TY - JOUR PY - 2020// TI - An energy-efficient fall detection method based on FD-DNN for elderly people JO - Sensors (Basel) A1 - Liu, Leyuan A1 - Hou, Yibin A1 - He, Jian A1 - Lungu, Jonathan A1 - Dong, Ruihai SP - e4192 EP - e4192 VL - 20 IS - 15 N2 - 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

LA - en SN - 1424-8220 UR - http://dx.doi.org/10.3390/s20154192 ID - ref1 ER -