Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Opportunities for computing to support healthy sleep behavior
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Monitoring body positions and movements during sleep using WISPs
WH '10 Wireless Health 2010
A Non-invasive Wearable Neck-Cuff System for Real-Time Sleep Monitoring
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Lullaby: a capture & access system for understanding the sleep environment
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
StressSense: detecting stress in unconstrained acoustic environments using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Will you have a good sleep tonight?: sleep quality prediction with mobile phone
Proceedings of the 7th International Conference on Body Area Networks
PhoneLab: A Large Programmable Smartphone Testbed
Proceedings of First International Workshop on Sensing and Big Data Mining
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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.