Sleepy: Wireless Channel Data Driven Sleep Monitoring via Commodity WiFi Devices

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sensor-based or vision-based sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. With the fast expansion of wireless infrastructures nowadays, channel data, which is pervasive and transparent, emerges as another alternative. To this end, we propose Sleepy, a wireless channel data driven sleep monitoring system leveraging commercial WiFi devices. The key idea of Sleepy is that the energy feature of the wireless channel follows a Gaussian Mixture Model (GMM) derived from the accumulated channel data over a long period. Therefore, a GMM based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures), leading to certain major merits, e.g., no calibrations or target-dependent training needed. We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.65 percent detection accuracy (DA) and 2.16 percent false negative rate (FNR) on average. In the 60-minute real sleep studies, Sleepy demonstrates strong stability, i.e., 0 percent FNR and 98.22 percent DA. Considering that Sleepy is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for sleep monitoring.