of other hospital-acquired conditions, patient safety research and quality improvement (QI) initiatives at the system and hospital levels have achieved positive results and provide insights into potentially effective risk reduction strategies. An academic medical center developed a QI-based multicomponent strategy for fall prevention and pilot tested it for six months in three high-risk units-the Neuroscience Acute Care Unit, the Myelosuppression/Stem Cell Transplant Unit, and the Acute Care for the Elderly Unit-before implementing and evaluating the strategy hospitalwide. The multicomponent fall strategy was evaluated using a pre-post study design. The main outcome measures were falls and falls with harm measured in events per 1,000 patient-days. Fall rates were monitored and compared for three classes of falls: (1) accidental, (2) anticipated physiologic, and (3) unanticipated physiologic. Statistical process control charts showed that the pilot units had achieved significant reductions in falls with harm during the last five months of data collection. Wald test and segmented regression analyses revealed significant improvements in pooled postintervention fall rates, stratified by fall type. The hospitalwide implementation of the program resulted in a 47% overall reduction in falls in the postintervention period. A fall prevention strategy that targeted the spectrum of risk factors produced measurable improvement in fall rates and rates of patient harm. Hospitals must continue developing, rigorously testing, and sharing their results and experiences in implementing and sustaining multicomponent fall prevention strategies. Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.

49 CFR 214.105 - Fall protection systems standards and practices.

Code of Federal Regulations, 2011 CFR

2011-10-01

.... (8) Connectors shall be drop forged, pressed or formed steel, or made of equivalent-strength... bridge worker shall have a minimum breaking strength of 5,000 pounds. (2) Self-retracting lifelines and... travels to 3.5 feet. (9) The personal fall arrest system shall have sufficient strength to withstand twice...

49 CFR 214.105 - Fall protection systems standards and practices.

Code of Federal Regulations, 2014 CFR

2014-10-01

.... (8) Connectors shall be drop forged, pressed or formed steel, or made of equivalent-strength... bridge worker shall have a minimum breaking strength of 5,000 pounds. (2) Self-retracting lifelines and... travels to 3.5 feet. (9) The personal fall arrest system shall have sufficient strength to withstand twice...

49 CFR 214.105 - Fall protection systems standards and practices.

Code of Federal Regulations, 2010 CFR

2010-10-01

.... (8) Connectors shall be drop forged, pressed or formed steel, or made of equivalent-strength... bridge worker shall have a minimum breaking strength of 5,000 pounds. (2) Self-retracting lifelines and... travels to 3.5 feet. (9) The personal fall arrest system shall have sufficient strength to withstand twice...

49 CFR 214.105 - Fall protection systems standards and practices.

Code of Federal Regulations, 2013 CFR

2013-10-01

.... (8) Connectors shall be drop forged, pressed or formed steel, or made of equivalent-strength... bridge worker shall have a minimum breaking strength of 5,000 pounds. (2) Self-retracting lifelines and... travels to 3.5 feet. (9) The personal fall arrest system shall have sufficient strength to withstand twice...

49 CFR 214.105 - Fall protection systems standards and practices.

Code of Federal Regulations, 2012 CFR

2012-10-01

.... (8) Connectors shall be drop forged, pressed or formed steel, or made of equivalent-strength... bridge worker shall have a minimum breaking strength of 5,000 pounds. (2) Self-retracting lifelines and... travels to 3.5 feet. (9) The personal fall arrest system shall have sufficient strength to withstand twice...

Exploring user experience and technology acceptance for a fall prevention system: results from a randomized clinical trial and a living lab.

PubMed

Vaziri, Daryoush D; Aal, Konstantin; Ogonowski, Corinna; Von Rekowski, Thomas; Kroll, Michael; Marston, Hannah R; Poveda, Rakel; Gschwind, Yves J; Delbaere, Kim; Wieching, Rainer; Wulf, Volker

2016-01-01

Falls are common in older adults and can result in serious injuries. Due to demographic changes, falls and related healthcare costs are likely to increase over the next years. Participation and motivation of older adults in fall prevention measures remain a challenge. The iStoppFalls project developed an information and communication technology (ICT)-based system for older adults to use at home in order to reduce common fall risk factors such as impaired balance and muscle weakness. The system aims at increasing older adults' motivation to participate in ICT-based fall prevention measures. This article reports on usability, user-experience and user-acceptance aspects affecting the use of the iStoppFalls system by older adults. In the course of a 16-week international multicenter study, 153 community-dwelling older adults aged 65+ participated in the iStoppFalls randomized controlled trial, of which half used the system in their home to exercise and assess their risk of falling. During the study, 60 participants completed questionnaires regarding the usability, user experience and user acceptance of the iStoppFalls system. Usability was measured with the System Usability Scale (SUS). For user experience the Physical Activity Enjoyment Scale (PACES) was applied. User acceptance was assessed with the Dynamic Acceptance Model for the Re-evaluation of Technologies (DART). To collect more detailed data on usability, user experience and user acceptance, additional qualitative interviews and observations were conducted with participants. Participants evaluated the usability of the system with an overall score of 62 (Standard Deviation, SD 15.58) out of 100, which suggests good usability. Most users enjoyed the iStoppFalls games and assessments, as shown by the overall PACES score of 31 (SD 8.03). With a score of 0.87 (SD 0.26), user acceptance results showed that participants accepted the iStoppFalls system for use in their own home. Interview data suggested that certain

An expert system for the quantification of fault rates in construction fall accidents.

PubMed

Talat Birgonul, M; Dikmen, Irem; Budayan, Cenk; Demirel, Tuncay

2016-01-01

Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports. Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases. DsSafe gives satisfactory results.

Seniors Falls Investigative Methodology (SFIM): A Systems Approach to the Study of Falls in Seniors

ERIC Educational Resources Information Center

Zecevic, Aleksandra A.; Salmoni, Alan W.; Lewko, John H.; Vandervoort, Anthony A.

2007-01-01

An in-depth understanding of human factors and human error is lacking in current research on seniors' falls. Additional knowledge is needed to understand why seniors are falling. The purpose of this article is to describe the adapting of the Integrated Safety Investigation Methodology (ISIM) (used for investigating transportation and industrial…

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.

Falling and fall risk factors in adults with haemophilia: an exploratory study.

PubMed

Sammels, M; Vandesande, J; Vlaeyen, E; Peerlinck, K; Milisen, K

2014-11-01

Falls are a particular risk in persons with haemophilia (PWH) because of damaged joints, high risk of bleeding, possible impact on the musculoskeletal system and functioning and costs associated with treatment for these fall-related injuries. In addition, fall risk increases with age and PWH are increasingly entering the over 65 age group. The aim of this study was to determine the occurrence of falls during the past year and to explore which fall risk factors are present in community-dwelling PWH. Dutch speaking community-dwelling adults were included from the age of 40 years with severe or moderate haemophilia A or B, independent in their mobility and registered at the University Hospitals Leuven. They were asked to come to the haemophilia centre; otherwise a telephone survey was conducted. Demographic and social variables, medical variables, fall evaluation and clinical variables were queried. From the 89 PWH, 74 (83.1%) participated in the study. Twenty-four (32.4%) fell in the past year, and 10 of them (41.7%) more than once with an average of four falls. Living conditions, physical activity, avoidance of winter sports due to fear of falling, orthopaedic status, urinary incontinence and mobility impairments are potential fall risk factors in adult PWH. This exploratory study indicates that PWH are attentive to falling since they are at higher risk for falls and because of the serious consequences it might have. Screening and fall prevention should be stimulated in the daily practice of haemophilia care. © 2014 John Wiley & Sons Ltd.

29 CFR Appendix B to Subpart I of... - General Testing Conditions and Additional Guidelines for Personal Fall Protection Systems (Non...

Code of Federal Regulations, 2010 CFR

2010-07-01

...) OCCUPATIONAL SAFETY AND HEALTH STANDARDS FOR SHIPYARD EMPLOYMENT Personal Protective Equipment (PPE) Pt. 1915... Personal Fall Protection Systems (Non-Mandatory) B Appendix B to Subpart I of Part 1915 Labor Regulations... Guidelines for Personal Fall Protection Systems (Non-Mandatory) 1. Personal fall arrest systems—(a) General...

29 CFR Appendix B to Subpart I of... - General Testing Conditions and Additional Guidelines for Personal Fall Protection Systems (Non...

Code of Federal Regulations, 2011 CFR

2011-07-01

...) OCCUPATIONAL SAFETY AND HEALTH STANDARDS FOR SHIPYARD EMPLOYMENT Personal Protective Equipment (PPE) Pt. 1915... Personal Fall Protection Systems (Non-Mandatory) B Appendix B to Subpart I of Part 1915 Labor Regulations... Guidelines for Personal Fall Protection Systems (Non-Mandatory) 1. Personal fall arrest systems—(a) General...

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.

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.

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

Meteorite falls in Africa

NASA Astrophysics Data System (ADS)

Khiri, Fouad; Ibhi, Abderrahmane; Saint-Gerant, Thierry; Medjkane, Mohand; Ouknine, Lahcen

2017-10-01

The study of meteorites provides insight into the earliest history of our solar system. From 1800, about the year meteorites were first recognized as objects falling from the sky, until December 2014, 158 observed meteorite falls were recorded in Africa. Their collected mass ranges from 1.4 g to 175 kg with the 1-10 kg cases predominant. The average rate of African falls is low with only one fall recovery per 1.35-year time interval (or 0.023 per year per million km2). This African collection is dominated by ordinary chondrites (78%) just like in the worldwide falls. The seventeen achondrites include three Martian meteorite falls (Nakhla of Egypt, Tissint of Morocco and Zagami of Nigeria). Observed Iron meteorite falls are relatively rare and represent only 5%. The falls' rate in Africa is variable in time and in space. The number of falls continues to grow since 1860, 80% of which were recovered during the period between 1910 and 2014. Most of these documented meteorite falls have been recovered from North-Western Africa, Eastern Africa and Southern Africa. They are concentrated in countries which have a large surface area and a large population with a uniform distribution. Other factors are also favorable for observing and collecting meteorite falls across the African territory, such as: a genuine meteorite education, a semi-arid to arid climate (clear sky throughout the year most of the time), croplands or sparse grasslands and possible access to the fall location with a low percentage of forest cover and dense road network.

Graduation and Persistence Rates: University of Hawaii Community Colleges, Fall 1987-Fall 1995 Cohorts. A Summary of Selected Data from the NCHEMS/University of Hawaii System Longitudinal Database Project. Special Report.

ERIC Educational Resources Information Center

Hawaii Univ., Honolulu. Institutional Research Office.

This report details graduation and persistence rates for degree-seeking students at the seven University of Hawaii Community Colleges (UHCC) from Fall 1987-Fall 1995. The data are from the National Center for Higher Education Management Systems/University of Hawaii System Longitudinal Database Project. The report focuses on full-time and part-time…

Fall 2014 SEI Research Review High Confidence Cyber Physical Systems

DTIC Science & Technology

2014-10-28

2014 Carnegie Mellon University Fall 2014 SEI Research Review High Confidence Cyber Physical Systems Software Engineering Institute Carnegie... Research Review de Niz Oct 28th, 2014 © 2014 Carnegie Mellon University Copyright 2014 Carnegie Mellon University This material is based upon work...Software Engineering Institute, a federally funded research and development center. Any opinions, findings and conclusions or recommendations expressed

29 CFR Appendix C to Subpart M of... - Personal Fall Arrest Systems

Code of Federal Regulations, 2014 CFR

2014-07-01

... appendix D of this subpart, the test methods listed here in appendix C can also be used to assist employers... about the system based on its performance during testing so that the employer can know if the system... deceleration device of the self-retracting type since this can result in additional free fall for which the...

29 CFR Appendix C to Subpart M of... - Personal Fall Arrest Systems

Code of Federal Regulations, 2013 CFR

2013-07-01

... appendix D of this subpart, the test methods listed here in appendix C can also be used to assist employers... about the system based on its performance during testing so that the employer can know if the system... deceleration device of the self-retracting type since this can result in additional free fall for which the...

29 CFR Appendix C to Subpart M of... - Personal Fall Arrest Systems

Code of Federal Regulations, 2012 CFR

2012-07-01

... appendix D of this subpart, the test methods listed here in appendix C can also be used to assist employers... about the system based on its performance during testing so that the employer can know if the system... deceleration device of the self-retracting type since this can result in additional free fall for which the...

Developing a multi-systemic fall prevention model, incorporating the physical environment, the care process and technology: a systematic review.

PubMed

Choi, Young-Seon; Lawler, Erin; Boenecke, Clayton A; Ponatoski, Edward R; Zimring, Craig M

2011-12-01

This paper reports a review that assessed the effectiveness and characteristics of fall prevention interventions implemented in hospitals. A multi-systemic fall prevention model that establishes a practical framework was developed from the evidence. Falls occur through complex interactions between patient-related and environmental risk factors, suggesting a need for multifaceted fall prevention approaches that address both factors. We searched Medline, CINAHL, PsycInfo and the Web of Science databases for references published between January 1990 and June 2009 and scrutinized secondary references from acquired papers. Due to the heterogeneity of interventions and populations, we conducted a quantitative systematic review without a meta-analysis and used a narrative summary to report findings. From the review, three distinct characteristics of fall prevention interventions emerged: (1) the physical environment, (2) the care process and culture and (3) technology. While clinically significant evidence shows the efficacy of environment-related interventions in reducing falls and fall-related injuries, the literature identified few hospitals that had introduced environment-related interventions in their multifaceted fall intervention strategies. Using the multi-systemic fall prevention model, hospitals should promote a practical strategy that benefits from the collective effects of the physical environment, the care process and culture and technology to prevent falls and fall-related injuries. By doing so, they can more effectively address the various risk factors for falling and therefore, prevent falls. Studies that test the proposed model need to be conducted to establish the efficacy of the model in practice. © 2011 The Authors. Journal of Advanced Nursing © 2011 Blackwell Publishing Ltd.

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

Falls and Fall-Related Injuries among Community-Dwelling Adults in the United States

PubMed Central

Verma, Santosh K.; Willetts, Joanna L.; Corns, Helen L.; Marucci-Wellman, Helen R.; Lombardi, David A.; Courtney, Theodore K.

2016-01-01

Introduction Falls are the leading cause of unintentional injuries in the U.S.; however, national estimates for all community-dwelling adults are lacking. This study estimated the national incidence of falls and fall-related injuries among community-dwelling U.S. adults by age and gender and the trends in fall-related injuries across the adult life span. Methods Nationally representative data from the National Health Interview Survey (NHIS) 2008 Balance and Dizziness supplement was used to develop national estimates of falls, and pooled data from the NHIS was used to calculate estimates of fall-related injuries in the U.S. and related trends from 2004–2013. Costs of unintentional fall-related injuries were extracted from the CDC’s Web-based Injury Statistics Query and Reporting System. Results Twelve percent of community-dwelling U.S. adults reported falling in the previous year for a total estimate of 80 million falls at a rate of 37.2 falls per 100 person-years. On average, 9.9 million fall-related injuries occurred each year with a rate of 4.38 fall-related injuries per 100 person-years. In the previous three months, 2.0% of older adults (65+), 1.1% of middle-aged adults (45–64) and 0.7% of young adults (18–44) reported a fall-related injury. Of all fall-related injuries among community-dwelling adults, 32.3% occurred among older adults, 35.3% among middle-aged adults and 32.3% among younger adults. The age-adjusted rate of fall-related injuries increased 4% per year among older women (95% CI 1%–7%) from 2004 to 2013. Among U.S. adults, the total lifetime cost of annual unintentional fall-related injuries that resulted in a fatality, hospitalization or treatment in an emergency department was 111 billion U.S. dollars in 2010. Conclusions Falls and fall-related injuries represent a significant health and safety problem for adults of all ages. The findings suggest that adult fall prevention efforts should consider the entire adult lifespan to ensure a

Development and Field Testing of the FootFall Planning System for the ATHLETE Robots

NASA Technical Reports Server (NTRS)

SunSpiral, Vytas; Wheeler, D. W.; Chavez-Clementa, Daniel; Mittman, David

2011-01-01

The FootFall Planning System is a ground-based planning and decision support system designed to facilitate the control of walking activities for the ATHLETE (All-Terrain Hex-Limbed Extra-Terrestrial Explorer) family of robots. ATHLETE was developed at NASA's Jet Propulsion Laboratory (JPL) and is a large six-legged robot designed to serve multiple roles during manned and unmanned missions to the Moon; its roles include transportation, construction and exploration. Over the four years from 2006 through 2010 the FootFall Planning System was developed and adapted to two generations of the ATHLETE robots and tested at two analog field sites (the Human Robotic Systems Project's Integrated Field Test at Moses Lake, Washington, June 2008, and the Desert Research and Technology Studies (D-RATS), held at Black Point Lava Flow in Arizona, September 2010). Having 42 degrees of kinematic freedom, standing to a maximum height of just over 4 meters, and having a payload capacity of 450 kg in Earth gravity, the current version of the ATHLETE robot is a uniquely complex system. A central challenge to this work was the compliance of the high-DOF (Degree Of Freedom) robot, especially the compliance of the wheels, which affected many aspects of statically-stable walking. This paper will review the history of the development of the FootFall system, sharing design decisions, field test experiences, and the lessons learned concerning compliance and self-awareness.

