An Active Sleep Monitoring Framework Using Wearables
An Active Sleep Monitoring Framework Using Wearables
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DOI：
10.1145/3185516
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
H. M. S. Hossain;S. R. Ramamurthy;Md Abdullah Al Hafiz Khan;Nirmalya Roy
中科院分区：
文献类型：
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作者：
H. M. S. Hossain;S. R. Ramamurthy;Md Abdullah Al Hafiz Khan;Nirmalya Roy
睡眠是健康和活跃的生活的最重要的方面。日常生活的活动（ADL），睡眠对一个人的能力，行为和认知健康具有重大的协同作用。环境（例如眼睛或身体运动）是设计和开发强大的智能睡眠监测系统所必需的。行为，因此有助于塑造我们的初始分析的形成性评估。总体睡眠行为。在大规模的部署中，我们提出了一个基于学习的方法，以减少基于学习的方法标签。
Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person’s fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, 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 to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.