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題名 以影像為基礎之智慧型睡眠監測系統
Intelligent video-based sleep monitoring system作者 郭仁和
Kuo, Jen Ho貢獻者 廖文宏
Liao, Wen Hung
郭仁和
Kuo, Jen Ho關鍵詞 睡眠觀測
影像監控
智慧家庭生活
可適性背景模型
video-based sleep monitoring
sleep stages
smart living space
adaptive background modeling日期 2009 上傳時間 8-十二月-2010 12:02:55 (UTC+8) 摘要 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.參考文獻 [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.描述 碩士
國立政治大學
資訊科學學系
95753035
98資料來源 http://thesis.lib.nccu.edu.tw/record/#G0095753035 資料類型 thesis dc.contributor.advisor 廖文宏 zh_TW dc.contributor.advisor Liao, Wen Hung en_US dc.contributor.author (作者) 郭仁和 zh_TW dc.contributor.author (作者) Kuo, Jen Ho en_US dc.creator (作者) 郭仁和 zh_TW dc.creator (作者) Kuo, Jen Ho en_US dc.date (日期) 2009 en_US dc.date.accessioned 8-十二月-2010 12:02:55 (UTC+8) - dc.date.available 8-十二月-2010 12:02:55 (UTC+8) - dc.date.issued (上傳時間) 8-十二月-2010 12:02:55 (UTC+8) - dc.identifier (其他 識別碼) G0095753035 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/49468 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 95753035 zh_TW dc.description (描述) 98 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 1. Introduction 12. Related work 52.1. Traditional Approaches on Monitoring Sleep Quality 62.1.1. Acti-watch 72.1.2. Polysomnography 92.1.3. Questionnaire 113. Techniques in Video-based Sleep Monitoring 133.1. Near Infrared Images 133.2. Background Modeling 153.2.1. Consecutive Frames Subtraction 163.2.2. Gaussian Mixture Model 173.2.3. Local Binary Pattern 193.2.4. Local Ternary Pattern Model 213.3. Image Noise and Noise Removal 223.3.1. Gaussian Smooth Filter 253.3.2. Median Filter 273.3.3. Image Binarization 293.4. Motion History Image 313.4.1. Mechanism of Motion History Image 313.4.2. Features from Motion History Image 334. The iWakeUp System 354.1. System Architecture 364.1.1. Video Acquisition 374.1.2. Background Modeling 384.1.3. Noise Removal 414.1.4. Display Movement Areas 434.2. Wake-up Rule 434.3. A Time-Dependent Wake-up Rule 484.4. The iWakeUp User Interface 524.5. Experiments with Varying Lighting Conditions 544.5.1. Dataset Generation 554.5.1.1.Lightness Changes at Dawn 554.5.1.2.Adding Artificial Light 594.5.2. Experimental Results 604.5.2.1.Uniform Brightness Environment 614.5.2.2.Varying Brightness Environment 624.5.3. Discussion 635. Validation Results 655.1. Method 655.2. Results and Discussion 666. Conclusions and Future Work 68Nomenclature 70References 71 zh_TW dc.format.extent 3700165 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0095753035 en_US dc.subject (關鍵詞) 睡眠觀測 zh_TW dc.subject (關鍵詞) 影像監控 zh_TW dc.subject (關鍵詞) 智慧家庭生活 zh_TW dc.subject (關鍵詞) 可適性背景模型 zh_TW dc.subject (關鍵詞) video-based sleep monitoring en_US dc.subject (關鍵詞) sleep stages en_US dc.subject (關鍵詞) smart living space en_US dc.subject (關鍵詞) adaptive background modeling en_US dc.title (題名) 以影像為基礎之智慧型睡眠監測系統 zh_TW dc.title (題名) Intelligent video-based sleep monitoring system en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] Augusto, J. C., and Nugent, C. D., 2006, Designing Smart Homes: The Role of Artificial Intelligence, Springer, New York, NY, USA. zh_TW dc.relation.reference (參考文獻) [2] Avidan, A. Y., and Zee, P. C., 2006, Handbook of Sleep Medicine. Lippincott Williams& Wilkins, Philadelphia, PA, USA. zh_TW dc.relation.reference (參考文獻) [3] aXbo Company, “Sleep Phase Alarm Clock,” Retrieved at: http://www.axbo.com/ zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [6] Innovative Sleep Solutions LLC, 2009, “SLeepTrack watch” Available: http://www.sleeptracker.com/ zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [14] Tassi, P., and Muzet, A., 2000, “Sleep Inertia,” Sleep Medicine Reviews. Vol. 4 No. 4, pp. 341-353. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW dc.relation.reference (參考文獻) [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. zh_TW
