iSleep 论文

摘要

The quality of sleep is an important factor in maintaining a healthy life style. To date, technology has not enabled personalized, in-place sleep quality monitoring and analysis. Current sleep monitoring systems are often difficult to use and hence limited to sleep clinics, or invasive to users, e.g., requiring users to wear a device during sleep. This paper presents iSleep -- a practical system to monitor an individual's sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, including body movement, couch and snore, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events based on carefully selected acoustic features, and tracks the dynamic ambient noise characteristics to improve the robustness of classification. We have evaluated iSleep based on the experiment that involves 7 participants and total 51 nights of sleep, as well the data collected from real iSleep users. Our results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.