VibraGait: Multi-User Gait Recognition based on Footstep-Induced Floor Vibrations via mmWave

Abstract

Gait recognition has become an increasingly attractive and prevalent technology in ubiquitous computing. Recent works have explored radio frequency (RF) signals to directly sense walking postures for non-invasive and privacy-preserving gait recognition. However, these approaches face fundamental limitations including environment-dependent nature and line-of-sight (LoS) constraints. To overcome the above limitations, we consider leveraging footstep-induced mechanical signals (e.g., vibration) on the floor for gait recognition. In this paper, we present a multi-user gait recognition system, VibraGait, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense footstep-induced floor vibrations for identifying individuals. VibraGait first separates mmWave-sensed vibrations from different users using a FaSNet-based neural beamforming model in multi-user scenarios, and then constructs individual vibration patterns using a pulse-response model. Furthermore, VibraGait extracts path-invariant vibration patterns by a vibration pattern standardization method to ensure robustness across arbitrary walking paths. Finally, VibraGait extracts distinctive gait features and recognizes users’ identities using a ConvNeXt model. Extensive experiments in real-world environments show that VibraGait effectively identifies users in multi-user scenarios with an average accuracy of 95.5%.