政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49468
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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/49468

    Title: 以影像為基礎之智慧型睡眠監測系統
    Intelligent video-based sleep monitoring system
    Authors: 郭仁和
    Kuo, Jen Ho
    Contributors: 廖文宏
    Liao, Wen Hung
    Kuo, Jen Ho
    Keywords: 睡眠觀測
    video-based sleep monitoring
    sleep stages
    smart living space
    adaptive background modeling
    Date: 2009
    Issue Date: 2010-12-08 12:02:55 (UTC+8)
    Abstract: 我們提出的智慧型睡眠監測系統,是基於影像分析技術進行睡眠品質觀測,並利用所得到的數據來推斷最佳的喚醒時間。此系統命名為iWakeUp,利用非接觸式的方法來收集影像資料並進行後續處理,此裝置將被安裝在一般的臥室來幫助睡眠者,以期成為增進智慧家庭生活品質的一環。在此論文中,我們將會描述iWakeUp的各個模組包括測定動作量、推斷睡眠階段乃至於如何建立喚醒機制。更特別的是,我們考慮了喚醒時間與喚醒機制的關係,於較早的時間喚醒必須具有更高的信心度,否則將付出較大的代價,反之亦然。另外為了處理晨間臥室中的光影變化,不同的背景模型也已被整合測試,以期讓系統可以提升長時間觀測的準確度。最後,我們也進行了使用iWakeUp的臨床實驗,結果指出使用iWakeUp喚醒的睡眠者具有較低的嗜睡感與更好的活力。
    We present a video-based monitoring system to determine the sleep status and optimal wakeup time in this thesis. We envision a smart living space in which a data collection and processing module named iWakeUp is installed in the bedroom to record and monitor sleep in a non-invasive manner. We describe the overall structure of the iWakeUp system, including the procedure to measure amount of motion, the method for inferring wake/sleep status from the acquired video and the logics for deciding the optimal wakeup time. In particular, a time-dependent decision rule has been incorporated to account for unequal penalties when classification error occurs. Furthermore, various background modeling techniques have been examined to address lighting changes at dawn in the bedroom for long-term monitoring. Validation experiments are carried out to compare the alertness level upon awakening with/without reported a lower level of sleepiness and higher level of vigorousness in comparison to the control group.
    Reference: [1] Augusto, J. C., and Nugent, C. D., 2006, Designing Smart Homes: The Role of Artificial Intelligence, Springer, New York, NY, USA.
    [2] Avidan, A. Y., and Zee, P. C., 2006, Handbook of Sleep Medicine. Lippincott Williams& Wilkins, Philadelphia, PA, USA.
    [3] aXbo Company, “Sleep Phase Alarm Clock,” Retrieved at: http://www.axbo.com/
    [4] Ferrara, M., and De Gennaro, L., 2000, “The Sleep Inertia Phenomenon during the Sleep-Wake Transition: Theoretical and Operational Issues,” Journal of Aviation, Space, and Environmental Medicine, Vol. 71, No. 8, pp. 843-848.
    [5] Heikkila, M., Pietikainen, M., 2006, “A Texture-Based Method for Modeling the Background and Detecting Moving Objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 657-662.
    [6] Innovative Sleep Solutions LLC, 2009, “SLeepTrack watch” Available: http://www.sleeptracker.com/
    [7] Jewett, M. E., Wyatt, J. K., Ritz-De Cecco, A., Khalsa, S. B., Dijk, D. J., and Czeisler, C. A., 1999, “Time Course of Sleep Inertia Dissipation in Human Performance and Alertness,” Journal of Sleep Research, Vol.8, No. 1, pp. 1-8.
    [8] Liao, W. H., Kuo, J. H., and Yang, C. M., 2009, “iWakeUp: An Intelligent Video-Based Alarm Clock,” Proceedings of the 2009 Intelligent Buildings and Smart Homes Conference, Taipei, Taiwan, pp. 136-139.
    [9] Liao, W. H., and Yang, C. M., 2008, “Video-based Activity and Movement Pattern Analysis in Overnight Sleep Studies,” Proceedings of the 19th International Conference on Pattern Recognition, Tampa, Florida, USA, pp. 1-4.
    [10] Piccardi, M., 2004, “Background Subtraction Techniques: A Review,” Proceedings of IEEE SMC 2004 International Conference on Systems, Man and Cybernetics, Hague, Netherlands, Vol. 4, pp. 3099-3104.
    [11] Rechtschaffen, A., Kales, A., 1968, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, UCLA Brain Information Service/Brain Research Institute, Los Angeles, CA, USA.
    [12] Sivan, Y., Kornecki, A., and Schonfeld, T., 1996, “Screening Obstructive Sleep Apnea Syndrome by Home Videotape Recording in Children,” European Respiratory Journal, Vol. 9, pp. 2127-2131.
    [13] Stauffer, C., and Grimson, W. E. L., 1999, “Adaptive Background Mixture Models for Real-time Tracking,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 246-252.
    [14] Tassi, P., and Muzet, A., 2000, “Sleep Inertia,” Sleep Medicine Reviews. Vol. 4 No. 4, pp. 341-353.
    [15] Wang, C. W., Ahmed, A., and Hunter, A., 2007, “Locating the Upper Body of Covered Humans in Application to Diagnosis of Obstructive Sleep Apnea,” Proceedings of World Congress on Engineering 2007 - International Conference of Signal and Image Engineering, Vol. 1, pp. 662-667.
    [16] Wong, J. K. W., Li, H., and Wang, S. W., 2005, “Intelligent Building Research: A Review,” Automation in Construction, Vol. 14, No. 4, pp. 143-159.
    [17] Yang, F. C., Kuo, C. H., Tsai, M. Y., and Huang, S. C., 2003, “Image-Based Sleep Motion Recognition Using Artificial Neural Networks,” Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, Vol. 5, pp. 2775-2780.
    [18] 葉在庭, 康仕仲, 江秉穎, 江佳璇, “Sleep Coach 篩選介面:依據ICSD-II 建構中文版失眠篩選問卷”, 台灣睡眠醫學年會 98 年度會員大會暨第七屆學術研討會, 台北, 台灣, Mar. 2009.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095753035
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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