學術產出-學位論文
| 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 1 2. Related work 5 2.1. Traditional Approaches on Monitoring Sleep Quality 6 2.1.1. Acti-watch 7 2.1.2. Polysomnography 9 2.1.3. Questionnaire 11 3. Techniques in Video-based Sleep Monitoring 13 3.1. Near Infrared Images 13 3.2. Background Modeling 15 3.2.1. Consecutive Frames Subtraction 16 3.2.2. Gaussian Mixture Model 17 3.2.3. Local Binary Pattern 19 3.2.4. Local Ternary Pattern Model 21 3.3. Image Noise and Noise Removal 22 3.3.1. Gaussian Smooth Filter 25 3.3.2. Median Filter 27 3.3.3. Image Binarization 29 3.4. Motion History Image 31 3.4.1. Mechanism of Motion History Image 31 3.4.2. Features from Motion History Image 33 4. The iWakeUp System 35 4.1. System Architecture 36 4.1.1. Video Acquisition 37 4.1.2. Background Modeling 38 4.1.3. Noise Removal 41 4.1.4. Display Movement Areas 43 4.2. Wake-up Rule 43 4.3. A Time-Dependent Wake-up Rule 48 4.4. The iWakeUp User Interface 52 4.5. Experiments with Varying Lighting Conditions 54 4.5.1. Dataset Generation 55 4.5.1.1.Lightness Changes at Dawn 55 4.5.1.2.Adding Artificial Light 59 4.5.2. Experimental Results 60 4.5.2.1.Uniform Brightness Environment 61 4.5.2.2.Varying Brightness Environment 62 4.5.3. Discussion 63 5. Validation Results 65 5.1. Method 65 5.2. Results and Discussion 66 6. Conclusions and Future Work 68 Nomenclature 70 References 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 |