Archeological treasures protection based on early forest wildfire multi-band imaging detection system

NASA Astrophysics Data System (ADS)

Gouverneur, B.; Verstockt, S.; Pauwels, E.; Han, J.; de Zeeuw, P. M.; Vermeiren, J.

2012-10-01

Various visible and infrared cameras have been tested for the early detection of wildfires to protect archeological treasures. This analysis was possible thanks to the EU Firesense project (FP7-244088). Although visible cameras are low cost and give good results during daytime for smoke detection, they fall short under bad visibility conditions. In order to improve the fire detection probability and reduce the false alarms, several infrared bands are tested ranging from the NIR to the LWIR. The SWIR and the LWIR band are helpful to locate the fire through smoke if there is a direct Line Of Sight. The Emphasis is also put on the physical and the electro-optical system modeling for forest fire detection at short and longer ranges. The fusion in three bands (Visible, SWIR, LWIR) is discussed at the pixel level for image enhancement and for fire detection.

Epidemiology of Falls in Older Age

ERIC Educational Resources Information Center

Peel, Nancye May

2011-01-01

Worldwide, falls among older people are a public health concern because of their frequency and adverse consequences in terms of morbidity, mortality, and quality of life, as well as their impact on health system services and costs. This epidemiological review outlines the public health burden of falls and fall-related injuries and the impact of…

Falling Chains as Variable-Mass Systems: Theoretical Model and Experimental Analysis

ERIC Educational Resources Information Center

de Sousa, Celia A.; Gordo, Paulo M.; Costa, Pedro

2012-01-01

In this paper, we revisit, theoretically and experimentally, the fall of a folded U-chain and of a pile-chain. The model calculation implies the division of the whole system into two subsystems of variable mass, allowing us to explore the role of tensional contact forces at the boundary of the subsystems. This justifies, for instance, that the…

Habitat use and movement patterns by adult saugers from fall to summer in an unimpounded small-river system

USGS Publications Warehouse

Kuhn, K.M.; Hubert, W.A.; Johnson, K.; Oberlie, D.; Dufek, D.

2008-01-01

The Little Wind River drainage in Wyoming is a relatively small unimpounded river system inhabited by native saugers Sander canadensis. Radio telemetry was used to assess habitat use and movement patterns by adult saugers in the river system from fall through early summer. Fifty-four adult saugers were captured during fall 2004, surgically implanted with radio transmitters, and tracked through mid-July 2005. Tagged saugers selected large and deep pools. Such pools were abundant throughout the Little Wind River system and led to saugers being widely dispersed from fall to early spring. During fall, winter, and early spring, tagged saugers remained sedentary and moved short distances among pools in close proximity to each other. Longer movements by tagged saugers occurred from mid-spring to early summer, and were associated with both upstream and downstream movements to and from two river segments believed to be used for spawning. During early summer, most saugers returned to locations where they had been tagged the previous fall and had spent the winter. Our results provide evidence that preservation of the sauger fishery in the Wind River system will depend on maintaining fish passage throughout the portion of the watershed inhabited by saugers and preserving natural fluvial processes that maintain large and deep pools. ?? Copyright by the American Fisheries Society 2008.

Investigating passage of ESA-listed juvenile fall Chinook salmon at Lower Granite Dam during winter when the fish bypass system is not operated. 2006 Annual Report

USGS Publications Warehouse

Tiffan, Kenneth F.; Kock, Tobias J.; Connor, William P.

2007-01-01

During the winter of 2005-06, we radio and PIT tagged and released 48 juvenile fall Chinook salmon to evaluate over-wintering behavior and dam passage in the lower Snake River, Washington. Fish were released at the upstream end of the Lower Granite Dam forebay in November and December 2005. Fixed radio telemetry detection sites located in forebay and tailrace areas of Lower Granite, Little Goose, Lower Monumental and Ice Harbor dams were used to monitor fish movements and dam passage through early-May 2006. Of the 48 fish released during our study, 39 (81 %) passed Lower Granite Dam and were detected at downstream detection sites, 29 (60%) passed Little Goose Dam, 25 (52%) passed Lower Monumental Dam, and 15 (31%) passed Ice Harbor Dam. Thirty-seven (95%), 23 (79%), 16 (64%), and 9 (60%) of the fish that passed Lower Granite, Little Goose, Lower Monumental, and Ice Harbor dams respectively, did so when the fish bypass system was not operated. Passage of tagged fish past lower Snake River dams generally declined during the winter, but increased again after bypass began in April. Fish residence times in reservoirs and forebays was lengthy during the winter (up to 118 d), and varied by reservoir and time of year. We observed no diel passage trends. Only 15 of the 48 fish were subsequently detected at a PIT-tag interrogation site the following spring. 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 also have implications for transportation studies of subyearling fall Chinook salmon in the Snake River. Specifically, the finding that some fish can pass undetected during the winter may bias smolt-to-adult return rate calculations that are typically used to measure the

Low-impact falls: demands on a system of trauma management, prediction of outcome, and influence of comorbidities.

PubMed

Kennedy, R L; Grant, P T; Blackwell, D

2001-10-01

Falls from a low height are an extremely common source of injury, the severity of which is often underestimated. As a result, low fall patients are usually not transferred to Level I trauma centers. There are surprisingly few systematic data relating to the demands made on systems of trauma care by patients with low falls. This study addresses this issue using information from a comprehensive national trauma database. The performance of TRISS methodology, and the factors associated with prolonged hospital stay, in low fall patients is also examined. The study included 31,419 patients. Patients with low falls (< 2 m) were compared with those suffering high falls (> or = 2 m), motor vehicle crashes, assault, sports injuries, and a group with unclassified injuries. Probability of survival was estimated using TRISS, and its performance in different types of injury was assessed using measures of discrimination and calibration. The influence of coexistent medical conditions on mortality and length of stay was investigated using logistic regression. Low falls accounted for 45.5% of all admissions, and 43.9% of the total bed days. The low fall group was older (mean age, 61.6 years), and predominantly female (62.5%) in contrast to the other groups (both p < 0.001). There were fewer severely injured patients than in all of the other groups except sports injuries. The area under the receiver operating characteristic curve for TRISS applied to low falls (0.874) was less than that for high falls (0.969), motor vehicle crashes (0.973), assaults (0.960), sports (1.000), and unclassified injuries (0.965). Also, the calibration of the TRISS model was poor for patients with low falls. A logistic regression model derived from a training set of 5,000 patients gave slightly improved discrimination and markedly improved calibration when compared with TRISS. Although there was a strong relationship between the number of coexistent medical conditions and the risk of dying after a low fall

Women's perspectives on falls and fall prevention during pregnancy.

PubMed

Brewin, Dorothy; Naninni, Angela

2014-01-01

Falls are the leading cause of unintentional injury in women. During pregnancy, even a minor fall can result in adverse consequences. Evidence to inform effective and developmentally appropriate pregnancy fall prevention programs is lacking. Early research on pregnancy fall prevention suggests that exercise may reduce falls. However, acceptability and effectiveness of pregnancy fall prevention programs are untested. To better understand postpartum women's perspective and preferences on fall prevention strategies during pregnancy to formulate an intervention. Focus groups and individual interviews were conducted with 31 postpartum women using descriptive qualitative methodology. Discussion of falls during pregnancy and fall prevention strategies was guided by a focus group protocol and enhanced by 1- to 3-minute videos on proposed interventions. Focus groups were audio recorded, transcribed, and analyzed using NVivo 10 software. Emerging themes were environmental circumstances and physical changes of pregnancy leading to a fall, prevention strategies, barriers, safety concerns, and marketing a fall prevention program. Wet surfaces and inappropriate footwear commonly contributed to falls. Women preferred direct provider counseling and programs including yoga and Pilates. Fall prevention strategies tailored to pregnant women are needed. Perspectives of postpartum women support fall prevention through provider counseling and individual or supervised exercise programs.

Development and clinical application of a computer-aided real-time feedback system for detecting in-bed physical activities.

PubMed

Lu, Liang-Hsuan; Chiang, Shang-Lin; Wei, Shun-Hwa; Lin, Chueh-Ho; Sung, Wen-Hsu

2017-08-01

Being bedridden long-term can cause deterioration in patients' physiological function and performance, limiting daily activities and increasing the incidence of falls and other accidental injuries. Little research has been carried out in designing effective detecting systems to monitor the posture and status of bedridden patients and to provide accurate real-time feedback on posture. The purposes of this research were to develop a computer-aided system for real-time detection of physical activities in bed and to validate the system's validity and test-retest reliability in determining eight postures: motion leftward/rightward, turning over leftward/rightward, getting up leftward/rightward, and getting off the bed leftward/rightward. The in-bed physical activity detecting system consists mainly of a clinical sickbed, signal amplifier, a data acquisition (DAQ) system, and operating software for computing and determining postural changes associated with four load cell sensing components. Thirty healthy subjects (15 males and 15 females, mean age = 27.8 ± 5.3 years) participated in the study. All subjects were asked to execute eight in-bed activities in a random order and to participate in an evaluation of the test-retest reliability of the results 14 days later. Spearman's rank correlation coefficient was used to compare the system's determinations of postural states with researchers' recordings of postural changes. The test-retest reliability of the system's ability to determine postures was analyzed using the interclass correlation coefficient ICC(3,1). The system was found to exhibit high validity and accuracy (r = 0.928, p < 0.001; accuracy rate: 87.9%) in determining in-bed displacement, turning over, sitting up, and getting off the bed. The system was particularly accurate in detecting motion rightward (90%), turning over leftward (83%), sitting up leftward or rightward (87-93%), and getting off the bed (100%). The test-retest reliability ICC(3

Falls and fall-related injuries in older dialysis patients.

PubMed

Cook, Wendy L; Tomlinson, George; Donaldson, Meghan; Markowitz, Samuel N; Naglie, Gary; Sobolev, Boris; Jassal, Sarbjit V

2006-11-01

Dialysis patients are increasingly older and more disabled. In community-dwelling seniors without kidney disease, falls commonly predict hospitalization, the onset of frailty, and the need for institutional care. Effective fall prevention strategies are available. On the basis of retrospective data, it was hypothesized that the fall rates of older (> or =65 yr) chronic outpatient hemodialysis (HD) patients would be higher than published rates for community-dwelling seniors (0.6 to 0.8 falls/patient-year). It also was hypothesized that risk factors for falls in dialysis outpatients would include polypharmacy, dialysis-related hypotension, cognitive impairment, and decreased functional status. Using a prospective cohort study design, HD patients who were > or =65 yr of age at a large academic dialysis unit were recruited. All study participants underwent baseline screening for fall risk factors. Patients were followed prospectively for a minimum of 1 yr. Falls were identified through biweekly patient interviews in the HD unit. A total of 162 patients (mean age 74.7 yr) were recruited; 57% were male. A total of 305 falls occurred in 76 (47%) patients over 190.5 person-years of follow-up (fall-incidence 1.60 falls/person-year). Injuries occurred in 19% of falls; 41 patients had multiple falls. Associated risk factors included age, comorbidity, mean predialysis systolic BP, and a history of falls. In the HD population, the fall risk is higher than in the general community, and fall-related morbidity is high. Better identification of HD patients who are at risk for falls and targeted fall intervention strategies are required.

Exploring the relationship between fall risk-increasing drugs and fall-related fractures.

PubMed

De Winter, Sabrina; Vanwynsberghe, Sarah; Foulon, Veerle; Dejaeger, Eddy; Flamaing, Johan; Sermon, An; Van der Linden, Lorenz; Spriet, Isabel

2016-04-01

Hospital admissions due to fall-related fractures are a major problem in the aging population. Several risk factors have been identified, including drug use. Most studies often retrieved prescription-only drugs from national databases. These are associated with some limitations as they do not always reliably reproduce the complete patient's active drug list. To evaluate the association between the number of FRIDs intake identified by a standardised medication reconciliation process and a fall-related fracture leading to a hospital admission in older adults. The first cohort has been recruited from one traumatology ward of a tertiary teaching hospital in Belgium and the second cohort has been recruited from 11 community pharmacies in Belgium. A prospective study with two individually matched cohorts was performed. Adult patients (≥75 years) admitted with an injury due to a fall were included in the first cohort (faller group). The second cohort consisted of patients who did not suffer from a fall within the last 6 months (non-faller group). Matching was performed for age, gender, place of residence and use of a walking aid. In both groups, clinical pharmacists and undergraduate pharmacy students obtained the medication history, using a standardised approach. A list of drugs considered to increase the risk of falling was created. It included cardiovascular drugs and drugs acting on the nervous system. A linear mixed model was used to compare the number of fall risk-increasing drugs between fallers and non-fallers. The number of fall risk-increasing drugs in a faller versus a non-faller group. Sixty-one patients were matched with 121 non-fallers. Patients received on average 3.1 ± 2.1 and 3.2 ± 1.8 fall risk-increasing drugs in the faller and in the non-faller group, respectively. The mean number of fall risk-increasing drugs was comparable in both groups (p = 0.844), even after adjusting for alcohol consumption, fear of falling, vision and foot problems (p = 0

Business model for sensor-based fall recognition systems.

PubMed

Fachinger, Uwe; Schöpke, Birte

2014-01-01

AAL systems require, in addition to sophisticated and reliable technology, adequate business models for their launch and sustainable establishment. This paper presents the basic features of alternative business models for a sensor-based fall recognition system which was developed within the context of the "Lower Saxony Research Network Design of Environments for Ageing" (GAL). The models were developed parallel to the R&D process with successive adaptation and concretization. An overview of the basic features (i.e. nine partial models) of the business model is given and the mutual exclusive alternatives for each partial model are presented. The partial models are interconnected and the combinations of compatible alternatives lead to consistent alternative business models. However, in the current state, only initial concepts of alternative business models can be deduced. The next step will be to gather additional information to work out more detailed models.

Does fall history influence residential adjustments?

PubMed

Leland, Natalie; Porell, Frank; Murphy, Susan L

2011-04-01

To determine whether reported falls at baseline are associated with an older adult's decision to make a residential adjustment (RA) and the type of adjustment made in the subsequent 2 years. Observations (n = 25,036) were from the Health and Retirement Study, a nationally representative sample of community-living older adults, 65 years of age and older. At baseline, fall history (no fall, 1 fall no injury, 2 or more falls no injury, or 1 or more falls with an injury) and factors potentially associated with RA were used to predict the initiation of an RA (i.e., moving, home modifications, increased use of adaptive equipment, family support, or personal care assistance) during the subsequent 2 years. Compared with those with no history of falls, individuals with a history of falls had higher odds of making any RA. Among those making an RA, individuals with an injurious fall were more likely than those with no history of a fall to start using adaptive equipment or increase their use of personal care assistance. The higher initiation of RAs among fallers may indicate proactive steps to prevent future falls and may be influenced by interactions with the health care system. To optimize fall prevention efforts, older adults would benefit from education and interventions addressing optimal use of RAs before falls occur.

