Multimodality Sensors for Sleep Quality Monitoring and Logging

Abstract

In this paper, we investigate the possibility of using simple multimodality sensors to automatically detect a person’s sleep condition. We propose a system which consists of heart-rate, video, and audio sensors, and apply machine learning methods to infer the sleep-awake condition during the time a user spends on the bed. The sleep-awake conditions will be useful information for inferring sleep latency and sleep efficiency, which are critical to both sleep-related diseases and sleep quality measurements. To eliminate possible privacy concerns, we further explore the feasibility of using passive infrared (PIR) sensor instead of video sensor for motion information acquisition. Our experimental results are promising and show the potential use of the proposed novel economical alternative to the traditional medical measurement equipment, with competitive performance on the sleeprelated activity monitoring and the sleep quality measurements.