PhaseAnti: an Anti-interference WiFi-based Activity Recognition System Using Interference-Independent Phase Component

Driven by a wide range of essential applications, significant achievements are made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf WiFi infrastructures to infer human activities without the need for subjects to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios.