Poster: LightWalk: Passive Gait Recognition via Reflected VLC Signals

Abstract

A visible light communication (VLC)-based sensing system is proposed for gait recognition and classification. Human-induced reflections are modeled using a time-varying channel representation, enabling the capture of gait dynamics without requiring wearable devices. A low-cost sensing module with embedded processing and multi-channel photodetectors is implemented. Filtered signals are transformed into spectral-spatial features and analyzed using deep learning models. Experiments involving ten participants across eight gait types demonstrate that contrastive and multi-scale models achieve over 98% accuracy, highlighting the potential of VLC-based sensing for unobtrusive, privacy-preserving, and real-time human gait recognition.