Sleep condition inferencing using simple multimodality sensors

In this paper, we investigate the possibility of using simple multimodality sensors to automatically detect a person's sleep condition. Sleep latency and sleep efficiency are critical to both sleep-related diseases and sleep quality measurements. 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 the sleep quality. 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 sleep-related activity monitoring and the sleep quality measurements