Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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International Journal of Human-Computer Studies
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This paper conducts a preliminary study in which sleeping behavior is predicted using long-term activity data from a wearable sensor. For this purpose, two scenarios are scrutinized: The first predicts sleeping behavior using a day-of-the-week model. In a second scenario typical sleep patterns for either working or weekend days are modeled. In a continuous experiment over 141 days (6 months), sleeping behavior is characterized by four main features: the amount of motion detected by the sensor during sleep, the duration of sleep, and the falling asleep and waking up times. Prediction of these values can be used in behavioral sleep analysis and beyond, as a component in healthcare systems.