AmFall: WiFi CSI Amplitude-Based Fall Detection Using Denoised Scalograms

Radio frequency (RF)-based fall detection systems have been actively studied over the past decade due to their low privacy concerns and convenient non-wearable nature. In particular, WiFi channel state information (CSI)-based solutions offer the additional benefit of requiring no specific hardware due to the widespread deployment of WiFi. However, WiFi CSI-based systems typically suffer from severe performance degradation in cross-domain scenarios, non-line-of-sight (NLOS) and/or through-the-wall (TTW) environments. For successful real-world deployment, a fall detection system must be able to provide reasonably good performance even in such poor conditions. In this paper, we propose a new WiFi CSI amplitude-based fall detection system (called AmFall) that effectively addresses the aforementioned challenges. To cope with NLOS/TTW situations, AmFall selectively uses the subcarriers and principal components containing valuable information. Considering that the continuous wavelet transform (CWT) is suitable for time-frequency analysis of non-stationary signals, such as those generated during falls, AmFall generates a scalogram by applying CWT to the resulting CSI signal with a novel denoising algorithm and extracts the speed information for segmentation. The segmented scalograms are fed into a deep learning-based classifier. In this paper, we also suggest a simple yet effective dataset augmentation method to generate multiple scalograms from a single CSI observation. The proposed system, implemented on commercial WiFi devices, achieves a high fall detection accuracy of 95.28% to 99.67% in TTW and cross-domain environments, outperforming state-of-the-art WiFi-based methods.