  Vital sign (breathing and heartbeat) monitoring is essential for patient care
and sleep disease prevention. Most current solutions are based on wearable
sensors or cameras; however, the former could affect sleep quality, while the
latter often present privacy concerns. To address these shortcomings, we
propose Wital, a contactless vital sign monitoring system based on low-cost and
widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two
challenges that need to be overcome. First, the torso deformations caused by
breathing/heartbeats are weak. How can such deformations be effectively
captured? Second, movements such as turning over affect the accuracy of vital
sign monitoring. How can such detrimental effects be avoided? For the former,
we propose a non-line-of-sight (NLOS) sensing model for modeling the
relationship between the energy ratio of line-of-sight (LOS) to NLOS signals
and the vital sign monitoring capability using Ricean K theory and use this
model to guide the system construction to better capture the deformations
caused by breathing/heartbeats. For the latter, we propose a motion
segmentation method based on motion regularity detection that accurately
distinguishes respiration from other motions, and we remove periods that
include movements such as turning over to eliminate detrimental effects. We
have implemented and validated Wital on low-cost COTS devices. The experimental
results demonstrate the effectiveness of Wital in monitoring vital signs.

Human-Computer Interaction