Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling
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Date
2015-09-14
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Citation of Original Publication
H. M. S. Hossain, N. Roy and M. A. A. H. Khan, "Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling," 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, 2015, pp. 44-53.
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
Abstract
Following healthy lifestyle is a key for active living. Regular exercise, controlled diet and sound sleep play an invisible role on the well being and independent living of the people. Sleep being the most durative activities of daily living (ADL) has a major synergistic influence on people's mental, physical and cognitive health. Understanding the sleep behavior longitudinally and its underpinning clausal relationships with physiological signals and contexts (such as eye or body movement etc.) horizontally responsible for a sound or disruptive sleep pattern help provide meaningful information for promoting healthy lifestyle and designing appropriate intervention strategy. In this paper we propose to detect the microscopic states of the sleep which fundamentally constitute the components of a good or bad sleeping behavior and help shape the formative assessment of sleep quality. We initially investigate several classification techniques to identify and correlate the relationship of microscopic sleep states with the overall sleep behavior. Subsequently we propose an online algorithm based on change point detection to better process and classify the microscopic sleep states and then test a lightweight version of this algorithm for real time sleep monitoring activity recognition and assessment at scale. For a larger deployment of our proposed model across a community of individuals we propose an active learning based methodology by reducing the effort of ground truth data collection. We evaluate the performance of our proposed algorithms on real data traces, and demonstrate the efficacy of our models for detecting and assessing fine-grained sleep states beyond an individual.