%0 Journal Article
%T An energy-efficient fall detection method based on FD-DNN for elderly people
%J Sensors (Basel)
%D 2020
%A Liu, Leyuan
%A Hou, Yibin
%A He, Jian
%A Lungu, Jonathan
%A Dong, Ruihai
%V 20
%N 15
%P e4192-e4192
%X 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.<p /> <p>Language: en</p>
%G en
%I MDPI: Multidisciplinary Digital Publishing Institute
%@ 1424-8220
%U http://dx.doi.org/10.3390/s20154192