Fall Monitoring System Research Articles

Vision-Based Fall Detector for Elderly Based on Sliding Window Approach and Feature Engineering

A new vision-based fall detector is proposed that uses the tsfresh tool to generate features from the bounding box motion parameters of an object and performs classification in a sliding window mode. The efficiency of the generated features is demonstrated compared to the primary ones. Using the auto-sklearn library and a generalized dataset compiled from the UR Fall Detection and CAUCAFall datasets, the best human fall detection model is found. This model, based on a gradient boosting classifier, achieved 96% accuracy, which is not inferior to well-known detection algorithms, but uses only two primary motion parameters to generate secondary features. A PCA-based class separability study showed that for secondary features, 99% of the variance is captured by the first 4 principal components, while for primary features, the first 10 principal components contain only 80% of the data variance. Furthermore, the processing time for generating secondary features and making predictions was found to be relatively short, taking only a few seconds per sequence, highlighting the practical applicability of the proposed approach in real-time fall monitoring systems.

Fall Monitoring System Based on Wearable Device and Improved KNN

Fall Monitoring System Based on Wearable Device and Improved KNN

Technologies to Increase Freedom for People Living With Dementia

What Is the Issue?
 
 Dementia refers to symptoms affecting cognition (including memory), behaviour, and mood, which can significantly impact daily activities and independence. Conditions that may contribute to the development of dementia include Alzheimer disease (AD), Parkinson disease, and stroke. As the population ages, the number of people in Canada living with dementia will continue to increase.
 More than 60% of people with dementia live at home rather than in a long-term care facility. Approximately 40% of people aged 80 and older who have dementia reside in long-term care facilities, while others stay in their homes. When living at home in the community, support is needed to maintain safety, independence, and quality of life for both the person living with dementia and their caregiver(s).
 Most of the assistive technologies used to help support people with dementia to live in their homes are paid for by the user and not by provincial or territorial insurance programs, highlighting issues of equity of access related to income.
 
 What Is the Technology?
 
 A variety of technologies, including medical devices and consumer electronics, are available that can be used with the intention of helping people living with dementia stay in their homes.
 Technologies to support the care of people living with dementia can be broadly grouped into 2 categories: technologies related to diagnosis, assessment, and early risk identification and technologies related to management and rehabilitation.
 This report primarily focuses on technologies that aim to provide management and rehabilitation; these aim to support people with dementia (and their caregivers) – allowing people to live in their homes and communities for longer.
 These are often classified as assistive devices and include GPS trackers, fall monitoring systems, and connected technologies that can increase and simplify access to services such as food or grocery delivery, pharmacies, and telehealth.
 
 What Is the Potential Impact?
 
 User-friendly, connected, and effective technologies that allow freedom for people living with dementia may also reduce caregiving stress; however, most randomized trials of assistive technologies have not demonstrated the usefulness of these technologies in real-world settings in a way that support them being formally incorporated into dementia management.
 The use of supportive technologies may help people living with dementia stay safely in their homes for longer, thereby reducing the burden on long-term care facilities and providing potential savings to the health care system.
 
 What Else Do We Need to Know?
 
 There is a constant conflict between safety and privacy for people living with dementia who may not always be aware of their current state of cognition and may not be able to provide adequately informed consent for the continued use of monitoring technologies.
 The evidence suggests further research into the effectiveness of these technologies in real-world settings is required to understand better their usefulness and place in therapy for people living at home with dementia. There remains a lack of consensus on the effectiveness of these technologies and a lack of guidance for their use.

An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room.

To reduce the risk of elderly people falling in a private room without relying on a closed-circuit television system that results in serious privacy and trust concerns, a fall monitoring system that protects the privacy and does not monitor a person's activities is needed. An ultrasonic-based sensor system for elderly fall monitoring in a smart room is proposed in this study. An array of ultrasonic sensors, whose ranges are designed to cover the room space, are initially installed on a wall of the room, and the sensors are rotated to transmit and receive ultrasonic signals to measure the distances to a moving object while preventing ultrasonic signal interference. Distance changes measured by ultrasonic sensors are used as time-independent patterns to recognize when an elderly person falls. To evaluate the performance of the proposed system, a sensor system prototype using long short-term memory was constructed, and experiments with 25 participants were performed. An accuracy of approximately 98% was achieved in this experiment using the proposed method, which was a slight improvement over that of the conventional method.

Design of Portable Wireless Fall Monitoring and Warning System

Aiming at preventing the old man falling down in the field of study, this article studies the status quo, focusing on sitting down research and analyze the behavior characteristic, this paper proposes a portable wireless fall monitoring and alarm system scheme design and manufacture, the conventional time-domain motion data multilevel threshold detection algorithm combined with combined with frequency domain characteristics of machine learning classification has realized the real-time identification of fall behavior. The time domain data used rectangular window filter to preserve the critical time series of behavior occurrence, fully considering the time characteristics of data changes, and completed the detection of lying posture and falling and the exclusion of daily low-intensity activities of the elderly. In the frequency domain, Discrete Fourier Transform (DFT) was applied to the combined acceleration data of sitting posture fall and moderate intensity activity, and the Support Vector Machine (SVM) classifier model was established by using frequency domain features. Complete the recognition and detection of sitting posture fall, and complement the detection of lying posture fall. After the decision of falling is made, the information of falling will be sent to the guardian of the elderly in time through wireless communication module, and the on-site alarm will be made at the same time, so that the elderly can get help from others in time when there are people nearby.

