Model-Driven Learning Approach for Robust WiFi-based Fall Detection
Sai Deepika Regani, Beibei Wang, K. J. Ray Liu · 2025
Indoor falls often lead to fatalities due to delayed assistance. Current approaches to detecting indoor falls, such as cameras and wearables, intrude on privacy and are inconvenient. Radar-based device-free sensing has a limited range and requires dense deployment, leading to overhead costs. WiFi-based solutions, while promising, are currently either environment-dependent or insufficiently tested. In this work, we propose a fusion approach that leverages signal processing techniques to extract environment-independent features from the Channel State Information (CSI) in commercial WiFi devices. We then use a neural network to detect differentiating patterns from these features. Our lightweight LSTM network, with just 21,000 parameters, has been tested on 2,400 fall events from over 25 volunteers in 5 environments. It has also undergone 21 months of false alarm testing in 6 diverse settings. The system achieves a 94.1% detection rate and fewer than 5 false alarms per month in single-person homes.