Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

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• 作者：Hongbo Ni ; Shu Wu ; Bessam Abdulrazak ; Daqing Zhang…
• 关键词：sleep pattern ; elder ; care ; pressure sensor ; UWB tags ; Na?ve Bayes ; Random Forest
• 刊名：Frontiers of Computer Science in China
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：9
• 期：6
• 页码：966-979
• 全文大小：700 KB
• 参考文献：1.Foley D, Ancoli-Israel S, Britz P, Walsh J. Sleep disturbances and chronic disease in older adults: results of the 2003 national sleep foundation sleep in America survey. Journal of Psychosomatic Research, 2004, 56(5): 497-02CrossRef
  2.Foley D, Monjan A A, Brown S L, Simonsick E M, Robert B W, Dan G B. Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep, 1995, 18(6): 425-32
  3.Cheng S P, Mei H. A personalized sleep quality assessment mechanism based on sleep pattern analysis. In: Proceedings of the 3rd IEEE International Conference on Innovations in Bio-Inspired Computing and Applications. 2012, 133-38
  4.Lutz W, Sanderson W, Scherbov S. The coming acceleration of global population ageing. Nature, 2008, 451(7179): 716-19CrossRef
  5.Matt S. China’s big (and growing) problem with its elderly population. The Atlantic, 2013, 6
  6.Cheng C M, Hsu Y L, Young C M, Wu C H. Development of a portable device for tele-monitoring of snoring and OSAS symptoms. Telemedicine and e-HEALTH, 2008, 14(1): 55-8CrossRef
  7.Zimmermann M, Toni I, de Lange Floris P. Body posture modulates action perception. The Journal of Neuroscience, 2013, 33(14): 5930-938CrossRef
  8.Vaughn M W, Boggs N, Letton A. Changes in sleep and wake in response to different sleeping surfaces: a pilot study. Applied Ergonomics, 2012, 43(2): 386-91CrossRef
  9.Kambayashi Y, Hagiwara H. Estimating sleep cycle using body movement density. In: Proceedings of the 5th IEEE International Conference on Biomedical Engineering and Informatics. 2012, 1081-085
  10.Bhadoria S S, Gupta H. A wearable personal healthcare and emergency information based on mobile application. International Journal of Scientific Research in Computer Science and Engineering, 2013, 1(04): 24-0
  11.Wang P C. Screening instruments of sleep disorders: actigraphy. Intelligent Systems, Control and Automation: Science and Engineering, 2012, 33-8
  12.Elbaz M, Yauy K, Metlaine A, Martoni M, Léger D. Validation of a new actigraph motion watch versus polysomnography on 70 healthy and suspected sleep-disordered subjects. Journal of Sleep Research, 2012, 21(111): 218
  13.Warren W T. Issues of validity in actigraphic sleep assessment. Sleep, 2004, 27(1): 158-65MathSciNet
  14.Terrill P I, Leong M, Barton K, Freakley C, Downey C, Vanniekerk M, Jorgensen G, Douglas J. Measuring leg movements during sleep using accelerometry: comparison with EMG and piezo-electric scored events. In: Proceedings of the 35th IEEE Annual International Conference on Engineering in Medicine and Biology Society. 2013, 6862-865
  15.Cecilia O, Gaetano M. RFID technology for the neuroscience: feasibility of sleep disease monitoring. In: Proceedings of the 3rd IEEE European Conference on Antennas and Propagation. 2009, 2326-330
  16.Paolo B. Position recognition to support bedsores prevention. IEEE Biomedical and Health Informatics, 2013, 17(1): 53-9CrossRef
  17.Nikamalfard H, Zheng H, Wang H, Mulvenna M, McCullagh P, Jeffers P. A sleep pattern analysis and visualization system to support people with early dementia. In: Proceedings of the 5th IEEE International Conference on Pervasive Computing Technologies for Healthcare. 2011, 510-13
  18.Jukka K, Ilpo K, Pekka P. Long-term monitoring of movements in bed and their relation to subjective sleep quality. Sleep and Hypnosis, 2003, 5: 145-53
  19.Van der Loos H F M, Nino U, Hisato K. Development of sensate and robotic bed technologies for vital signs monitoring and sleep quality improvement. Autonomous Robots, 2003, 15(1): 67-9CrossRef
  20.Huang W M, Wai A A P, Biswas J, Foo S F, Hsia C C, Liou K. Multimodal Situational Awareness for Eldercare. In: Proceedings of the 8th International Conference on Smart Homes and Health Telematics. 2010, 85-3
  21.Gaddam A, Kaur K, Gupta G S, Mukhopadhyay S C. Determination of sleep quality of inhabitant in a smart home using an intelligent bed sensing system. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference. 2010, 1613-617
  22.Zhu X, Chen WX, Nemoto T, Kitamura K, Wei D M. Long-term monitoring of heart rate, respiration rhythm, and body movement during sleep based upon a network. Telemedicine and e-Health, 2010, 16(2): 244-53CrossRef
  23.Witt J, Narbonneau F, Schukar M, Krebber K, De Jonckheere J, Jeanne M, Kinet D, Paquet B, Depré A, D’Angelo L T, Thiel T, Logier R. Medical textiles with embedded fiber optic sensors for monitoring of respiratory movement. Sensors Journal, 2012, 12(1): 246-54CrossRef
  24.Tang Y W, Lin Y D, Lin Y T, Chakhap J. Using conductive fabric for capacitive EEG measurements. IETE Technical Review, 2013, 30(4): 295-02CrossRef
  25.Hadiseh M, Juan R C, Giuseppe G, Miguel A G G, Xavier R. A wireless under-mattress sensor system for sleep monitoring in people with major depression. Biomedical Engineering, 2012, 9(1): 395
  26.Fu J B, Pan S L. UWB-over-fiber sensor network for accurate locali
• 作者单位：Hongbo Ni (1)
  Shu Wu (2)
  Bessam Abdulrazak (3)
  Daqing Zhang (4)
  Xiaojuan Ma (5)
  Xingshe Zhou (1)
  
  1. School of Computer Science, Northwestern Polytechnic University, Xi’an, 710072, China
  2. Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing, 100090, China
  3. Department of Computer, University of Sherbrooke, Sherbrooke, JIK2R1, Canada
  4. Handicom Lab, Institut Telecom SudParis, Evry, 91011, France
  5. Huawei Noah’s Ark Lab, Hong Kong, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

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. Keywords sleep pattern elder-care pressure sensor UWB tags Na?ve Bayes Random Forest