Abstract
The quality of sleep may be a reflection of an elderly individual’s health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novelmulti-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user’s sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user’s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operateswithout disrupting the users’ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.
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Hongbo Ni is an associate professor from the School of Computer Science, Northwestern Polytechnical University, China. He received his BS and PhD from Northwestern Polytechnical University, and worked as a research fellow in the Department of Information Systems, Sherbrooke University, Canada from 2009 to 2010. He has published more than 40 academic papers, served as publicity/session chair and program member for a number of conferences. His research interests include pervasive computing, embedded computing and system.
Shu Wu received his BS from Hunan University, China in 2004, and his MS from Xiamen University, China in 2007, both in computer science. He received his PhD from Department of Computer Science, University of Sherbrooke, Canada. He has joined Center for Research on Intelligent Perception and Computing at National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, where he works as an Assistant Professor. His research interests include data mining, information retrieval and recommendation systems.
Bessam Abdulrazak is an associate professor in the Department of Information Systems, Sherbrooke University, Canada. He received his PhD from national telecommunication university in Paris, France. He has published more than 100 academic papers, and served as general chair for a number of conferences. His research interests include Human-Computer Interaction, pervasive computing.
Daqing Zhang is a professor from Institut Mines-Telecom of Telecom SudParis, France. He obtained his PhD from University of Rome “La Sapienza”, Italy in 1996. He is the associate editor of four journals including ACM Transactions on Intelligent Systems and Technology, Journal of Ambient Intelligence and Humanized Computing, etc. He also served in the technical committee for conferences such as UbiComp, Pervasive, PerCom, etc. His research interests include ubiquitous computing, context-aware computing, big data analytics, and social computing.
Xiaojuan Ma is a Researcher at Huawei Noah’s Ark Lab. Prior to that, she was a Computing Innovation Post-doc Fellow in Human-Computer Interaction Institute, Carnegie Mellon University, USA. She received her bachelor’s degree from Tsinghua University, China, and PhD from Princeton University, USA, and worked as a research fellow in the Department of Information Systems, National University of Singapore, Singapore in 2010. Dr. Ma’s background is in Human-Computer Interaction. She is particularly interested in multimedia-augmented communication in both human-human and human-robot interactions, design, visual/auditory perception, and (computational) linguistics.
Xingshe Zhou is a professor from the School of Computer Science, Northwestern Polytechnical University, China. He is the director of Shaanxi Key Laboratory of Embedded System Technology, China. He has published more than 100 academic papers, and served as general chair or program chair for a number of conferences. His research interests include embedded computing, distributed realtime computing, and pervasive computing.
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Ni, H., Wu, S., Abdulrazak, B. et al. Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment. Front. Comput. Sci. 9, 966–979 (2015). https://doi.org/10.1007/s11704-015-4404-7
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DOI: https://doi.org/10.1007/s11704-015-4404-7