Citation

Liu L, Hou Y, He J, Lungu J, Dong R. Sensors (Basel) 2020; 20(15): e4192.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20154192

PMID

32731465

Abstract

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.

Language: en

Keywords

fall detection; energy-efficient; FD-DNN; ZigBee