  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.
