Room-scale 2D Passive Acoustic Tracking and Gait Recognition using a Smart Speaker

The rapid proliferation of the IoT and AI has heightened the demand for non-intrusive, high-precision sensing in smart home environments. Acoustic-based methods offer lower costs and enhanced privacy but often struggle to achieve high accuracy, long-range, and wide-area functionality. This paper presents EchoGuard, an acoustic-based system for room-scale user tracking and gait recognition using commercial smart speakers. EchoGuard adopts an OFDM-based based demodulation framework that exploits phase and amplitude variations across subcarriers to enable accurate 2D localization, effectively mitigating truncation effects in conventional acoustic localization methods. To support real-time deployment, EchoGuard further incorporates dimensionality reduction and pruning techniques, reducing the computational overhead of localization and motion imaging. By modeling continuous human motion and suppressing variations in user position and orientation, EchoGuard enables spatial-state-independent motion imaging for robust gait recognition. Experimental results show median localization errors of 0.18m and 0.16m in static and dynamic scenarios, respectively, and achieve a gait recognition accuracy of 79.2% across 10 users, consistently outperforming existing baseline approaches.