Static Posturography and Falls According to Pyramidal, Sensory and Cerebellar Functional Systems in People with Multiple Sclerosis

PubMed Central

Kalron, Alon; Givon, Uri; Frid, Lior; Dolev, Mark; Achiron, Anat

2016-01-01

Balance impairment is common in people with multiple sclerosis (PwMS) and frequently impacts quality of life by decreasing mobility and increasing the risk of falling. However, there are only scarce data examining the contribution of specific neurological functional systems on balance measures in MS. Therefore, the primary aim of our study was to examine the differences in posturography parameters and fall incidence according to the pyramidal, cerebellar and sensory systems functional systems in PwMS. The study included 342 PwMS, 211 women and mean disease duration of 8.2 (S.D = 8.3) years. The study sample was divided into six groups according to the pyramidal, cerebellar and sensory functional system scores, derived from the Expanded Disability Status Scale (EDSS) data. Static postural control parameters were obtained from the Zebris FDM-T Treadmill (zebris® Medical GmbH, Germany). Participants were defined as "fallers" and "non-fallers" based on their fall history. Our findings revealed a trend that PwMS affected solely in the pyramidal system, have reduced stability compared to patients with cerebellar and sensory dysfunctions. Moreover, the addition of sensory impairments to pyramidal dysfunction does not exacerbate postural control. The patients in the pure sensory group demonstrated increased stability compared to each of the three combined groups; pyramidal-cerebellar, pyramidal-sensory and pyramidal-cerebellar-sensory groups. As for fall status, the percentage of fallers in the pure pyramidal, cerebellar and sensory groups were 44.3%, 33.3% and 19.5%, respectively. As for the combined functional system groups, the percentage of fallers in the pyramidal-cerebellar, pyramidal-sensory and pyramidal-cerebellar-sensory groups were 59.7%, 40.7% and 65%, respectively. This study confirms that disorders in neurological functional systems generate different effects on postural control and incidence of falls in the MS population. From a clinical standpoint, the

Improving safety culture in hospitals: Facilitators and barriers to implementation of Systemic Falls Investigative Method (SFIM).

PubMed

Zecevic, Aleksandra A; Li, Alvin Ho-Ting; Ngo, Charity; Halligan, Michelle; Kothari, Anita

2017-06-01

The purpose of this study was to assess the facilitators and barriers to implementation of the Systemic Falls Investigative Method (SFIM) on selected hospital units. A cross-sectional explanatory mixed methods design was used to converge results from a standardized safety culture survey with themes that emerged from interviews and focus groups. Findings were organized by six elements of the Ottawa Model of Research Use framework. A geriatric rehabilitation unit of an acute care hospital and a neurological unit of a rehabilitation hospital were selected purposefully due to the high frequency of falls. Hospital staff who took part in: surveys (n = 39), interviews (n = 10) and focus groups (n = 12), and 38 people who were interviewed during falls investigations: fallers, family, unit staff and hospital management. Implementation of the SFIM to investigate fall occurrences. Percent of positive responses on the Modified Stanford Patient Safety Culture Survey Instrument converged with qualitative themes on facilitators and barriers for intervention implementation. Both hospital units had an overall poor safety culture which hindered intervention implementation. Facilitators were hospital accreditation, strong emphasis on patient safety, infrastructure and dedicated champions. Barriers included heavy workloads, lack of time, lack of resources and poor communication. Successful implementation of SFIM requires regulatory and organizational support, committed frontline staff and allocation of resources to identify active causes and latent contributing factors to falls. System-wide adjustments show promise for promotion of safety culture in hospitals where falls happen regularly. © The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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.

Electronic Out-fall Inspection Application - 12007

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

Weymouth, A Kent III; Pham, Minh; Messick, Chuck

2012-07-01

In early 2009 an exciting opportunity was presented to the Geographic Information Systems (GIS) team at the Savannah River Site (SRS). The SRS maintenance group was directed to maintain all Out-falls on Site, increasing their workload from 75 to 183 out-falls with no additional resources. The existing out-fall inspection system consisted of inspections performed manually and documented via paper trail. The inspections were closed out upon completion of activities and placed in file cabinets with no central location for tracking/trending maintenance activities. A platform for meeting new improvements required for documentation by the Department of Health and Environmental Control (DHEC)more » out-fall permits was needed to replace this current system that had been in place since the 1980's. This was accomplished by building a geographically aware electronic application that improved reliability of site out-fall maintenance and ensured consistent standards were maintained for environmental excellence and worker efficiency. Inspections are now performed via tablet and uploaded to a central point. Work orders are completed and closed either in the field using tablets (mobile application) or in their offices (via web portal) using PCs. And finally completed work orders are now stored in a central database allowing trending of maintenance activities. (authors)« less

Diversity in fall characteristics hampers effective prevention: the precipitants, the environment, the fall and the injury.

PubMed

Sanders, K M; Lim, K; Stuart, A L; Macleod, A; Scott, D; Nicholson, G C; Busija, L

2017-10-01

Falls among the elderly are common and characteristics may differ between injurious and non-injurious falls. Among 887 older Australian women followed for 1.6 years, 32% fell annually. Only 8.5% resulted in fracture and/or hospital admission. The characteristics of those falls are indistinguishable from those not coming to medical attention. The precipitants and environment of all falls occurring among a large cohort of older Caucasian women were categorised by injury status to determine if the characteristics differed between injurious and non-injurious falls. Among 887 Australian women (70+ years), falls were ascertained using monthly postcard calendars and a questionnaire was administered for each fall. Hospital admissions and fractures were independently confirmed. All falls were reported for a mean observation time of 577 (IQR 546-607) days per participant, equating to a total 1400 person-years. Thirty-two percent fell at least once per year. The most common features of a fall were that the faller was walking (61%) at home (61%) during the day (88%) and lost balance (32%). Only 12% of all falls occurred at night. Despite no difference in the type of injury between day and night, the likelihood of being hospitalised from a fall at night was 4.5 times greater than that of a daytime fall with adjustment for injury type and participant age (OR 4.5, 95% CI 2.1, 9.5; p < 0.001). Of all falls, approximately one third were associated with no injury to the faller (31%), one third reported a single injury (37%) and one third reported more than one injury (32%). In 95% of falls, the faller was not admitted to hospital. Only 5% of falls resulted in fracture(s). Our findings demonstrate the significant diversity of precipitants and environment where falls commonly occur among older community-dwelling women. Falls resulting in fracture and/or hospital admission collectively represent 8.5% of all falls and their characteristics are indistinguishable from falls not coming

Risk Factors for Falls and Fall-Related Injuries in Adults 85 Years of Age and Older

PubMed Central

Grundstrom, Anna C.; Guse, Clare E.; Layde, Peter M.

2011-01-01

Background Falls are a leading cause of morbidity and mortality in older adults. No previous studies on risk factors for falls have focused on adults 85 years and older, the most rapidly growing segment of adults. Methods We examined demographic, health, and behavioral risk factors for falls and fall-related injuries in adults 65 years and older, with a particular focus on adults 85 years and older. We analyzed self-reported information from the Behavioral Risk Factor Surveillance System (BRFSS) for 2008. Results Data was available for 120,923 people aged 65 or older and 12,684 people aged 85 or older. Of those aged 85 or older, 21.3% reported at least one fall in the past 3 months and 7.2% reported at least one fall related injury requiring medical care or limiting activity for a day or longer. Below average general health, male sex, perceived insufficient sleep, health problems requiring assistive devices, alcohol consumption, increasing body mass index and history of stroke were all independently associated with a greater risk of falls or fall related injuries. The greater risk of falling in those 85 years and older appeared to be due to the deterioration of overall health status with age; among those with excellent overall health status, there was no greater risk of falling in adults 85 years and older compared to those 65–84 years of age. Conclusions Our results suggest that those with risk factors for falls and fall-related injuries may be appropriate targets for evidence-based fall prevention programs. PMID:21862143

Risk factors for falls and fall-related injuries in adults 85 years of age and older.

PubMed

Grundstrom, Anna C; Guse, Clare E; Layde, Peter M

2012-01-01

Falls are a leading cause of morbidity and mortality in older adults. No previous studies on risk factors for falls have focused on adults 85 years and older, the most rapidly growing segment of adults. We examined demographic, health, and behavioral risk factors for falls and fall-related injuries in adults 65 years and older, with a particular focus on adults 85 years and older. We analyzed self-reported information from the Behavioral Risk Factor Surveillance System (BRFSS) for 2008. Data was available for 120,923 people aged 65 or older and 12,684 people aged 85 or older. Of those aged 85 or older, 21.3% reported at least one fall in the past 3 months and 7.2% reported at least one fall related injury requiring medical care or limiting activity for a day or longer. Below average general health, male sex, perceived insufficient sleep, health problems requiring assistive devices, alcohol consumption, increasing body mass index and history of stroke were all independently associated with a greater risk of falls or fall related injuries. The greater risk of falling in those 85 years and older appeared to be due to the deterioration of overall health status with age; among those with excellent overall health status, there was no greater risk of falling in adults 85 years and older compared to those 65-84 years of age. Our results suggest that those with risk factors for falls and fall-related injuries may be appropriate targets for evidence-based fall prevention programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

Electrostatic demonstration of free-fall weightlessness

NASA Astrophysics Data System (ADS)

Balukovic, Jasmina; Slisko, Josip; Corona Cruz, Adrian

2015-05-01

The phenomena of free-fall weightlessness have been demonstrated to students for many years in a number of different ways. The essential basis of all these demonstrations is the fact that in free-falling, gravitationally accelerated systems, the weight force and weight-related forces (for example, friction and hydrostatic forces) disappear. In this article, an original electrostatic demonstration of weightlessness is presented. A charged balloon fixed at the opening of a plastic container cannot lift a light styrofoam sphere sitting on the bottom when the container is at rest. However, while the system is in free-fall, the sphere becomes weightless and the charged balloon is able to lift it electrostatically.

Ageing vision and falls: a review.

PubMed

Saftari, Liana Nafisa; Kwon, Oh-Sang

2018-04-23

Falls are the leading cause of accidental injury and death among older adults. One of three adults over the age of 65 years falls annually. As the size of elderly population increases, falls become a major concern for public health and there is a pressing need to understand the causes of falls thoroughly. While it is well documented that visual functions such as visual acuity, contrast sensitivity, and stereo acuity are correlated with fall risks, little attention has been paid to the relationship between falls and the ability of the visual system to perceive motion in the environment. The omission of visual motion perception in the literature is a critical gap because it is an essential function in maintaining balance. In the present article, we first review existing studies regarding visual risk factors for falls and the effect of ageing vision on falls. We then present a group of phenomena such as vection and sensory reweighting that provide information on how visual motion signals are used to maintain balance. We suggest that the current list of visual risk factors for falls should be elaborated by taking into account the relationship between visual motion perception and balance control.

Relationship between subjective fall risk assessment and falls and fall-related fractures in frail elderly people

PubMed Central

2011-01-01

Background Objective measurements can be used to identify people with risks of falls, but many frail elderly adults cannot complete physical performance tests. The study examined the relationship between a subjective risk rating of specific tasks (SRRST) to screen for fall risks and falls and fall-related fractures in frail elderly people. Methods The SRRST was investigated in 5,062 individuals aged 65 years or older who were utilized day-care services. The SRRST comprised 7 dichotomous questions to screen for fall risks during movements and behaviours such as walking, transferring, and wandering. The history of falls and fall-related fractures during the previous year was reported by participants or determined from an interview with the participant's family and care staff. Results All SRRST items showed significant differences between the participants with and without falls and fall-related fractures. In multiple logistic regression analysis adjusted for age, sex, diseases, and behavioural variables, the SRRST score was independently associated with history of falls and fractures. Odds ratios for those in the high-risk SRRST group (≥ 5 points) compared with the no risk SRRST group (0 point) were 6.15 (p < 0.01) for a single fall, 15.04 (p < 0.01) for recurrent falls, and 5.05 (p < 0.01) for fall-related fractures. The results remained essentially unchanged in subgroup analysis accounting for locomotion status. Conclusion These results suggest that subjective ratings by care staff can be utilized to determine the risks of falls and fall-related fractures in the frail elderly, however, these preliminary results require confirmation in further prospective research. PMID:21838891

Executive Control Deficits as a Prodrome to Falls in Healthy Older Adults: A Prospective Study Linking Thinking, Walking, and Falling

PubMed Central

Herman, Talia; Mirelman, Anat; Giladi, Nir; Schweiger, Avraham

2010-01-01

Background. Executive function (EF) deficits may increase fall risk, even among older adults with no overt cognitive impairment. Indeed, the effects of dual tasking (DT) on gait, a challenge to executive control, are more exaggerated in persons with a history of falls. Prospective evidence is, however, lacking. Methods. We prospectively evaluated whether EF predicts falls over a 2-year period among 262 community-living, healthy, and well-functioning older adults, focusing on the 201 who reported no falls during the previous year. At baseline, participants completed a computerized cognitive battery that generated an index of EF and other cognitive domains. Gait was assessed using performance-based tests and by quantifying walking during single- and dual-task conditions. Results. The 262 participants (mean age: 76.3 ± 4.3 years, 60.3% women) had intact cognitive function on testing, a low comorbidity index, and good mobility. The EF index predicted future falls. Among those who reported no previous falls, participants in the worst EF quartile were three times more likely to fall during the 2 years of follow-up, and they were more likely to transition from nonfaller to faller sooner. DT gait variability also predicted future falls and multiple falls, whereas other measures of cognitive function, gait, and mobility did not. Conclusions. Among healthy older adults, individuals with poorer EF are more prone to falls. Higher-level cognitive functions such as those regulated by the frontal lobes are apparently needed for safe everyday navigation that demands multitasking. Optimal screening, early detection, and treatment of falls should, apparently, also target this cognitive domain. PMID:20484336

Fall Prevention in a Neurological Care Unit

ERIC Educational Resources Information Center

Jeffrey, Claudeth

2017-01-01

Patient falls are an ongoing concern for health systems in the US and in the setting where this project took place. Inpatient falls affect consumers and health providers because falls often result in patient morbidity and mortality, legal risk, increased length of stay, and increased costs. The purpose of this project was to evaluate the existing…

Temporal Trends in Fall Rates with the Implementation of a Multifaceted Fall Prevention Program: Persistence Pays Off.

PubMed

Walsh, Catherine M; Liang, Li-Jung; Grogan, Tristan; Coles, Courtney; McNair, Norma; Nuckols, Teryl K

2018-02-01

Most fall prevention programs are only modestly effective, and their sustainability is unknown. An academic medical center implemented a series of fall prevention interventions from 2001 to 2014. The medical center's series of fall prevention interventions were as follows: reorganized the Falls Committee (2001), started flagging high-risk patients (2001), improved fall reporting (2002), increased scrutiny of falls (2005), instituted hourly nursing rounds (2006), reorganized leadership systems (2007), standardized fall prevention equipment (2008), adapted to a move to a new hospital building (2008), routinely investigated root causes (2009), mitigated fall risk during hourly nursing rounds (2009), educated patients about falls (2011), and taught nurses to think critically about risk (2012). To evaluate temporal trends in falls and injury falls, piecewise negative binomial regression with study unit-level random effects was used to analyze structured validated data sets available since 2003. From July 2003 through December 2014, the crude fall rate declined from 3.07 to 2.22 per 1,000 patient days, and injury falls declined from 0.77 to 0.65 per 1,000 patient days. Nonsignificant increases in falls occurred after nurses started rounding hourly and after the move to the new hospital. On the basis of regression models, significant declines occurred after nurses began to mitigate fall risk during hourly rounds (p = 0.009). Instituting incremental changes for more than a decade was associated with a meaningful (about 28%) and sustained decline in falls, although the rate of decline varied over time. Hospitals interested in reducing falls but concerned about competing clinical and financial priorities may find an incremental approach to be effective. Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.

