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 realised. We argue a universal approach with well-grounded formalisation is necessary to characterise the relationship between 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 formalisation, 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 characterise the signal and noise subspaces for the case of uncontrolled human movement, and show that these subspaces are dynamic. Our results demonstrate that proposed channel statistics alone can robustly reproduce state-of-the-art application-specific feature engineering baseline, however, across multiple usage scenarios. We expect, our universal channel statistics will yield an effective general-purpose featurisation of wireless channel measurements and will uncover opportunities for applying CSI for a variety of human sensing applications in a robust way.