On Tracking the Physicality of Wi-Fi: A Subspace Approach

• Main Authors: Mohammed Alloulah, Anton Isopoussu, Chulhong Min, Fahim Kawsar
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Channel sensing; interpretable dimensionality reduction; machine learning; multiple-input multiple-output (MIMO)
• Online Access: https://ieeexplore.ieee.org/document/8636492/

Wi-Fi channel state information (CSI) has emerged as a plausible modality for sensing different human activities as a function of modulations in the wireless signal that travels between wireless devices. Until now, most research has taken a statistical approach and/or purpose-built inference pipeline. Although interesting, these approaches struggle to sustain sensing performances beyond experimental conditions. As such, the full potential of CSI as a general-purpose sensing modality is yet to be realized. We argue that a universal approach with the well-grounded formalization is necessary to characterize the relationship between the wireless channel modulations (spatial and temporal) and human movement. To this end, we present a formalism for quantifying the changing part of the wireless signal modulated by human motion. Grounded in this formalization, we then present a new subspace tracking technique to describe the channel statistics in an interpretable way, which succinctly contains the human modulated part of the channel. We characterize the signal and noise subspaces for the case of uncontrolled human movement and show that these subspaces are dynamic. Our results demonstrate that the proposed channel statistics alone can robustly reproduce the state-of-the-art application-specific feature engineering baseline, however, across multiple usage scenarios. We expect that our universal channel statistics will yield an effective general-purpose featurization of wireless channel measurements and will uncover opportunities for applying CSI for a variety of human sensing applications in a robust way.