Older Adults’ Perceptions of Smart City Initiatives to Age in Community

This project explores older American adults’ perceptions of smart city initiatives for them to “age in community” particularly in the northeast region. As the U.S. population is aging, it is imperative that the American cities can support their citizens to live in their preferred community environments for as long as they want. While there are many definitions of a smart city, some exemplary smarty city initiatives can be characterized as actively utilizing information and sensor technologies to promote efficiency and sustainability of city-wide systems, ultimately enhancing the quality of citizens' life. This project examines, in particular, seven smart city initiatives that are implemented globally: smart streetlights, health and fall monitoring system, community ridesharing, enhanced CCTVs, "age-friendly map," contact tracing app, and smart traffic system. By surveying those age 55 and older, with a representative sampling from the nine states in the northeast region, this project found that the vast majority of older Americans in this region would prefer to age in rural and suburban communities, and depending on where they prefer to age in (rural-exurban-suburban communities vs. urban-urban center communities) and gender (female vs. male), they perceive particular sets of smart city initiatives as more important for them to age in community. Furthermore, regardless of the community/location preference and demographic (gender, income level, and age) differences, 40% of the respondents expressed no concern of data or information privacy issues from these initiatives, opening some doors for the municipalities that plan to adopt some of these initiatives in the near future.

Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments

Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications. Human fall monitoring is especially important for elderly people and for non-typical activities with VR/AR applications. There are a lot of different approaches to improving the fidelity of fall monitoring systems through the use of novel sensors and deep learning architectures; however, there is still a lack of detail and diverse datasets for training deep learning fall detectors using monocular images. The issues with synthetic data generation based on digital human simulation were implemented and examined using the Unreal Engine. The proposed pipeline provides automatic “playback” of various scenarios for digital human behaviour simulation, and the result of a proposed modular pipeline for synthetic data generation of digital human interaction with the 3D environments is demonstrated in this paper. We used the generated synthetic data to train the Mask R-CNN-based segmentation of the falling person interaction area. It is shown that, by training the model with simulation data, it is possible to recognize a falling person with an accuracy of 97.6% and classify the type of person’s interaction impact. The proposed approach also allows for covering a variety of scenarios that can have a positive effect at a deep learning training stage in other human action estimation tasks in an VR/AR environment.

Elderly Fall Detection using Lightweight Convolution Deep Learning Model

Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For accurate identification of fall accidents, a publicly accessible data collection and a lightly weighted CNN model are used. The model reports proposed and 98.21 % precision.

Heterogeneous Sensor Data Fusion for Human Falling Detection

With the continuous improvement of human living standards, population aging has become a global development trend. At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern. Due to the aging of physiological structure and the decline of physical function, the probability and frequency of accidental falls in the elderly are very high. Under the above background, the purpose of this study is based on a heterogeneous sensor data fusion algorithm in an intelligent wearable sensor network. This article proposes a heterogeneous sensor data fusion algorithm based on wearable wireless body area network technology, and constructs a high-precision and stable wearable elderly activities of daily living (ADLs) and fall monitoring system. We first select the three-axis acceleration sensor, three-axis magnetic sensor, and three-axis angular velocity sensor to monitor the activities of the elderly. Then, we use Bluetooth to transmit the data collected by heterogeneous sensors to smartphones, and communicate with service centers and users through the mobile phone communication network, Family members interact to form a wireless city network based on wearable technology. Our proposed data fusion approach is based on the Kalman filter algorithm, which can reduce the system noise and improve the stability of the system. The experimental results demonstrate that the fall detection system proposed and implemented in this study can well detect accidental falls in the daily activities of the elderly, the sensitivity and specificity of the fall detection system are 98.7% and 98.5% respectively. The study in this article has a high research value and practical application significance in protecting the healthy life of the elderly.

Activity-Aware Fall Detection and Recognition Based on Wearable Sensors

Falling is a severe hazard among older adults. Fall treatment is considered to be one of the most costly treatments, which usually extends to a long time. One bad fall can cause severe injuries that may lead to permanent disability or even death. Therefore, an efficient and cost-effective fall monitoring system is exceptionally indispensable. With the advancement in technology, wearable sensors and systems provide a lucrative way to continuously monitor the elderly people for detecting any fall incident that may occur. Most of these wearable fall monitoring systems focus only on detecting a fall incident. However, to avoid the risk of any future fall, it is essential to be aware of the cause of a fall incident also. Therefore, to address this challenge, a wearable sensor-based continuous fall monitoring system is proposed in this paper, which is capable of detecting a fall and identifying the falling pattern and the activity associated with the fall incident. The performance of the proposed scheme is investigated with a series of experiments using three machine learning algorithms, namely, $k$ -nearest neighbors (KNNs), support vector machine, and random forest (RF). The proposed methodology achieved the highest accuracy for fall detection, i.e., 99.80%, using KNNs classifier, whereas the highest accuracy achieved in recognizing different falling activities is 96.82% using RF classifier.

