FallAware: An Explainable Learning Approach to Robust Fall Detection With WiFi

Indoor falls have proved fatal to many people due to a lack of timely assistance. Existing approaches for fall detection using cameras and wearable devices intrude on privacy and cause inconvenience. Passive sensing approaches using radar have limited coverage and demand dense deployment. Current solutions using commercial off-the-shelf (COTS) WiFi devices are either environment-dependent or lack extensive testing in real environments to confidently assess false alarm rates. In this work, we propose a fusion approach to detect falls with COTS WiFi, where we leverage signal processing techniques to extract environment-independent features, and use a neural network to detect differentiating patterns in those features. We designed a lightweight long short-term memory-based neural network with only 21 k parameters that can easily be deployed on edge devices. We further provide a framework to explain the network's behavior that supports a calibration-free design. Our proposed FallAware system's detection performance has been extensively tested on $\sim$2400 falls gathered from over 25 volunteers in 5 different environments. In addition, we conducted long-term false alarm testing in 6 diverse environments for a total duration of 21 months. The results show that FallAware can detect falls with an average detection rate of 94.1% in unseen environments with $< $5 false alarms per month in single-person occupancy homes.