  WiFi channel state information (CSI) has emerged as a plausible modality for
sensing different human vital signs, i.e. respiration and body motion, as a
function of modulated wireless signals that travel between WiFi devices.
Although a remarkable proposition, most of the existing research in this space
struggles to withstand robust performance beyond experimental conditions. To
this end, we take a careful look at the dynamics of WiFi signals under human
respiration and body motions in the wild. We first characterize the WiFi signal
components - multipath and signal subspace - that are modulated by human
respiration and body motions. We extrapolate on a set of transformations,
including first-order differentiation, max-min normalization and component
projections, that faithfully explains and quantifies the dynamics of
respiration and body motions on WiFi signals. Grounded in this
characterization, we propose two methods: 1) a respiration tracking technique
that models the peak dynamics observed in the time-varying signal subspaces and
2) a body-motion tracking technique built with a multi-dimensional clustering
of evolving signal subspaces. Finally, we reflect on the manifestation of these
techniques in a practical sleep monitoring application. Our systematic
evaluation with over 550 hours of data from 5 users covering both line-of-sight
(LOS) and non-line-of-sight (NLOS) settings shows that the proposed techniques
can achieve comparable performance to purpose-built pulse-Doppler radar.