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

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.

A Hybrid Temporal Reasoning Framework for Fall Monitoring

This paper presents a real-time method for detecting a fall at different phases using a wireless tri-axial accelerometer and reports the classification performance when the sensor is placed on different body parts. The proposed hybrid framework combines a rule-based knowledge representation scheme with a time control mechanism and machine-learning-based activity classification. Real-time temporal reasoning is performed using a standard rule-based inference engine. The framework is validated for fall detection performance, false alarm evaluation, and comparison with a highly cited baseline method. Based on a data set with 14 fall types (280 falls) collected from 16 subjects, the highest accuracy values of 86.54%, 87.31%, and 91.15% are obtained for fall detection at pre-impacts, impacts, and post-impacts, respectively. Without post-impact activity information, the side of the waist and chest are the best sensor positions, followed by the head, front of the waist, wrist, ankle, thigh, and upper arm. With post-impact activity information, the best sensor position is the side of the waist, followed by the head, wrist, front of the waist, thigh, chest, ankle, and upper arm. Most false alarms occur during transitions of lying postures. The proposed method is more robust to a variety of fall and activity types and yields better classification performance and false alarm rates compared with the baseline method. The results provide guidelines for sensor placement when developing a fall monitoring system.

Usability Study of a Wireless Monitoring System among Alzheimer's Disease Elderly Population.

Healthcare technologies are slowly entering into our daily lives, replacing old devices and techniques with newer intelligent ones. Although they are meant to help people, the reaction and willingness to use such new devices by the people can be unexpected, especially among the elderly. We conducted a usability study of a fall monitoring system in a long-term nursing home. The subjects were the elderly with advanced Alzheimer's disease. The study presented here highlights some of the challenges faced in the use of wearable devices and the lessons learned. The results gave us useful insights, leading to ergonomics and aesthetics modifications to our wearable systems that significantly improved their usability and acceptance. New evaluating metrics were designed for the performance evaluation of usability and acceptability.

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

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.

Accelerometer Sensor Module and Fall Detection Monitoring System Based on Wireless Sensor Network for e-Health Applications

This research implements a wireless accelerometer sensor module and an algorithm to determine an individual's posture, activity, and fall. An analog wireless, two-axis accelerometer sensor module was used. Using a wireless radiofrequency module, the accelerometer signal is displayed on a PC-based "Acceloger" (e-Digitalmed, Seoul, Korea) viewer program. The activities of daily living algorithm uses the AC components of the accelerometer signal to determine posture, activity, and fall, and posture while standing, sitting, lying, walking, running, and so on is determined by the DC component. The performance of 30 subjects was evaluated by assessing the algorithm and calculating the detection rate for postures, motions, and subjects. Using a wireless sensor network in an experimental space, subject's postures, motions, and fall monitoring system was implemented. In conclusion, this system can be applied to patients such as the elderly for activity monitoring and fall detection and also for sports athlete's exercise measurement and pattern analysis.

Enhancing the safety of hospitalization by reducing patient falls

The iatrogenic nature of hospitalization places patients at risk of falling, injury, and death. In this article, the major principles of providing protective and preventive interventions are outlined. The principles are the establishment of a multifaceted fall prevention program that targets fall interventions according to each etiologic factor; the recognition that fall protective and prevention interventions are distinct and serve a different function; the use of the fall monitoring system comprehensively; the creation of a clinical nurse specialist position, responsible for fall intervention; and a conscious and individualized approach to fall prevention. The process and problems of the varying nature of providing fall protection and fall prevention are discussed; for example, use of a side rail as a protective strategy may be successful with one patient but considered a hazard when used with a different patient. (Am J Infect Control 2002;30:376-80.)

How to reduce falls

The iatrogenic nature of hospitalization places patients at risk of falling, injury, and death. In this article, the major principles of providing protective and preventive interventions are outlined. The principles are the establishment of a multifaceted fall prevention program that targets fall interventions according to each etiologic factor; the recognition that fall protective and prevention interventions are distinct and serve a different function; the use of the fall monitoring system comprehensively; the creation of a clinical nurse specialist position, responsible for fall intervention; and a conscious and individualized approach to fall prevention. The process and problems of the varying nature of providing fall protection and fall prevention are discussed; for example, use of a side rail as a protective strategy may be successful with one patient but considered a hazard when used with a different patient. (Am J Infect Control 2002;30:376-80.)