@article{ref1,
title="An energy-efficient fall detection method based on FD-DNN for elderly people",
journal="Sensors (Basel)",
year="2020",
author="Liu, Leyuan and Hou, Yibin and He, Jian and Lungu, Jonathan and Dong, Ruihai",
volume="20",
number="15",
pages="e4192-e4192",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20154192",
url="http://dx.doi.org/10.3390/s20154192"
}