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.
Highlights
According to the report issued by the World Health Organization (WHO), around 30% of people aged over 65 fall every year, and it leads to more than 300,000 people dying from falls each year [1]
It only takes about 1.05 s to classify 1920 samples. It proves that FD-DNN takes advantages of convolutional neural networks (CNN) and long short-term memory (LSTM)
This paper presents a novel low-power fall detection workflow for the elderly
Summary
According to the report issued by the World Health Organization (WHO), around 30% of people aged over 65 fall every year, and it leads to more than 300,000 people dying from falls each year [1]. Of the elderly fall victims suffered at least one new fall in six months [2]. Falling has been one of the leading causes of injuries to the elderly. It causes serious trauma to the brain, fractures, etc., and causes psychological fear and brings great psychological trauma to the elderly [3]. If the elderly cannot be found and rescued in time when they fall, the fall will often cause serious injury or even death [4]. The study of fall detection techniques is of great significance in reducing the mortality from falls in the elderly
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