Healthcare Providers’ Perceptions and Self-Reported Fall Prevention Practices: Findings from a Large New York Health System

PubMed Central

Smith, Matthew Lee; Stevens, Judy A.; Ehrenreich, Heidi; Wilson, Ashley D.; Schuster, Richard J.; Cherry, Colleen O’Brien; Ory, Marcia G.

2015-01-01

Among older adults, falls are the leading cause of injury-related deaths and emergency department visits, and the incidence of falls in the United States is rising as the number of older Americans increases. Research has shown that falls can be reduced by modifying fall-risk factors using multifactorial interventions implemented in clinical settings. However, the literature indicates that many providers feel that they do not know how to conduct fall-risk assessments or do not have adequate knowledge about fall prevention. To help healthcare providers incorporate older adult fall prevention (i.e., falls risk assessment and treatment) into their clinical practice, the Centers for Disease Control and Prevention’s (CDC) Injury Center has developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool kit. This study was conducted to identify the practice characteristics and providers’ beliefs, knowledge, and fall-related activities before they received training on how to use the STEADI tool kit. Data were collected as part of a larger State Fall Prevention Project funded by CDC’s Injury Center. Completed questionnaires were returned by 38 medical providers from 11 healthcare practices within a large New York health system. Healthcare providers ranked falls as the lowest priority of five conditions, after diabetes, cardiovascular disease, mental health, and musculoskeletal conditions. Less than 40% of the providers asked most or all of their older patients if they had fallen during the past 12 months. Less than a quarter referred their older patients to physical therapists for balance or gait training, and <20% referred older patients to community-based fall prevention programs. Less than 16% reported they conducted standardized functional assessments with their older patients at least once a year. These results suggest that implementing the STEADI tool kit in clinical settings could address knowledge gaps and provide the necessary tools to help

Using administrative data to track fall-related ambulatory care services in the Veterans Administration Healthcare system.

PubMed

Luther, Stephen L; French, Dustin D; Powell-Cope, Gail; Rubenstein, Laurence Z; Campbell, Robert

2005-10-01

The Veterans Administration (VA) Healthcare system, containing hospital and community-based outpatient clinics, provides the setting for the study. Summary data was obtained from the VA Ambulatory Events Database for fiscal years (FY) 1997-2001 and in-depth data for FY 2001. In FY 2001, the database included approximately 4 million unique patients with 60 million encounters. The purpose of this study was: 1) to quantify injuries and use of services associated with falls among the elderly treated in Veterans Administration (VA) ambulatory care settings using administrative data; 2) to compare fall-related services provided to elderly veterans with those provided to younger veterans. Retrospective analysis of administrative data. This study describes the trends (FY 1997-2001) and patterns of fall-related ambulatory care encounters (FY 2001) in the VA Healthcare System. An approximately four-fold increase in both encounters and patients seen was observed in FY 1997-2001, largely paralleling the growth of VA ambulatory care services. More than two-thirds of the patients treated were found to be over the age of 65. Veterans over the age of 65 were found to be more likely to receive care in the non-urgent setting and had higher numbers of co-morbid conditions than younger veterans. While nearly half of the encounters occurred in the Emergency/Urgent Care setting, fall-related injuries led to services across a wide spectrum of medical and surgical providers/departments. This study represents the first attempt to use the VA Ambulatory Events Database to study fall-related services provided to elderly veterans. In view of the aging population served by the VA and the movement to provide increased services in the outpatient setting, this database provides an important resource for researchers and administrators interested in the prevention and treatment of fall-related injuries.

Patient centered fall risk awareness perspectives: clinical correlates and fall risk

PubMed Central

Verghese, Joe

2016-01-01

Background While objective measures to assess risk of falls in older adults have been established; the value of patient self-reports in the context of falls is not known. Objectives To identify clinical correlates of patient centered fall risk awareness, and their validity for predicting falls. Design Prospective cohort study. Setting and Participants 316 non-demented and ambulatory community-dwelling older adults (mean age 78 years, 55% women). Measurements Fall risk awareness was assessed with a two-item questionnaire, which asked participants about overall likelihood and personal risk of falling over the next 12 months. Incident falls were recorded over study follow-up. Results Fifty-three participants (16.8%) responded positively to the first fall risk awareness question about being likely to have a fall in the next 12 months, and 100 (31.6%) reported being at personal risk of falling over the next 12 months. There was only fair correlation (kappa 0.370) between responses on the two questions. Prior falls and depressive symptoms were associated with positive responses on both fall risk awareness questions. Age and other established fall risk factors were not associated with responses on both fall risk awareness questions. The fall risk awareness questionnaire did not predict incident falls or injurious falls. Conclusion Fall risk awareness is low in older adults. While patient centered fall risk awareness is not predictive of falls, subjective risk perceptions should be considered when designing fall preventive strategies as they may influence participation and behaviors. PMID:27801936

Fall-related activity avoidance in relation to a history of falls or near falls, fear of falling and disease severity in people with Parkinson's disease.

PubMed

Kader, Manzur; Iwarsson, Susanne; Odin, Per; Nilsson, Maria H

2016-06-02

There is limited knowledge concerning fall-related activity avoidance in people with Parkinson's disease (PD); such knowledge would be of importance for the development of more efficient PD-care and rehabilitation. This study aimed to examine how fall-related activity avoidance relates to a history of self-reported falls/near falls and fear of falling (FOF) as well as to disease severity in people with PD. Data were collected from 251 (61 % men) participants with PD; their median (min-max) age and PD duration were 70 (45-93) and 8 (1-43) years, respectively. A self-administered postal survey preceded a home visit which included observations, clinical tests and interview-administered questionnaires. Fall-related activity avoidance was assessed using the modified Survey of Activities and Fear of Falling in the Elderly (mSAFFE) as well as by using a dichotomous (Yes/No) question. Further dichotomous questions concerned: the presence of FOF and the history (past 6 months) of falls or near falls, followed by stating the number of incidents. Disease severity was assessed according to the Hoehn and Yahr (HY) stages. In the total sample (n = 251), 41 % of the participants reported fall-related activity avoidance; the median mSAFFE score was 22. In relation to a history of fall, the proportions of participants (p < 0.001) that reported fall-related activity avoidance were: non-fallers (30 %), single fallers (50 %) and recurrent fallers, i.e. ≥ 2 falls (57 %). Among those that reported near falls (but no falls), 51 % (26 out of 51) reported fall-related activity avoidance. Of those that reported FOF, 70 % reported fall-related activity avoidance. Fall-related activity avoidance ranged from 24 % in the early PD-stage (HY I) to 74 % in the most severe stages (HY IV-V). Results indicate that fall-related activity avoidance may be related to a history of self-reported falls/near falls, FOF and disease severity in people with PD. Importantly, fall

Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients.

PubMed

Hou, Wen-Hsuan; Kang, Chun-Mei; Ho, Mu-Hsing; Kuo, Jessie Ming-Chuan; Chen, Hsiao-Lien; Chang, Wen-Yin

2017-03-01

To evaluate the accuracy of the inpatient fall risk screening tool and to identify the most critical fall risk factors in inpatients. Variations exist in several screening tools applied in acute care hospitals for examining risk factors for falls and identifying high-risk inpatients. Secondary data analysis. A subset of inpatient data for the period from June 2011-June 2014 was extracted from the nursing information system and adverse event reporting system of an 818-bed teaching medical centre in Taipei. Data were analysed using descriptive statistics, receiver operating characteristic curve analysis and logistic regression analysis. During the study period, 205 fallers and 37,232 nonfallers were identified. The results revealed that the inpatient fall risk screening tool (cut-off point of ≥3) had a low sensitivity level (60%), satisfactory specificity (87%), a positive predictive value of 2·0% and a negative predictive value of 99%. The receiver operating characteristic curve analysis revealed an area under the curve of 0·805 (sensitivity, 71·8%; specificity, 78%). To increase the sensitivity values, the Youden index suggests at least 1·5 points to be the most suitable cut-off point for the inpatient fall risk screening tool. Multivariate logistic regression analysis revealed a considerably increased fall risk in patients with impaired balance and impaired elimination. The fall risk factor was also significantly associated with days of hospital stay and with admission to surgical wards. The findings can raise awareness about the two most critical risk factors for falls among future clinical nurses and other healthcare professionals and thus facilitate the development of fall prevention interventions. This study highlights the needs for redefining the cut-off points of the inpatient fall risk screening tool to effectively identify inpatients at a high risk of falls. Furthermore, inpatients with impaired balance and impaired elimination should be closely

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.

Person-Centered Fall Risk Awareness Perspectives: Clinical Correlates and Fall Risk.

PubMed

Verghese, Joe

2016-12-01

To identify clinical correlates of person-centered fall risk awareness and their validity for predicting falls. Prospective cohort study. Community. Ambulatory community-dwelling older adults without dementia (N = 316; mean age 78, 55% female). Fall risk awareness was assessed using a two-item questionnaire that asked participants about overall likelihood of someone in their age group having a fall and their own personal risk of falling over the next 12 months. Incident falls were recorded over study follow-up. Fifty-three participants (16.8%) responded positively to the first fall risk awareness question about being likely to have a fall in the next 12 months, and 100 (31.6%) reported being at personal risk of falling over the next 12 months. There was only fair correlation (κ = 0.370) between responses on the two questions. Prior falls and depressive symptoms were associated with positive responses on both fall risk awareness questions. Age and other established fall risk factors were not associated with responses on either fall risk awareness question. The fall risk awareness questionnaire did not predict incident falls or injurious falls. Fall risk awareness is low in older adults. Although person-centered fall risk awareness is not predictive of falls, subjective risk perceptions should be considered when designing fall preventive strategies because they may influence participation and behaviors. © 2016, Copyright the Author Journal compilation © 2016, The American Geriatrics Society.

Using a Medical Intranet of Things System to Prevent Bed Falls in an Acute Care Hospital: A Pilot Study.

PubMed

Balaguera, Henri U; Wise, Diana; Ng, Chun Yin; Tso, Han-Wen; Chiang, Wan-Lin; Hutchinson, Aimee M; Galvin, Tracy; Hilborne, Lee; Hoffman, Cathy; Huang, Chi-Cheng; Wang, C Jason

2017-05-04

Hospitalized patients in the United States experience falls at a rate of 2.6 to 17.1 per 1000 patient-days, with the majority occurring when a patient is moving to, from, and around the bed. Each fall with injury costs an average of US $14,000. The aim was to conduct a technology evaluation, including feasibility, usability, and user experience, of a medical sensor-based Intranet of things (IoT) system in facilitating nursing response to bed exits in an acute care hospital. Patients 18 years and older with a Morse fall score of 45 or greater were recruited from a 35-bed medical-surgical ward in a 317-bed Massachusetts teaching hospital. Eligible patients were recruited between August 4, 2015 and July 31, 2016. Participants received a sensor pad placed between the top of their mattress and bed sheet. The sensor pad was positioned to monitor movement from patients' shoulders to their thighs. The SensableCare System was evaluated for monitoring patient movement and delivering timely alerts to nursing staff via mobile devices when there appeared to be a bed-exit attempt. Sensor pad data were collected automatically from the system. The primary outcomes included number of falls, time to turn off bed-exit alerts, and the number of attempted bed-exit events. Data on patient falls were collected by clinical research assistants and confirmed with the unit nurse manager. Explanatory variables included room locations (zones 1-3), day of the week, nursing shift, and Morse Fall Scale (ie, positive fall history, positive secondary diagnosis, positive ambulatory aid, weak impaired gait/transfer, positive IV/saline lock, mentally forgets limitations). We also assessed user experience via nurse focus groups. Qualitative data regarding staff interactions with the system were collected during two focus groups with 25 total nurses, each lasting approximately 1.5 hours. A total of 91 patients used the system for 234.0 patient-days and experienced no bed falls during the study period

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.

148. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

148. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER DAM; HEADGATES AT INLET, SOUTHWEST VIEW. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

98. SHOESTRING, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY NORTHWEST ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

98. SHOESTRING, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY NORTHWEST OF MURTAUGH, IDAHO; PROFILE VIEW, SOUTH. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

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.

147. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

147. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER, IDAHO; VIEW OF MAIN HEADGATES, EAST VIEW. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

97. POINT SPILL, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

97. POINT SPILL, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY NORTHWEST OF MURTAUGH, IDAHO; OVERALL WEST VIEW FROM CANAL SIDE. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

149. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

149. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER DAM; CLOSE-UP OF MAIN CANAL GATES, SOUTH VIEW. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

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.

Fall risk: the clinical relevance of falls and how to integrate fall risk with fracture risk.

PubMed

Peeters, G; van Schoor, Natasja M; Lips, Paul

2009-12-01

In old age, 5-10% percent of all falls result in a fracture, and up to 90% of all fractures result from a fall. This article describes the link between fall risk and fracture risk in community-dwelling older persons. Which factors attribute to both the fall risk and the fracture risk? Which falls result in a fracture? Which tools are available to predict falls and fractures? Directions for the use of prediction tools in clinical practice are given. Challenges for future research include further validation of existing prediction tools and evaluation of the cost-effectiveness of treatment after screening.

Falls and Fear of Falling After Stroke: A Case-Control Study.

PubMed

Goh, Hui-Ting; Nadarajah, Mohanasuntharaam; Hamzah, Norhamizan Binti; Varadan, Parimalaganthi; Tan, Maw Pin

2016-12-01

Falls are common after stroke, with potentially serious consequences. Few investigations have included age-matched control participants to directly compare fall characteristics between older adults with and without stroke. Further, fear of falling, a significant psychological consequence of falls, has only been examined to a limited degree as a risk factor for future falls in a stroke population. To compare the fall history between older adults with and without a previous stroke and to identify the determinants of falls and fear of falling in older stroke survivors. Case-control observational study. Primary teaching hospital. Seventy-five patients with stroke (mean age ± standard deviation, 66 ± 7 years) and 50 age-matched control participants with no previous stroke were tested. Fall history, fear of falling, and physical, cognitive, and psychological function were assessed. A χ 2 test was performed to compare characteristics between groups, and logistic regression was performed to determine the risk factors for falls and fear of falling. Fall events in the past 12 months, Fall Efficacy Scale-International, Berg Balance Scale, Functional Ambulation Category, Fatigue Severity Scale, Montreal Cognitive Assessment, and Patient Healthy Questionnaire-9 were measured for all participants. Fugl-Meyer Motor Assessment was used to quantify severity of stroke motor impairments. Twenty-three patients and 13 control participants reported at least one fall in the past 12 months (P = .58). Nine participants with stroke had recurrent falls (≥2 falls) compared with none of the control participants (P < .01). Participants with stroke reported greater concern for falling than did nonstroke control participants (P < .01). Female gender was associated with falls in the nonstroke group, whereas falls in the stroke group were not significantly associated with any measured outcomes. Fear of falling in the stroke group was associated with functional ambulation level and balance

141. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

141. TWIN FALLS MAIN CANAL DIVERSION, TWIN FALLS COUNTY, MILNER, IDAHO; CLOSE-UP OF MAIN HEADGATES, RADIAL GATES INSIDE, SOUTHEAST VIEW. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

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.

Older people and falls: health status, quality of life, lifestyle, care networks, prevention and views on service use following a recent fall.

