On Goodness of WiFi Based Monitoring of Sleep Vital Signs in the Wild

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 subspace and 2) a body-motion tracking technique built with a multi-dimensional clustering of evolving signal subspace. 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.