Noninvasive Fine-Grained Sleep Monitoring Leveraging Smartphones

摘要

Sleep monitoring has drawn increasing attention as sleep quality is important to maintain a person's well-being. For instance, serious health problems, such as cardiovascular disease, fatigue, or depression, are usually associated with inadequate and irregular sleep. Traditional sleep monitoring systems involve wearable sensors with professional installation, and thus are usually limited to clinical usage. Recent work for sleep monitoring can detect several sleep events, such as coughing and snoring, using smartphone sensors. However, such coarse-grained sleep monitoring is unable to detect the breathing rate which is an important health indicator. In this paper, we present a fine-grained sleep monitoring system to detect the breathing rate and sleep events simultaneously by leveraging smartphones. Our system exploits the readily available smartphone earphone placed close to the user to reliably capture the human breathing sound. Given the captured acoustic sound, noise reduction is performed to remove the environmental noise and the breathing rate is then identified based on the signal envelope detection. Our system can further detect some sleep events, including snoring, coughing, turning over, and getting up, based on the features extracted from the acoustic sound. Moreover, we develop a body move-mentassisted sleep event detection method to provide higher detection accuracy by further exploiting the user's body movement patterns captured by the accelerometer embedded on smartphones. Our extensive experiments involving nine subjects over six months confirm the effectiveness of our proposed system on breathing rate monitoring and sleep events detection under various environments. By combining breathing rate and sleep events, our system can provide noninvasive and continuous fine-grained sleep monitoring for healthcare related applications, such as sleep apnea monitoring, as evidenced by our experimental study.