PubMed

Roe, Brenda; Howell, Fiona; Riniotis, Konstantinos; Beech, Roger; Crome, Peter; Ong, Bie Nio

2009-08-01

This study has investigated older people's experiences of a recent fall, its impact on their health, lifestyle, quality of life, care networks, prevention and their views on service use. Falls are common in older people and prevalence increases with age. Falls prevention is a major policy and service initiative. An exploratory, qualitative design involving two time points. A convenience sample of 27 older people from two primary care trusts who had a recent fall. Taped semi structured qualitative interviews were conducted and repeated at follow up to detect change over time and repeat falls. Data were collected on their experience of falls, health, activities of living, lifestyle, quality of life, use of services, prevention of falls, informal care and social networks. Content analysis of transcribed interviews identified key themes. The majority of people fell indoors (n = 23), were repeat fallers (n = 22) with more than half alone when they fell (n = 15). For five people it was their first ever fall. Participants in primary care trust 1 had a higher mean age than those in primary care trust 2 and had more injurious falls (n = 12, mean age 87 years vs. n = 15, mean age 81 years). The majority of non-injurious falls went unreported to formal services. Falls can result in a decline in health status, ability to undertake activities of living, lifestyle and quality of life. Local informal care and support networks are as important as formal care for older people at risk of falls or who have fallen. Access to falls prevention programmes and services is limited for people living in more rural communities. Falls prevention initiatives and services should work with local communities, agencies and informal carers to ensure equitable access and provision of information, resources and care to meet the needs of older people at risk or who have fallen.

Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study.

PubMed

Taylor-Piliae, Ruth E; Mohler, M Jane; Najafi, Bijan; Coull, Bruce M

2016-12-01

Stroke survivors often have persistent neural deficits related to motor function and sensation, which increase their risk of falling, most of which occurs at home or in community settings. The use of wearable technology to monitor fall risk and gait in stroke survivors may prove useful in enhancing recovery and/or preventing injuries. Determine the feasibility of using wearable technology (PAMSys™) to objectively monitor fall risk and gait in home and community settings in stroke survivors. In this feasibility study, we used the PAMSys to identify fall risk indicators (postural transitions: duration in seconds, and number of unsuccessful attempts), and gait (steps, speed, duration) for 48 hours during usual daily activities in stroke survivors (n = 10) compared to age-matched controls (n = 10). A questionnaire assessed device acceptability. Stroke survivors mean age was 70 ± 8 years old, were mainly Caucasian (60%) women (70%), and not significantly different than the age-matched controls (all P-values >0.20). Stroke survivors (100%) reported that the device was comfortable to wear, didn't interfere with everyday activities, and were willing to wear it for another 48 hours. None reported any difficulty with the device while sleeping, removing/putting back on for showering or changing clothes. When compared to controls, stroke survivors had significantly worse fall risk indicators and walked less (P < 0.05). Stroke survivors reported high acceptability of 48 hours of continuous PAMSys monitoring. The use of in-home wearable technology may prove useful in monitoring fall risk and gait in stroke survivors, potentially enhancing recovery.

Falling and fall risk in adult patients with severe haemophilia.

PubMed

Rehm, Hanna; Schmolders, Jan; Koob, Sebastian; Bornemann, Rahel; Goldmann, Georg; Oldenburg, Johannes; Pennekamp, Peter; Strauss, Andreas C

2017-05-10

The objective of this study was to define fall rates and to identify possible fall risk factors in adult patients with severe haemophilia. 147 patients with severe haemophilia A and B were evaluated using a standardized test battery consisting of demographic, medical and clinical variables and fall evaluation. 41 (27.9 %) patients reported a fall in the past 12 months, 22 (53.7 %) of them more than once. Young age, subjective gait insecurity and a higher number of artificial joints seem to be risk factors for falling. Falls seem to be a common phenomenon in patients with severe haemophilia. Fall risk screening and fall prevention should be implemented into daily practice.

99. POINT SPILL, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

99. POINT SPILL, TWIN FALLS MAIN CANAL, TWIN FALLS COUNTY NORTHWEST OF MURTAUGH, IDAHO; CLOSE-UP OF OUTLET SIDE OF GATES, SOUTH VIEW. - Milner Dam & Main Canal: Twin Falls Canal Company, On Snake River, 11 miles West of city of Burley, Idaho, Twin Falls, Twin Falls County, ID

Effect of square stepping exercise for older adults to prevent fall and injury related to fall: systematic review and meta-analysis of current evidences.

PubMed

Fisseha, Berihu; Janakiraman, Balamurugan; Yitayeh, Asmare; Ravichandran, Hariharasudhan

2017-02-01

Falls and fall related injuries become an emerging health problem among older adults. As a result a review of the recent evidences is needed to design a prevention strategy. The aim of this review was to determine the effect of square stepping exercise (SSE) for fall down injury among older adults compared with walking training or other exercises. An electronic database search for relevant randomized control trials published in English from 2005 to 2016 was conducted. Articles with outcome measures of functional reach, perceived health status, fear of fall were included. Quality of the included articles was rated using Physiotherapy Evidence Database (PEDro) scale and the pooled effect of SSE was obtained by Review Manager (RevMan5) software. Significant effect of SSE was detected over walking or no treatment to improve balance as well to prevent fear of fall and improve perceived health status. The results of this systematic review proposed that SSE significantly better than walking or no treatment to prevent fall, prevent fear of fall and improve perceived health status.

Older people's experience of falls: understanding, interpretation and autonomy.

PubMed

Roe, Brenda; Howell, Fiona; Riniotis, Konstantinos; Beech, Roger; Crome, Peter; Ong, Bie Nio

2008-09-01

This paper is a report of a study to explore the experiences of older people who suffered a recent fall and identify possible factors that could contribute to service development. Falls in older people are prevalent and are associated with morbidity, hospitalization and mortality, personal costs to individuals and financial costs to health services. A convenience sample of 27 older people (mean age 84 years; range 65-98) participated in semi-structured taped interviews. Follow-up interviews during 2003-2004 were undertaken to detect changes over time. Data were collected about experience of the fall, use of services, health and well-being, activities of daily living, informal care, support networks and prevention. Thematic content analysis was undertaken. Twenty-seven initial interviews and 18 follow-up interviews were conducted. The majority of people fell indoors (n = 23) and were alone (n = 15). The majority of falls were repeat falls (n = 22) and five were a first-ever fall. People who reflected on their fall and sought to understand why and how it occurred developed strategies to prevent future falls, face their fear, maintain control and choice and continue with activities of daily living. Those who did not reflect on their fall and did not know why it occurred restricted their activities and environments and remained in fear of falling. Assisting people to reflect on their falls and to understand why they happened could help with preventing future falls, allay fear, boost confidence and aid rehabilitation relating to their activities of daily living.

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.

Visual risk factors for falls in older people.

PubMed

Lord, Stephen R

2006-09-01

Poor vision reduces postural stability and significantly increases the risk of falls and fractures in older people. Most studies have found that poor visual acuity increases the risk of falls. However, studies that have included multiple visual measures have found that reduced contrast sensitivity and depth perception are the most important visual risk factors for falls. Multifocal glasses may add to this risk because their near-vision lenses impair distance contrast sensitivity and depth perception in the lower visual field. This reduces the ability of an older person to detect environmental hazards. There is now evidence that maximising vision through cataract surgery is an effective strategy for preventing falls. Further randomised controlled trials are required to determine whether individual strategies (such as restriction of use of multifocal glasses) or multi-strategy visual improvement interventions can significantly reduce falls in older people. Public health initiatives are required to raise awareness in older people and their carers of the importance of regular eye examinations and use of appropriate prescription glasses.

78 FR 59725 - Construction Fall Protection Systems Criteria and Practices, and Training Requirements; Extension...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-27

...- 1648. Mail, hand delivery, express mail, messenger, or courier service: When using this method, you... DEPARTMENT OF LABOR Occupational Safety and Health Administration [Docket No. OSHA-2010-0008] Construction Fall Protection Systems Criteria and Practices, and Training Requirements; Extension of the Office...

Preoperative Falls Predict Postoperative Falls, Functional Decline, and Surgical Complications.

PubMed

Kronzer, Vanessa L; Jerry, Michelle R; Ben Abdallah, Arbi; Wildes, Troy S; Stark, Susan L; McKinnon, Sherry L; Helsten, Daniel L; Sharma, Anshuman; Avidan, Michael S

2016-10-01

Falls are common and linked to morbidity. Our objectives were to characterize postoperative falls, and determine whether preoperative falls independently predicted postoperative falls (primary outcome), functional dependence, quality of life, complications, and readmission. This prospective cohort study included 7982 unselected patients undergoing elective surgery. Data were collected from the medical record, a baseline survey, and follow-up surveys approximately 30days and one year after surgery. Fall rates (per 100 person-years) peaked at 175 (hospitalization), declined to 140 (30-day survey), and then to 97 (one-year survey). After controlling for confounders, a history of one, two, and ≥three preoperative falls predicted postoperative falls at 30days (adjusted odds ratios [aOR] 2.3, 3.6, 5.5) and one year (aOR 2.3, 3.4, 6.9). One, two, and ≥three falls predicted functional decline at 30days (aOR 1.2, 2.4, 2.4) and one year (aOR 1.3, 1.5, 3.2), along with in-hospital complications (aOR 1.2, 1.3, 2.0). Fall history predicted adverse outcomes better than commonly-used metrics, but did not predict quality of life deterioration or readmission. Falls are common after surgery, and preoperative falls herald postoperative falls and other adverse outcomes. A history of preoperative falls should be routinely ascertained. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The Effect of Personalization on Smartphone-Based Fall Detectors

PubMed Central

Medrano, Carlos; Plaza, Inmaculada; Igual, Raúl; Sánchez, Ángel; Castro, Manuel

2016-01-01

The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training. PMID:26797614

Reliability, Validity, and Ability to Identify Fall Status of the Balance Evaluation Systems Test, Mini-Balance Evaluation Systems Test, and Brief-Balance Evaluation Systems Test in Older People Living in the Community.

PubMed

Marques, Alda; Almeida, Sara; Carvalho, Joana; Cruz, Joana; Oliveira, Ana; Jácome, Cristina

2016-12-01

To assess the reliability, validity, and ability to identify fall status of the Balance Evaluation Systems Test (BESTest), Mini-BESTest, and Brief-BESTest, compared with the Berg Balance Scale (BBS), in older people living in the community. Cross-sectional. Community centers. Older adults (N=122; mean age ± SD, 76±9y). Not applicable. Participants reported on falls history in the preceding year and completed the Activities-Specific Balance Confidence (ABC) Scale. The BBS, BESTest, and the Five Times Sit-To-Stand Test were administered. Interrater (2 physiotherapists) and test-retest relative (48-72h) and absolute reliabilities were explored with the intraclass correlation coefficient (ICC) equation (2,1) and the Bland and Altman method. Minimal detectable changes at the 95% confidence level (MDC 95 ) were established. Validity was assessed by correlating the balance tests with each other and with the ABC Scale (Spearman correlation coefficients-ρ). Receiver operating characteristics assessed the ability of each balance test to differentiate between people with and without a history of falls. All balance tests presented good to excellent interrater (ICC=.71-.93) and test-retest (ICC=.50-.82) relative reliability, with no evidence of bias. MDC 95 values were 4.6, 9, 3.8, and 4.1 points for the BBS, BESTest, Mini-BESTest, and Brief-BESTest, respectively. All tests were significantly correlated with each other (ρ=.83-.96) and with the ABC Scale (ρ=.46-.61). Acceptable ability to identify fall status (areas under the curve, .71-.78) was found for all tests. Cutoff points were 48.5, 82, 19.5, and 12.5 points for the BBS, BESTest, Mini-BESTest, and Brief-BESTest, respectively. All balance tests are reliable, valid, and able to identify fall status in older people living in the community. Therefore, the choice of which test to use will depend on the level of balance impairment, purpose, and time availability. Copyright © 2016. Published by Elsevier Inc.

Fear of falling as seen in the Multidisciplinary falls consultation.

PubMed

Gaxatte, C; Nguyen, T; Chourabi, F; Salleron, J; Pardessus, V; Delabrière, I; Thévenon, A; Puisieux, F

2011-06-01

Fear of falling may be as debilitating as the fall itself, leading to a restriction in activities and even a loss of autonomy. The main objective was to evaluate the prevalence of the fear of falling among elderly fallers. The secondary objectives were to determine the factors associated with the fear of falling and evaluate the impact of this fear on the activity "getting out of the house". Prospective study conducted between 1995 and 2006 in which fallers and patients at high risk for falling were seen at baseline by the multidisciplinary falls consultation team (including a geriatrician, a neurologist and a physical medicine and rehabilitation physician) and then, again 6 month later, by the same geriatrician. The fear of falling was evaluated with a yes/no question: "are you afraid of falling?". Out of 635 patients with a mean age of 80.6 years, 502 patients (78%) expressed a fear of falling. Patients with fear of falling were not older than those who did not report this fear, but the former were mostly women (P<0,001), who experienced more falls in the 6 months preceding the consultation (P=0.01), reported more frequently a long period of time spent on the floor after a fall (P<0.001), had more balance disorders (P=0.002) and finally, were using more frequently a walking technical aid (P=0.02). Patients with fear of falling were not going out alone as much as the fearless group (31% vs 53%, P<0.0001). Eighty-two percent of patients in the fearful group admitted to avoiding going out because they were afraid of falling. The strong prevalence of the fear of falling observed in this population and its consequences in terms of restricted activities justifies systematically screening for it in fallers or patients at risk for falling. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Fall prevention in central coast community pharmacies.

PubMed

Stuart, Gina M; Kale, Helen L

2018-04-19

Fall injuries among people aged 65 years and over (older people) cause substantial health decline and cost to the health system. In 2009 in New South Wales, 25.6% of older people fell in the previous year, and 10.7% (32 000) were hospitalised. Pharmacists are trusted professionals, who interact extensively with older people and have potential to augment fall prevention in pharmacies. This brief report describes how professional development improved pharmacist's knowledge and confidence in fall prevention, encouraged implementation of fall prevention plans and facilitated the provision of brief fall prevention interventions for older clients, after identification of fall risk. In 2014, pharmacists from all Central Coast pharmacies (n = 76) were invited to free, continuing professional development (CPD) in fall prevention. It provided education and resources to identify clients' fall risk, conduct brief fall prevention interventions and implement fall prevention health promotion plans (FPHPP). Pharmacists completed written: Baseline and post-workshop questionnaires to assess changes in pharmacist's knowledge and confidence, and existing fall prevention in pharmacies. Logs of client fall risk and brief fall prevention interventions offered to clients. Four-month follow-up questionnaires to assess implementation of FPHPPs and pharmacy practice changes. Pharmacists representing 36% of pharmacies participated. At four-month follow-up, 67% had implemented FPHPPs, and 62% delivered brief interventions determined by client fall risk. Fall prevention in pharmacies can be augmented through locally provided CPD tailored for pharmacists. SO WHAT?: This model could increase fall prevention reach. It is transferable to settings where health professionals provide services to older adults and require reregistration through professional development. © 2018 Australian Health Promotion Association.

Inpatient falls in older adults: a cohort study of antihypertensive prescribing pre- and post-fall.

PubMed

Omer, H M R B; Hodson, J; Pontefract, S K; Martin, U

2018-02-23

Falls are common during hospital admissions and may occur more frequently in patients who are taking antihypertensive medications, particularly in the context of normal to low blood pressure. The review and adjustment of these medications is an essential aspect of the post-fall assessment and should take place as soon as possible after the fall. Our aim was to investigate whether appropriate post-fall adjustments of antihypertensive medications are routinely made in a large National Health Service (NHS) Trust. Inpatient records over an eight-month period were captured from an electronic prescribing system to identify older adults (≥80 years old) with normal/low blood pressures (< 140 mmHg systolic) who had a documented inpatient fall as these patients were considered to be at high risk of further falls. Prescribed antihypertensive medication on admission was then compared with the post-fall (within 24 h after the fall) and discharge prescriptions. A total of 146 patients were included in the analysis. Of those, 120 patients (82%) were taking the same number of antihypertensive medications in the 24 h after the fall as they were before; only 19 patients (13%) had a reduction in the number of medications and seven patients (5%) had an increase in medications during that period. Only 9% of the antihypertensive classes assessed were either stopped or reduced in dose immediately post-fall. In addition, 11 new antihypertensives were prescribed at this time. At discharge, half of the patients (n = 73) remained on the same number of antihypertensive medication as on admission, 51 patients (35%) were on fewer antihypertensives and 22 (15%) were on more. Additionally, no changes were made to individual antihypertensives in 49% of prescriptions; 34% were stopped or reduced in dose but 38 new agents were started by the time of discharge. Angiotensin converting enzyme inhibitors and angiotensin II receptor blockers (ACEi/ARB) were the class of medications most

Assessment of an interactive voice response system for identifying falls in a statewide sample of older adults.

