A WiFi-Based Smart Home Fall Detection System Using Recurrent Neural Network

Falls among the elderly living on their own have been regarded as a major public health worry that can even lead to death. Fall detection system (FDS) that alerts caregivers or family members can potentially save lives of the elderly. However, conventional FDS involves wearable sensors and specialized hardware installations. This article presents a passive device-free FDS based on commodity WiFi framework for smart home, which is mainly composed of two modules in terms of hardware platform and client application. Concretely, commercial WiFi devices collect disturbance signal induced by human motions from smart home and transmit the data to a data analysis platform for further processing. Based on this basis, a discrete wavelet transform (DWT) method is used to eliminate the influence of random noise presented in the collected data. Next, a recurrent neural network (RNN) model is utilized to classify human motions and identify the fall status automatically. By leveraging Web Application Programming Interface (API), the analyzed data is able to be uploaded to the proxy server from which the client application then obtains the corresponding fall information. Moreover, the system has been implemented as a consumer mobile App that can help the elderly saving their lives in smart home, and detection performance of the proposed FDS has been evaluated by conducting comprehensive experiments on real-world dataset. The results confirm that the proposed FDS is able to achieve a satisfactory performance compared with some state-of-the-art algorithms.

I. Introduction

Falls are the leading cause of fatal and nonfatal injuries to the elderly in the modern society. According to the center for Disease Control and Prevention, one out of three adults aged 65 and over fall each year at home [1], [2]. Falls not only bring a main threat to the elderly’s health, but also account for a large part of medical cost. Most of the elderly are unable to get up by themselves after a fall, and studies have shown that the medical outcome of a fall is largely dependent on the response and rescue time [2]. The delay of medical treatment after a fall can increase the mortality risk in clinical conditions, half of those who experienced an extended period of lying on the floor died within six months after an incident [3]. In addition to physical injuries and high medical cost, falls can cause psychological damage to the elderly as well, which is termed as the fear of falling cycle by the fall researchers [3]. The fear cycle refers to the fact that after a fall, even without injury, the elderly become so afraid of falling again that they would reduce physical activities [3]. This in turn decreases their fitness, mobility and balance, and leads to decreased social interactions, reduced life satisfaction, and increased depression. This fear cycle further increases the risk of another fall. Especially for the elderly who live alone and independently, about 50% of the falls occur within their own home, so timely and automatic detection of falls has the potential to save the lives of the elderly [4].