Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

Abstract

Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system’s performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

“…Vision-based methods eliminate the need to wear something, but they are costly, sensitive to the lighting conditions, and invade privacy [ 5 , 6 ]. Recently, radar sensors have become more popular in fall detection system due to the advantages compared with other sensing technologies: (a) convenience over wearable technologies [ 7 ]; (b) high sensitivity to motion compared to depth sensors in complex living environments and weak lighting conditions; (c) privacy compliance over vision sensors [ 8 ]; and (d) low hardware cost compared with other sensors [ 9 ]. Typical radars for human fall detection are continuous wave (CW) radars and frequency-modulated continuous wave (FMCW) radars.…”

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

“…For illustrating the enhanced indoor monitoring results of the IBWOA-FIMS technique, a series of simulations were performed. Cardenas et al (2023) followed a technique to characterize fall events based on the Doppler sign imprinted on the CW probe signals by falling persons. Two neural network (NN) methods are adopted.…”