PubMed

Albert, Steven M; King, Jennifer; Keene, Robert M

2015-02-01

Interactive voice response (IVR) systems offer great advantages for data collection in large, geographically dispersed samples involving frequent contact. We assessed the quality of IVR data collected from older respondents participating in a statewide falls prevention program evaluation in Pennsylvania in 2010-12. Participants (n=1834) were followed up monthly for up to 10 months to compare respondents who completed all, some, or no assessments in the IVR system. Validity was assessed by examining IVR-reported falls incidence relative to baseline in-person self-report and performance assessment of balance. While a third of the sample switched from IVR to in-person calls over follow-up, IVR interviews were successfully used to complete 68.1% of completed monthly assessments (10,511/15,430). Switching to in-person interviews was not associated with measures of participant function or cognition. Both self-reported (p<.0001) and performance assessment of balance (p=.05) at baseline were related to falls incidence. IVR is a productive modality for falls research among older adults. Future research should establish what level of initial personal research contact is optimal for boosting IVR completion rates and what research domains are most appropriate for this kind of contact. Copyright © 2014 Elsevier Inc. All rights reserved.

Incidence and characteristics of accidental falls in hospitalizations

PubMed Central

Kobayashi, Kazuyoshi; Imagama, Shiro; Inagaki, Yuko; Suzuki, Yusuke; Ando, Kei; Nishida, Yoshihiro; Nagao, Yoshimasa; Ishiguro, Naoki

2017-01-01

ABSTRACT Aging of the patient population has led to increased occurrence of accidental falls in acute care settings. The aim of this study is to survey the annual occurrence of falls in a university hospital, and to examine procedures to prevent fall. A total of 49,059 inpatients were admitted to our hospital from April 2015 to March 2016. A fall assessment scale was developed to estimate the risk of fall at admission. Data on falls were obtained from the hospital incident reporting system. There were fall-related incidents in 826 patients (1.7%). Most falls occurred in hospital rooms (67%). Adverse events occurred in 101 patients who fell (12%) and were significantly more frequent in patients aged ≥80 years old and in those wearing slippers. The incidence of falls was also significantly higher in patients in the highest risk group. These results support the validity of the risk assessment scale for predicting accidental falls in an acute treatment setting. The findings also clarify the demographic and environmental factors and consequences associated with fall. These results of the study could provide important information for designing effective interventions to prevent fall in elderly patients. PMID:28878434

Falls efficacy, postural balance, and risk for falls in older adults with falls-related emergency department visits: prospective cohort study.

PubMed

Pua, Yong-Hao; Ong, Peck-Hoon; Clark, Ross Allan; Matcher, David B; Lim, Edwin Choon-Wyn

2017-12-21

Risk for falls in older adults has been associated with falls efficacy (self-perceived confidence in performing daily physical activities) and postural balance, but available evidence is limited and mixed. We examined the interaction between falls efficacy and postural balance and its association with future falls. We also investigated the association between falls efficacy and gait decline. Falls efficacy, measured by the Modified Falls Efficacy Scale (MFES), and standing postural balance, measured using computerized posturography on a balance board, were obtained from 247 older adults with a falls-related emergency department visit. Six-month prospective fall rate and habitual gait speed at 6 months post baseline assessment were also measured. In multivariable proportional odds analyses adjusted for potential confounders, falls efficacy modified the association between postural balance and fall risk (interaction P = 0.014): increasing falls efficacy accentuated the increased fall risk related to poor postural balance. Low baseline falls efficacy was strongly predictive of worse gait speed (0.11 m/s [0.06 to 0.16] slower gait speed per IQR decrease in MFES; P < 0.001). Older adults with high falls efficacy but poor postural balance were at greater risk for falls than those with low falls efficacy; however, low baseline falls efficacy was strongly associated with worse gait function at follow-up. Further research into these subgroups of older adults is warranted. ClinicalTrials.gov identifier: NCT01713543 .

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

Occupational ladder fall injuries - United States, 2011.

PubMed

Socias, Christina M; Chaumont Menéndez, Cammie K; Collins, James W; Simeonov, Peter

2014-04-25

Falls remain a leading cause of unintentional injury mortality nationwide [corrected].Among workers, approximately 20% of fall injuries involve ladders. Among construction workers, an estimated 81% of fall injuries treated in U.S. emergency departments (EDs) involve a ladder. To fully characterize fatal and nonfatal injuries associated with ladder falls among workers in the United States, CDC's National Institute for Occupational Safety and Health (NIOSH) analyzed data across multiple surveillance systems: 1) the Census of Fatal Occupational Injuries (CFOI), 2) the Survey of Occupational Injuries and Illnesses (SOII), and 3) the National Electronic Injury Surveillance System-occupational supplement (NEISS-Work). In 2011, work-related ladder fall injuries (LFIs) resulted in 113 fatalities (0.09 per 100,000 full-time equivalent [FTE] workers), an estimated 15,460 nonfatal injuries reported by employers that involved ≥1 days away from work (DAFW), and an estimated 34,000 nonfatal injuries treated in EDs. Rates for nonfatal, work-related, ED-treated LFIs were higher (2.6 per 10,000 FTE) than those for such injuries reported by employers (1.2 per 10,000 FTE). LFIs represent a substantial public health burden of preventable injuries for workers. Because falls are the leading cause of work-related injuries and deaths in construction, NIOSH, the Occupational Safety and Health Administration, and the Center for Construction Research and Training are promoting a national campaign to prevent workplace falls. NIOSH is also developing innovative technologies to complement safe ladder use.

[Evaluation of a simple screening tool for ambulant fall prevention].

PubMed

Knobe, M; Rasche, P; Rentemeister, L; Bliemel, C; Bücking, B; Bollheimer, L C; Pape, H-C

2018-02-02

An individual's risk of falling is generally difficult to detect and it is likely to be underestimated. Thus, preventive measures are challenging and they demand sufficient integration and implementation into aftercare and outpatient management. The Aachen Falls Prevention Scale (AFPS) is a quick and easy tool for patient-driven fall risk assessment. Older adults' risk of falling is identified in a suitable manner and they then have the opportunity to independently assess and monitor their risk of falling. The aim of the current study was to evaluate the AFPS as a simple screening tool in geriatric trauma patients via the identification of influencing factors, e.g. objective or subjective fall risk, fear of falling (FOF) and demographic data. In this context, we investigated older adults' willingness to take part in special activities concerning fall prevention. Retrospectively, all patients over 70 years of age who received in-hospital fracture treatment between July 2014 and April 2016 were analyzed at a level I trauma center. After identification of 884 patients, participants completed a short questionnaire (47 questions, yes/no, Likert scale) comprising the AFPS. A history of falls in the past year was considered an indicator of a balance disorder. In addition, ambulant patients were invited to participate between July and August 2016. In total, 201 patients (mean 80.4 years, range 63-97 years) performed a self-assessment based on the AFPS. After steps 1 and 2 of the AFPS had been completed, 95 (47%) participants rated their subjective risk of falling as high (more than 5 points). Of the participants 84 (42%) were objectively classified as "fallers" with significant effects on their AFPS evaluation and rating of their subjective risk of falling. Furthermore, 67% of the participants identified a general practitioner as their main contact person, and 43% of the respondents viewed the AFPS as a beneficial screening tool in fall risk evaluation (8

Falls in People with Multiple Sclerosis Compared with Falls in Healthy Controls

PubMed Central

Mazumder, Rajarshi; Murchison, Charles; Bourdette, Dennis; Cameron, Michelle

2014-01-01

Objective To compare the risk, circumstances, consequences and causes of prospectively recorded falls between people with multiple sclerosis (PwMS) and healthy controls of similar age and gender. Methods 58 PwMS and 58 healthy controls, who are community-dwelling, were recruited in this 6-month prospective cohort study. 90% of PwMS and 84% of healthy controls completed the study. Participants counted falls prospectively using fall calendars and noted fall location, fall-related injuries, and the cause of the falls. Kaplan Meier survival analysis and log-rank tests were performed to compare the distributions of survival without falling between PwMS and healthy controls. Results 40.8% of controls and 71.2% of PwMS fell at least once. 48.1% of PwMS and 18.4% of healthy controls fell at least twice. 42.3% of PwMS and 20.4% of health controls sustained a fall-related injury. After adjusting for age and gender, the time to first fall (HR: 1.87, p = 0.033) and the time to recurrent falls (HR: 2.87, p = 0.0082) were significantly different between PwMS and healthy controls. PwMS reported an almost equal number of falls inside and outside, 86% of the falls in healthy controls were outside. Healthy controls were more likely to fall due to slipping on a slippery surface (39.5% vs 10.4%). PwMS more often attributed falls to distraction (31% vs 7%) and uniquely attributed falls to fatigue or heat. Conclusions Fall risk, circumstances, consequences, and causes are different for PwMS than for healthy people of the same age and gender. PwMS fall more, are more likely to be injured by a fall, and often fall indoors. PwMS, but not healthy controls, frequently fall because they are distracted, fatigued or hot. PMID:25254633

Falls prevention for the elderly.

PubMed

Balzer, Katrin; Bremer, Martina; Schramm, Susanne; Lühmann, Dagmar; Raspe, Heiner

2012-01-01

results must be rated as compromised due to different biasing factors. In summary, it appears that the performance of tests or the application of parameters to identify individuals at risk of falling yields little or no clinically relevant information. Positive effects of exercise interventions may be expected in relatively young and healthy seniors, while studies indicate opposite effects in the fragile elderly. For this specific vulnerable population the modification of the housing environment shows protective effects. A low number of studies, low quality of studies or inconsistent results lead to the conclusion that the effectiveness of the following interventions has to be rated unclear yet: correction of vision disorders, modification of psychotropic medication, vitamin D supplementation, nutritional supplements, psychological interventions, education of nursing personnel, multiple and multifactorial programs as well as the application of hip protectors. For the context of the German health care system the economic evaluations of fall prevention retrieved by the literature searches yield very few useful results. Cost-effectiveness calculations of fall prevention are mostly based on weak effectiveness data as well as on epidemiological and cost data from foreign health care systems. Ethical analysis demonstrates ambivalent views of the target population concerning fall risk and the necessity of fall prevention. The willingness to take up preventive measures depends on a variety of personal factors, the quality of information, guidance and decision-making, the prevention program itself and social support. THE ANALYSIS OF PAPERS REGARDING LEGAL ISSUES SHOWS THREE MAIN CHALLENGES: the uncertainty of which standard of care has to be expected with regard to fall prevention, the necessity to consider the specific conditions of every single case when measures for fall prevention are applied, and the difficulty to balance the rights to autonomous decision making and physical

Nucleic acid detection system and method for detecting influenza

DOEpatents

Cai, Hong; Song, Jian

2015-03-17

The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

A Piece of Paper Falling Faster than Free Fall

ERIC Educational Resources Information Center

Vera, F.; Rivera, R.

2011-01-01

We report a simple experiment that clearly demonstrates a common error in the explanation of the classic experiment where a small piece of paper is put over a book and the system is let fall. This classic demonstration is used in introductory physics courses to show that after eliminating the friction force with the air, the piece of paper falls…

Designing and evaluating an electronic patient falls reporting system: perspectives for the implementation of health information technology in long-term residential care facilities.

PubMed

Mei, Yi You; Marquard, Jenna; Jacelon, Cynthia; DeFeo, Audrey L

2013-11-01

Patient falls are the leading cause of unintentional injury and death among older adults. In 2000, falls resulted in over 10,300 elderly deaths, costing the United States approximately $179 million in incidence and medical costs. Furthermore, non-fatal injuries caused by falls cost the United States $19 billion annually. Health information technology (IT) applications, specifically electronic falls reporting systems, can aid quality improvement efforts to prevent patient falls. Yet, long-term residential care facilities (LTRCFs) often do not have the financial resources to implement health IT, and workers in these settings are often not ready to adopt such systems. Additionally, most health IT evaluations are conducted in large acute-care settings, so LTRCF administrators currently lack evidence to support the value of health IT. In this paper, we detail the development of a novel, easy-to-use system to facilitate electronic patient falls reporting within a LTRCF using off-the-shelf technology that can be inexpensively implemented in a wide variety of settings. We report the results of four complimentary system evaluation measures that take into consideration varied organizational stakeholders' perspectives: (1) System-level benefits and costs, (2) system usability, via scenario-based use cases, (3) a holistic assessment of users' physical, cognitive, and marcoergonomic (work system) challenges in using the system, and (4) user technology acceptance. We report the viability of collecting and analyzing data specific to each evaluation measure and detail the relative merits of each measure in judging whether the system is acceptable to each stakeholder. The electronic falls reporting system was successfully implemented, with 100% reporting at 3-months post-implementation. The system-level benefits and costs approach showed that the electronic system required no initial investment costs aside from personnel costs and significant benefits accrued from user time savings

Prevalence of fall injuries and risk factors for fall among hospitalized children in a specialized childrens hospital in Saudi Arabia.

PubMed

AlSowailmi, Banan Abdullah; AlAkeely, Maha Heshaam; AlJutaily, Hayat Ibrahim; Alhasoon, Mohammad Abdulaziz; Omair, Amir; AlKhalaf, Hamad Abdullah

2018-01-01

Fall injuries among children during hospital stay is a major patient safety issue. Inpatient pediatric falls can lead to numerous negative consequences. In contrast to adults, there is a paucity of information on the prevalence and risk factors associated with children's falls during hospitalization. Identify the prevalence of fall injuries among hospital.ized children and describe the demographic and environmental factors that could predict a higher risk of severe outcomes of fall. Descriptive, cross-sectional prevalence study. Specialized children's hospital. Data was obtained through the electronic Safety Reporting System (SRS). All reported fall events during hospitalization in children less than or equal 14 years of age for the period from 1 April 2015 to 30 April 2016 were included. Fall events that occurred in the day care unit and the outpatient clinic were excluded. Prevalence and possible risk factors for fall events. 48. The prevalence of falls among the 4860 admitted children was 9.9 (95% CI=7.5, 13.1) per 1000 patients (48/4860). A majority of the falls were among boys (n=26, 54%), in the age group from 1-5 years old (n=22, 46%), in children at high risk of falling (n=35, 73%), with normal mobility status (n=21, 44%), and with no history of previ.ous falls (n=33, 69%). Severe injuries accounted for 25% of falls (n=12). However, falls among the moderate risk category (n=9, 69%) were more often severe than falls among the high risk category of children (n=12, 34%) (P=.03). Risk factor identification is required to prevent falls and their severe outcomes. Underreporting and single-centered study. None.

Increasing fall risk awareness using wearables: A fall risk awareness protocol.

PubMed

Danielsen, Asbjørn; Olofsen, Hans; Bremdal, Bernt Arild

2016-10-01

Each year about a third of elderly aged 65 or older experience a fall. Many of these falls may have been avoided if fall risk assessment and prevention tools where available in a daily living situation. We identify what kind of information is relevant for doing fall risk assessment and prevention using wearable sensors in a daily living environment by investigating current research, distinguishing between prospective and context-aware fall risk assessment and prevention. Based on our findings, we propose a fall risk awareness protocol as a fall prevention tool integrating both wearables and ambient sensing technology into a single platform. Copyright © 2016. Published by Elsevier Inc.

Portable modular detection system

DOEpatents

Brennan, James S [Rodeo, CA; Singh, Anup [Danville, CA; Throckmorton, Daniel J [Tracy, CA; Stamps, James F [Livermore, CA

2009-10-13

Disclosed herein are portable and modular detection devices and systems for detecting electromagnetic radiation, such as fluorescence, from an analyte which comprises at least one optical element removably attached to at least one alignment rail. Also disclosed are modular detection devices and systems having an integrated lock-in amplifier and spatial filter and assay methods using the portable and modular detection devices.

