scholarly journals An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4192
Author(s):  
Leyuan Liu ◽  
Yibin Hou ◽  
Jian He ◽  
Jonathan Lungu ◽  
Ruihai Dong
Keyword(s):  
Energy Efficient ◽  
Short Term Memory ◽  
Fall Detection ◽  
The Elderly ◽  
Experimental Result ◽  
Sleep Mode ◽  
Detection Accuracy ◽  
Community Based ◽  
Care For The Elderly ◽  
Specificity And Sensitivity

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.

scholarly journals IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5948
Author(s):  
Taekjin Han ◽  
Wonho Kang ◽  
Gyunghyun Choi
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Privacy Preservation ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Ultra Wideband ◽  
Detection Accuracy ◽  
Secondary Damage ◽  
Behavior Types

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

Managing Home Care for the Elderly: Lessons from Community-Based Agencies.

1987 ◽  
Vol 16 (1) ◽  
pp. 65
Author(s):  
Linda J. Redford ◽  
Anabel O. Pelham ◽  
William F. Clark
Keyword(s):  
Home Care ◽  
The Elderly ◽  
Community Based ◽  
Care For The Elderly

Models for community-based long-term care for the elderly in a changing health system

1997 ◽  
Vol 45 (2) ◽  
pp. 59-63 ◽  
Author(s):  
Clare E. Collins ◽  
Frieda R. Butler ◽  
Sarah H. Gueldner ◽  
Mary H. Palmer
Keyword(s):  
Health System ◽  
Long Term Care ◽  
The Elderly ◽  
Community Based ◽  
Term Care ◽  
Care For The Elderly

scholarly journals Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 508
Author(s):  
Feng-Shuo Hsu ◽  
Tang-Chen Chang ◽  
Zi-Jun Su ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen
Keyword(s):  
Density Functional ◽  
Probability Model ◽  
Three Dimensional ◽  
Fall Detection ◽  
The Elderly ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Movement Trajectory ◽  
Motion Data ◽  
Comparable Performance

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.

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