Abstract
High-quality sleep is essential to our daily lives, and real-time
monitoring of vital signs during sleep is beneficial. Current sleep
monitoring solutions are mostly based on wearable sensors or cameras,
the former is worse for sleep quality, the latter is worse for privacy,
dissimilar to such methods, we implement our sleep monitoring system
based on COTS WiFi devices. There are two challenges need to be overcome
in the system implementation process: First, the torso deformation
caused by breathing/heartbeat is weak, how to effectively capture this
deformation? Second, movements such as turning over will affect the
accuracy of vital signs monitoring, how to quickly distinguish such
movements? For the former, we propose a motion detection capability
enhancement method based on Rice-K theory and Fresnel theory. For the
latter, we propose a sleep motion positioning algorithm based on
regularity detection. The experimental results indicated the performance
of our method.