[Accidental falls in the elderly].

PubMed

Heinimann, Niklas B; Kressig, Reto W

2014-06-18

Falls in the elderly are common with consecutive high mortality and morbidity. Recent consecutive data focus on identification and therapy of intrinsic risk factors. Sarcopenia, imbalance and gait disorders represent the major risk factors. Sarcopenia is caused by a disequilibrium of protein synthesis and breakdown, probably in consequence of age-related changes in protein metabolism. Protein supplements in combination with strength training shows the best benefit. Disorders in balance and gait are caused by age-related or pathologic changes in a complex regulation system of gait. The individual fall risk correlates with the gait variability and even increases with bad dual task performance. Activities with high requirements of attention and body awareness are the most effective prevention for falls in the elderly (-50%).

Avoiding Low Falling Numbers Problems in Wheat

USDA-ARS?s Scientific Manuscript database

The Hagberg-Perten Falling Number (FN) method is used to detect starch degradation due to ''-amylase enzyme activity in wheat meal. Wheat can be severely discounted when the FN is below 300 seconds. Farmers in the northwest wheat-growing states suffered serious economic losses due to widespread pro...

Microcontroller based driver alertness detection systems to detect drowsiness

NASA Astrophysics Data System (ADS)

Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

2018-04-01

The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

Occupational fall injuries presenting to the emergency department.

PubMed

Son, Hyung Min; Kim, Sun Hyu; Shin, Sang Do; Ryoo, Hyun Wook; Ryu, Hyun Ho; Lee, Ji Ho

2014-04-01

This study was performed to evaluate occupational and fall injuries presenting to the ED, the risk factors associated with falls among all occupational injuries, and factors affecting prognosis. Data from ED-based Occupational Injury Surveillance System were analysed to investigate the occupational injuries. The 2147 occupational injury subjects were divided into two groups: fall (n = 213, 9.9%) and non-fall (n = 1934, 90.1%). Data including baseline and clinical characteristics were compared between the groups. The mean age was older in the fall group (46 vs 42 years old). The rate of construction site-related injury was 32% in the fall group and only 8% in the non-fall group. Injury occurrence during regular working hours (09.00 hours to 18.00 hours) was 70% in the fall group and 57% in the non-fall group. Injury severity using the Excess Mortality Ratio-adjusted Injury Severity Score (EMR-ISS) was more severe in the fall group than in the non-fall group, and days away from work were longer in the fall group than the non-fall group. Older age, compared with an age <29 years old and presence in a construction area during regular working hours were factors associated with fall injuries. Factors affecting prolonged absence for work were older age, higher EMR-ISS, fall injury and poor workplace environmental conditions. Risk factors associated with fall-related occupational injuries include older age and being at a construction area during regular working hours. Falls among occupational injuries are more severe than other injuries and result in longer work loss. © 2014 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

PubMed

Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Event detection in an assisted living environment.

PubMed

Stroiescu, Florin; Daly, Kieran; Kuris, Benjamin

2011-01-01

This paper presents the design of a wireless event detection and in building location awareness system. The systems architecture is based on using a body worn sensor to detect events such as falls where they occur in an assisted living environment. This process involves developing event detection algorithms and transmitting such events wirelessly to an in house network based on the 802.15.4 protocol. The network would then generate alerts both in the assisted living facility and remotely to an offsite monitoring facility. The focus of this paper is on the design of the system architecture and the compliance challenges in applying this technology.

Spatial analysis of falls in an urban community of Hong Kong

PubMed Central

Lai, Poh C; Low, Chien T; Wong, Martin; Wong, Wing C; Chan, Ming H

2009-01-01

Background Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations. Results This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh) and Standard Deviational Ellipse (SDE) techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population. Conclusion The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies. PMID:19291326

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

Unexplained Falls Are Frequent in Patients with Fall-Related Injury Admitted to Orthopaedic Wards: The UFO Study (Unexplained Falls in Older Patients).

PubMed

Chiara, Mussi; Gianluigi, Galizia; Pasquale, Abete; Alessandro, Morrione; Alice, Maraviglia; Gabriele, Noro; Paolo, Cavagnaro; Loredana, Ghirelli; Giovanni, Tava; Franco, Rengo; Giulio, Masotti; Gianfranco, Salvioli; Niccolò, Marchionni; Andrea, Ungar

2013-01-01

To evaluate the incidence of unexplained falls in elderly patients affected by fall-related fractures admitted to orthopaedic wards, we recruited 246 consecutive patients older than 65 (mean age 82 ± 7 years, range 65-101). Falls were defined "accidental" (fall explained by a definite accidental cause), "medical" (fall caused directly by a specific medical disease), "dementia-related" (fall in patients affected by moderate-severe dementia), and "unexplained" (nonaccidental falls, not related to a clear medical or drug-induced cause or with no apparent cause). According to the anamnestic features of the event, older patients had a lower tendency to remember the fall. Patients with accidental fall remember more often the event. Unexplained falls were frequent in both groups of age. Accidental falls were more frequent in younger patients, while dementia-related falls were more common in the older ones. Patients with unexplained falls showed a higher number of depressive symptoms. In a multivariate analysis a higher GDS and syncopal spells were independent predictors of unexplained falls. In conclusion, more than one third of all falls in patients hospitalized in orthopaedic wards were unexplained, particularly in patients with depressive symptoms and syncopal spells. The identification of fall causes must be evaluated in older patients with a fall-related injury.

Where attention falls: Increased risk of falls from the converging impact of cortical cholinergic and midbrain dopamine loss on striatal function

PubMed Central

Sarter, Martin; Albin, Roger L.; Kucinski, Aaron; Lustig, Cindy

2015-01-01

Falls are a major source of hospitalization, long-term institutionalization, and death in older adults and patients with Parkinson’s disease (PD). Limited attentional resources are a major risk factor for falls. In this review, we specify cognitive–behavioral mechanisms that produce falls and map these mechanisms onto a model of multi-system degeneration. Results from PET studies in PD fallers and findings from a recently developed animal model support the hypothesis that falls result from interactions between loss of basal forebrain cholinergic projections to the cortex and striatal dopamine loss. Striatal dopamine loss produces inefficient, low-vigor gait, posture control, and movement. Cortical cholinergic deafferentation impairs a wide range of attentional processes, including monitoring of gait, posture and complex movements. Cholinergic cell loss reveals the full impact of striatal dopamine loss on motor performance, reflecting loss of compensatory attentional supervision of movement. Dysregulation of dorsomedial striatal circuitry is an essential, albeit not exclusive, mediator of falls in this dual-system model. Because cholinergic neuromodulatory activity influences cortical circuitry primarily via stimulation of α4β2* nicotinic acetylcholine receptors, and because agonists at these receptors are known to benefit attentional processes in animals and humans, treating PD fallers with such agonists, as an adjunct to dopaminergic treatment, is predicted to reduce falls. Falls are an informative behavioral endpoint to study attentional–motor integration by striatal circuitry. PMID:24805070

Effects of a Community-Based Fall Management Program on Medicare Cost Savings.

PubMed

Ghimire, Ekta; Colligan, Erin M; Howell, Benjamin; Perlroth, Daniella; Marrufo, Grecia; Rusev, Emil; Packard, Michael

2015-12-01

Fall-related injuries and health risks associated with reduced mobility or physical inactivity account for significant costs to the U.S. healthcare system. The widely disseminated lay-led A Matter of Balance (MOB) program aims to help older adults reduce their risk of falling and associated activity limitations. This study examined effects of MOB participation on health service utilization and costs for Medicare beneficiaries, as a part of a larger effort to understand the value of community-based prevention and wellness programs for Medicare. A controlled retrospective cohort study was conducted in 2012-2013, using 2007-2011 MOB program data and 2006-2013 Medicare data. It investigated program effects on falls and fall-related fractures, and health service utilization and costs (standardized to 2012 dollars), of 6,136 Medicare beneficiaries enrolled in MOB from 2007 through 2011. A difference-in-differences analysis was employed to compare outcomes of MOB participants with matched controls. MOB participation was associated with total medical cost savings of $938 per person (95% CI=$379, $1,498) at 1 year. Savings per person amounted to $517 (95% CI=$265, $769) for unplanned hospitalizations; $81 for home health care (95% CI=$20, $141); and $234 (95% CI=$55, $413) for skilled nursing facility care. Changes in the incidence of falls or fall-related fractures were not detected, suggesting that cost savings accrue through other mechanisms. This study suggests that MOB and similar prevention programs have the potential to reduce Medicare costs. Further research accounting for program delivery costs would help inform the development of Medicare-covered preventive benefits. Copyright © 2015 American Journal of Preventive Medicine. All rights reserved.

Washington Community Colleges Fall Quarter Report, 1980.

ERIC Educational Resources Information Center

Story, Sherie; And Others

This three-part report presents a series of 46 tables providing data about enrollments, student characteristics, and personnel in the Washington community college system for Fall Quarter 1980. After a summary of the statistical highlights of the study, Chapter I offers historical data on Fall Quarter, full-time equivalent (FTE) and student…

Fatal falls in the US construction industry, 1990 to 1999.

PubMed

Derr, J; Forst, L; Chen, H Y; Conroy, L

2001-10-01

The Occupational Safety and Health Administration's (OSHA's) Integrated Management Information System (IMIS) database allows for the detailed analysis of risk factors surrounding fatal occupational events. This study used IMIS data to (1) perform a risk factor analysis of fatal construction falls, and (2) assess the impact of the February 1995 29 CFR Part 1926 Subpart M OSHA fall protection regulations for construction by calculating trends in fatal fall rates. In addition, IMIS data on fatal construction falls were compared with data from other occupational fatality surveillance systems. For falls in construction, the study identified several demographic factors that may indicate increased risk. A statistically significant downward trend in fatal falls was evident in all construction and within several construction categories during the decade. Although the study failed to show a statistically significant intervention effect from the new OSHA regulations, it may have lacked the power to do so.

Generalized Detectability for Discrete Event Systems

PubMed Central

Shu, Shaolong; Lin, Feng

2011-01-01

In our previous work, we investigated detectability of discrete event systems, which is defined as the ability to determine the current and subsequent states of a system based on observation. For different applications, we defined four types of detectabilities: (weak) detectability, strong detectability, (weak) periodic detectability, and strong periodic detectability. In this paper, we extend our results in three aspects. (1) We extend detectability from deterministic systems to nondeterministic systems. Such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems. (2) We develop polynomial algorithms to check strong detectability. The previous algorithms are based on observer whose construction is of exponential complexity, while the new algorithms are based on a new automaton called detector. (3) We extend detectability to D-detectability. While detectability requires determining the exact state of a system, D-detectability relaxes this requirement by asking only to distinguish certain pairs of states. With these extensions, the theory on detectability of discrete event systems becomes more applicable in solving many practical problems. PMID:21691432

Intelligent transportation system (ITS) study for the Buffalo and Niagara Falls metropolitan area, Erie and Niagara Counties, New York : system architecture, working paper #5

DOT National Transportation Integrated Search

1997-06-18

This document has been prepared as part of the New York State Department of Transportation (NYSDOT) Buffalo and Niagara Falls Intelligent Transportation System Study. Working Paper #5 defines the conceptual system architecture that applies to the reg...

Central Nervous System Medication Burden and Serious Falls in Older Nursing Home Residents

PubMed Central

Hanlon, Joseph T.; Zhao, Xinhua; Naples, Jennifer G.; Aspinall, Sherrie L.; Perera, Subashan; Nace, David A.; Castle, Nicholas G.; Greenspan, Susan L.; Thorpe, Carolyn T.

2016-01-01

Objectives To examine the association between CNS medication burden and serious falls in those with a recent fall history. Design Nested-case control study; cases matched to four controls by age, gender, and date. Setting US nursing homes Participants 5,556 residents age ≥ 65 with a recent fall history admitted to a nursing home between 1/1–9/30/2010 and followed until discharge, death, or 12/31/2010. Measurements Outcome was serious falls as per Medicare Part A and B ICD/CPT codes. CNS burden, from Medicare Part D data, was calculated by dividing the daily dose of each CNS agent (i.e., specific antidepressants, antiepileptic, antipsychotic, benzodiazepine and opioid receptor agonists) received during the six days prior to the index (outcome) date by the minimum effective geriatric daily dose and summing the results across medications. Results There were 367 cases and 1468 matched controls. Those taking 3+ CNS standardized daily doses were more likely to have a serious fall than those not taking any CNS medications (Adjusted Odds Ratio 1.83; 95% confidence interval 1.35–2.48). There was no significant difference in fall risk for residents taking >0 to <3 CNS standardized daily doses compared to residents taking no CNS medications (Adjusted Odds Ratio 0.85; 95% CI 0.63–1.15). Conclusion CNS medication burden, approximately 3+ standardized daily doses, was associated with an increased risk of serious falls in nursing home residents with recent fall. Clinicians should be vigilant for opportunities to discontinue or decrease the doses of individual CNS medications and/or consider non-pharmacological alternatives. Such interventions that reduce use of CNS medications in nursing homes could reduce fall rates but further research is needed to confirm this. PMID:28152179

Falls in spinocerebellar ataxias: Results of the EuroSCA Fall Study.

PubMed

Fonteyn, Ella M R; Schmitz-Hübsch, Tanja; Verstappen, Carla C; Baliko, Laslo; Bloem, Bastiaan R; Boesch, Silvia; Bunn, Lisa; Charles, Perrine; Dürr, Alexandra; Filla, Allesandro; Giunti, Paola; Globas, Christoph; Klockgether, Thomas; Melegh, Bela; Pandolfo, Massimo; De Rosa, Anna; Schöls, Ludger; Timmann, Dagmar; Munneke, Marten; Kremer, Berry P H; van de Warrenburg, Bart P C

2010-06-01

To investigate the frequency, details, and consequences of falls in patients with autosomal dominant spinocerebellar ataxias (SCAs) and to derive specific disease-related risk factors that are associated with an increased fall frequency. Two hundred twenty-eight patients with SCA1, SCA2, SCA3, or SCA6, recruited from the EuroSCA natural history study, completed a fall questionnaire that assessed the frequency, consequences, and several details of falls in the previous 12 months. Relevant disease characteristics were retrieved from the EuroSCA registry. The database of the natural history study provided the ataxia severity scores as well as the number and nature of non-ataxia symptoms. Patients (73.6%) reported at least one fall in the preceding 12 months. There was a high rate of fall-related injuries (74%). Factors that were associated with a higher fall frequency included: disease duration, severity of ataxia, the presence of pyramidal symptoms, the total number of non-ataxia symptoms, and the genotype SCA3. Factors associated with a lower fall frequency were: the presence of extrapyramidal symptoms (more specifically dystonia of the lower limbs) and the genotype SCA2. The total number of non-ataxia symptoms and longer disease duration were independently associated with a higher fall frequency in a logistic regression analysis, while the presence of extrapyramidal symptoms was independently associated with a lower fall frequency. Our findings indicate that, in addition to more obvious factors that are associated with frequent falls, such as disease duration and ataxia severity, non-ataxia manifestations in SCA play a major role in the fall etiology of these patients.

Impact of Fall Prevention on Nurses and Care of Fall Risk Patients.

PubMed

King, Barbara; Pecanac, Kristen; Krupp, Anna; Liebzeit, Daniel; Mahoney, Jane

2018-03-19

Falls are common events for hospitalized older adults, resulting in negative outcomes both for patients and hospitals. The Center for Medicare and Medicaid (CMS) has placed pressure on hospital administrators by identifying falls as a "never event", resulting in a zero falls goal for many hospitals. Staff nurses are responsible for providing direct care to patients and for meeting the hospital no falls goal. Little is known about the impact of "zero falls" on nurses, patients and the organization. A qualitative study, using Grounded Dimensional Analysis (GDA) was conducted to explore nurses' experiences with fall prevention in hospital settings and the impact of those experiences on how nurses provide care to fall risk patients. Twenty-seven registered nurses and certified nursing assistants participated in in-depth interviews. Open, axial and selective coding was used to analyze data. A conceptual model which illustrates the impact of intense messaging from nursing administration to prevent patient falls on nurses, actions nurses take to address the message and the consequences to nurses, older adult patients and to the organization was developed. Intense messaging from hospital administration to achieve zero falls resulted in nurses developing a fear of falls, protecting self and unit, and restricting fall risk patients as a way to stop messages and meet the hospital goal. Results of this study identify unintended consequences of fall prevention message on nurses and older adult patients. Further research is needed understand how nurse care for fall risk patients.

Effect of a Multidisciplinary Fall Risk Assessment on Falls Among Neurology Inpatients

PubMed Central

Hunderfund, Andrea N. Leep; Sweeney, Cynthia M.; Mandrekar, Jayawant N.; Johnson, LeAnn M.; Britton, Jeffrey W.

2011-01-01

OBJECTIVE: To evaluate whether the addition of a physician assessment of patient fall risk at admission would reduce inpatient falls on a tertiary hospital neurology inpatient unit. PATIENTS AND METHODS: A physician fall risk assessment was added to the existing risk assessment process (clinical nurse evaluation and Hendrich II Fall Risk Model score with specific fall prevention measures for patients at risk). An order to select either “Patient is” or “Patient is not at high risk of falls by physician assessment” was added to the physician electronic admission order set. Nurses and physicians were instructed to reach consensus when assessments differed. Full implementation occurred in second-quarter 2008. Preimplementation (January 1, 2006, to March 31, 2008) and postimplementation (April 1, 2008, to December 31, 2009) rates of falls were compared on the neurology inpatient unit and on 6 other medical units that did not receive intervention. RESULTS: The rate of falls during the 7 quarters after full implementation was significantly lower than that during the 9 preceding quarters (4.12 vs 5.69 falls per 1000 patient-days; P=.04), whereas the rate of falls on other medical units did not significantly change (2.99 vs 3.33 falls per 1000 patient-days; P=.24, Poisson test). The consensus risk assessment at admission correctly identified patients at risk for falls (14/325 at-risk patients fell vs 0/147 low-risk patients; P=.01, χ2 test), but the Hendrich II Fall Risk Model score, nurse, and physician assessments individually did not. CONCLUSION: A multidisciplinary approach to fall risk assessment is feasible, correctly identifies patients at risk, and was associated with a reduction in inpatient falls. PMID:21193651

The consumption of two or more fall risk-increasing drugs rather than polypharmacy is associated with falls.

PubMed

Zia, Anam; Kamaruzzaman, Shahrul B; Tan, Maw P

2017-03-01

The presemt study aimed to determine the association between the risk of recurrent and injurious falls with polypharmacy, fall risk-increasing drugs (FRID) and FRID count among community-dwelling older adults. Participants (n = 202) were aged ≥65 years with two or more falls or one injurious fall in the past year, whereas controls (n = 156) included volunteers aged ≥65 years with no falls in the past year. A detailed medication history was obtained alongside demographic data. Polypharmacy was defined as "regular use of five or more prescription drugs." FRID were identified as cardiovascular agents, central nervous system drugs, analgesics and endocrine drugs; multiple FRID were defined as two or more FRID. Multiple logistic regression analyses were used to adjust for confounders. The use of non-steroidal anti-inflammatory drugs was independently associated with an increased risk of falls. Univariate analyses showed both polypharmacy (OR 2.23, 95% CI 1.39-3.56; P = 0.001) and the use of two or more FRID (OR 2.9, 95% CI 1.9-4.5; P = 0.0001) were significantly more likely amongst fallers. After adjustment for age, sex and comorbidities, blood pressure, and physical performance scores, polypharmacy was no longer associated with falls (OR 1.6, 95% CI 0.9-2.9; P = 0.102), whereas the consumption of two or more FRID remained a significant predictor for falls (OR 2.8, 95% CI 1.4-5.3; P = 0.001). Among high risk fallers, the use of two or more FRID was an independent risk factor for falls instead of polypharmacy. Our findings will inform clinical practice in terms of medication reviews among older adults at higher risk of falls. Future intervention studies will seek to confirm whether avoidance or withdrawal of multiple FRID reduces the risk of future falls. Geriatr Gerontol Int 2017; 17: 463-470. © 2016 Japan Geriatrics Society.

Reducing falls after hospital discharge: a protocol for a randomised controlled trial evaluating an individualised multimodal falls education programme for older adults

PubMed Central

Hill, Anne-Marie; Etherton-Beer, Christopher; McPhail, Steven M; Morris, Meg E; Flicker, Leon; Bulsara, Max; Lee, Den-Ching; Francis-Coad, Jacqueline; Waldron, Nicholas; Boudville, Amanda; Haines, Terry

2017-01-01

Introduction Older adults frequently fall after discharge from hospital. Older people may have low self-perceived risk of falls and poor knowledge about falls prevention. The primary aim of the study is to evaluate the effect of providing tailored falls prevention education in addition to usual care on falls rates in older people after discharge from hospital compared to providing a social intervention in addition to usual care. Methods and analyses The ‘Back to My Best’ study is a multisite, single blind, parallel-group randomised controlled trial with blinded outcome assessment and intention-to-treat analysis, adhering to CONSORT guidelines. Patients (n=390) (aged 60 years or older; score more than 7/10 on the Abbreviated Mental Test Score; discharged to community settings) from aged care rehabilitation wards in three hospitals will be recruited and randomly assigned to one of two groups. Participants allocated to the control group shall receive usual care plus a social visit. Participants allocated to the experimental group shall receive usual care and a falls prevention programme incorporating a video, workbook and individualised follow-up from an expert health professional to foster capability and motivation to engage in falls prevention strategies. The primary outcome is falls rates in the first 6 months after discharge, analysed using negative binomial regression with adjustment for participant's length of observation in the study. Secondary outcomes are injurious falls rates, the proportion of people who become fallers, functional status and health-related quality of life. Healthcare resource use will be captured from four sources for 6 months after discharge. The study is powered to detect a 30% relative reduction in the rate of falls (negative binomial incidence ratio 0.70) for a control rate of 0.80 falls per person over 6 months. Ethics and dissemination Results will be presented in peer-reviewed journals and at conferences worldwide. This

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

PubMed

Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

2008-01-01

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

Dance! Don't Fall - preventing falls and promoting exercise at home.

PubMed

Kerwin, Maureen; Nunes, Francisco; Silva, Paula Alexandra

2012-01-01

Falling is a serious danger to older adults that is usually only addressed after a person has fallen, when doctors administer clinical tests to determine the patient's risk of falling again. Having the technological capability of performing fall risk assessment tests with a smartphone, the authors set out to design a mobile application that would enable users to monitor their risk themselves and consequently prevent falls from occurring. The authors conducted a literature review and two observation sessions before beginning the iterative design process that resulted in the Dance! Don't Fall (DDF) game, a mobile application that enables users to both monitor their fall risk and actively reduce it through fun and easy exercise.

Detection system for high-resolution gamma radiation spectroscopy with neutron time-of-flight filtering

DOEpatents

Dioszegi, Istvan; Salwen, Cynthia; Vanier, Peter

2014-12-30

A .gamma.-radiation detection system that includes at least one semiconductor detector such as HPGe-Detector, a position-sensitive .alpha.-Detector, a TOF Controller, and a Digitizer/Integrator. The Digitizer/Integrator starts to process the energy signals of a .gamma.-radiation sent from the HPGe-Detector instantly when the HPGe-Detector detects the .gamma.-radiation. Subsequently, it is determined whether a coincidence exists between the .alpha.-particles and .gamma.-radiation signal, based on a determination of the time-of-flight of neutrons obtained from the .alpha.-Detector and the HPGe-Detector. If it is determined that the time-of-flight falls within a predetermined coincidence window, the Digitizer/Integrator is allowed to continue and complete the energy signal processing. If, however, there is no coincidence, the Digitizer/Integrator is instructed to be clear and reset its operation instantly.

Where attention falls: Increased risk of falls from the converging impact of cortical cholinergic and midbrain dopamine loss on striatal function.

PubMed

Sarter, Martin; Albin, Roger L; Kucinski, Aaron; Lustig, Cindy

2014-07-01

Falls are a major source of hospitalization, long-term institutionalization, and death in older adults and patients with Parkinson's disease (PD). Limited attentional resources are a major risk factor for falls. In this review, we specify cognitive-behavioral mechanisms that produce falls and map these mechanisms onto a model of multi-system degeneration. Results from PET studies in PD fallers and findings from a recently developed animal model support the hypothesis that falls result from interactions between loss of basal forebrain cholinergic projections to the cortex and striatal dopamine loss. Striatal dopamine loss produces inefficient, low-vigor gait, posture control, and movement. Cortical cholinergic deafferentation impairs a wide range of attentional processes, including monitoring of gait, posture and complex movements. Cholinergic cell loss reveals the full impact of striatal dopamine loss on motor performance, reflecting loss of compensatory attentional supervision of movement. Dysregulation of dorsomedial striatal circuitry is an essential, albeit not exclusive, mediator of falls in this dual-system model. Because cholinergic neuromodulatory activity influences cortical circuitry primarily via stimulation of α4β2* nicotinic acetylcholine receptors, and because agonists at these receptors are known to benefit attentional processes in animals and humans, treating PD fallers with such agonists, as an adjunct to dopaminergic treatment, is predicted to reduce falls. Falls are an informative behavioral endpoint to study attentional-motor integration by striatal circuitry. Copyright © 2014 Elsevier Inc. All rights reserved.

Pilot study to investigate the feasibility of the Home Falls and Accidents Screening Tool (HOME FAST) to identify older Malaysian people at risk of falls

PubMed Central

Romli, Muhammad Hibatullah; Mackenzie, Lynette; Lovarini, Meryl; Tan, Maw Pin

2016-01-01

Objective The relationship between home hazards and falls in older Malaysian people is not yet fully understood. No tools to evaluate the Malaysian home environment currently exist. Therefore, this study aimed to pilot the Home Falls and Accidents Screening Tool (HOME FAST) to identify hazards in Malaysian homes, to evaluate the feasibility of using the HOME FAST in the Malaysian Elders Longitudinal Research (MELoR) study and to gather preliminary data about the experience of falls among a small sample of Malaysian older people. Design A cross-sectional pilot study was conducted. Setting An urban setting in Kuala Lumpur. Participants 26 older people aged 60 and over were recruited from the control group of a related research project in Malaysia, in addition to older people known to the researchers. Primary outcome measure The HOME FAST was applied with the baseline survey for the MELoR study via a face-to-face interview and observation of the home by research staff. Results The majority of the participants were female, of Malay or Chinese ethnicity and living with others in a double-storeyed house. Falls were reported in the previous year by 19% and 80% of falls occurred at home. Gender and fear of falling had the strongest associations with home hazards. Most hazards were detected in the bathroom area. A small number of errors were detected in the HOME FAST ratings by researchers. Conclusions The HOME FAST is feasible as a research and clinical tool for the Malaysian context and is appropriate for use in the MELoR study. Home hazards were prevalent in the homes of older people and further research with the larger MELoR sample is needed to confirm the validity of using the HOME FAST in Malaysia. Training in the use of the HOME FAST is needed to ensure accurate use by researchers. PMID:27531736

Prevention of Falls and Fall-Related Injuries in Community-Dwelling Seniors

PubMed Central

2008-01-01

Executive Summary In early August 2007, the Medical Advisory Secretariat began work on the Aging in the Community project, an evidence-based review of the literature surrounding healthy aging in the community. The Health System Strategy Division at the Ministry of Health and Long-Term Care subsequently asked the secretariat to provide an evidentiary platform for the ministry’s newly released Aging at Home Strategy. After a broad literature review and consultation with experts, the secretariat identified 4 key areas that strongly predict an elderly person’s transition from independent community living to a long-term care home. Evidence-based analyses have been prepared for each of these 4 areas: falls and fall-related injuries, urinary incontinence, dementia, and social isolation. For the first area, falls and fall-related injuries, an economic model is described in a separate report. Please visit the Medical Advisory Secretariat Web site, http://www.health.gov.on.ca/english/providers/program/mas/mas_about.html, to review these titles within the Aging in the Community series. Aging in the Community: Summary of Evidence-Based Analyses Prevention of Falls and Fall-Related Injuries in Community-Dwelling Seniors: An Evidence-Based Analysis Behavioural Interventions for Urinary Incontinence in Community-Dwelling Seniors: An Evidence-Based Analysis Caregiver- and Patient-Directed Interventions for Dementia: An Evidence-Based Analysis Social Isolation in Community-Dwelling Seniors: An Evidence-Based Analysis The Falls/Fractures Economic Model in Ontario Residents Aged 65 Years and Over (FEMOR) Objective To identify interventions that may be effective in reducing the probability of an elderly person’s falling and/or sustaining a fall-related injury. Background Although estimates of fall rates vary widely based on the location, age, and living arrangements of the elderly population, it is estimated that each year approximately 30% of community-dwelling individuals aged

One-leg balance is an important predictor of injurious falls in older persons.

PubMed

Vellas, B J; Wayne, S J; Romero, L; Baumgartner, R N; Rubenstein, L Z; Garry, P J

1997-06-01

To test the hypothesis that one-leg balance is a significant predictor of falls and injurious falls. Analysis of data from a longitudinal cohort study. Healthy, community-living volunteers older than age 60 enrolled in the Albuquerque Falls Study and followed for 3 years (N = 316; mean age 73 years). Falls and injurious falls detected via reports every other month. Baseline measures of demographics, history, physical examination, Iowa Self Assessment Inventory, balance and gait assessment, and one-leg balance (ability to stand unassisted for 5 seconds on one leg). At baseline, 84.5% of subjects could perform one-leg balance. (Impairment was associated with older age and gait abnormalities.) Over the 3-year follow-up, 71% experienced a fall and 22% an injurious fall. The only independent significant predictor of all falls using logistic regression was age greater than 73. However, impaired one-leg balance was the only significant independent predictor of injurious falls (relative risk: 2.13; 95% CI: 1.04, 4.34; P = .03). One-leg balance appears to be a significant and easy-to-administer predictor of injurious falls, but not of all falls. In our study, it was the strongest individual predictor. However, no single factor seems to be accurate enough to be relied on as a sole predictor of fall risk or fall injury risk because so many diverse factors are involved in falling.

The Association Between Fall Frequency, Injury Risk, and Characteristics of Falls in Older Residents of Long-Term Care: Do Recurrent Fallers Fall More Safely?

PubMed

van Schooten, Kimberley S; Yang, Yijian; Feldman, Fabio; Leung, Ming; McKay, Heather; Sims-Gould, Joanie; Robinovitch, Stephen N

2018-05-09

Although a fall is a necessary prerequisite to a fall-related injury, previous studies suggest that frequent fallers are at lower injury risk for a given fall. We tested the hypotheses that differences in protective responses or the circumstances of falls underlie differences in injury risk with fall frequency. We analyzed video footage of 897 falls experienced by 220 long-term care residents (mean age 82 ± 9 years) to identify the cause of imbalance, activity leading to falling, direction of fall initiation, balance recovery and fall protective responses, and occurrence of impact to the head or hip. We further obtained injury information from the facilities' fall registration. We used generalized estimating equation models to examine the association between quartiles of fall frequency, injury risk, and fall characteristics. Residents with the highest fall frequency group (Q4; ≥5.6 falls/year) were less likely to sustain an injury per fall. They were less likely to fall during walking and more likely to fall during stand-to-sit transfers. Residents in the lowest fall frequency group (Q1; <1.15 falls/year) were more likely to fall during walking, and walking was associated with an increased risk for injury. When compared to less frequent fallers, more frequent fallers had a lower risk for injury per fall. This appeared to be explained by differences in the circumstances of falls, and not by protective responses. Injury prevention strategies in long-term care should target both frequent and infrequent fallers, as the latter are more mobile and apt to sustain injury.