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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪一平 | |
dc.contributor.author | Jia-Lin Liou | en |
dc.contributor.author | 劉嘉麟 | zh_TW |
dc.date.accessioned | 2021-06-16T17:27:39Z | - |
dc.date.available | 2012-08-17 | |
dc.date.copyright | 2012-08-17 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-16 | |
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[2] M. Filippelli, R. Pellegrino, I. Iandelli, G. Misuri, J. R. Rodarte, R. Duranti, V. Brusasco, and G. Scano, “Respiratory dynamics during laughter,” J. Appl. Physiol. 90(4), 1441–1446 , 2001. [3] A. D. Groote, Y. Verbandt, M. Paiva, and P. Mathys, “Measurement of thoracoabdominal asynchrony: importance of sensor sensitivity to cross-section deformations,” J. Appl. Physiol. 88(4), 1295–1302, 2000. [4] Thought Technology Ltd, http://www.thoughttechnology.com, 2010. [5] J. H. Houtveen, P. F. Groot, and E. J. Geus, “Validation of the thoracic impedance derived respiratory signal using multilevel analysis,” Int. J. Psychophysiol, vol. 59, pp. 97-106, Feb. 2006. [6] V.-P. Seppa, J. Viik, and J. Hyttinen, “Assessment of Pulmonary Flow Using Impedance Pneumography,” IEEE Trans Biomed Eng, vol. 57, no. 9, pp. 2277- 2285, Sep. 2010. [7] G. B. Moody, R. G. Mark, A. Zoccola, and S. Mantero, “Derivation of Respiratory Signals from Multi-lead ECGs,” Computers in Cardiology, vol. 12, pp. 113-116, 1985. [8] P. Corbishley, and E. Rodriguez-Villegas, “Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System,” IEEE Transactions on Biomedical Engineering, vol. 55, pp. 196-204, Jan. 2008. [9] V. P. Harper, H. Pasterkamp, H. Kiyokawa, and G. R. Wodicka, “Modeling and measurement of flow effects on tracheal sounds,” IEEE Trans. Biomed. Eng., vol. 50, no. 1, pp. 1-10, Jan. 2003. [10] A. D. Groote, Y. Verbandt, M. Paiva, and P. Mathys, “Measurement of thoracoabdominal asynchrony: importance of sensor sensitivity to cross-section deformations,” J. Appl. Physiol, vol. 88, no. 4, pp. 1295–1302, Apr. 2000. [11] S. Levine, D. Silage, D. Henson, J. Y. Wang, J. Krieg, J. LaManca, and S. Levy, “Use of a triaxial magnetometer for respiratory measurements,” J. Appl. Physio, vol. 70, no. 5, pp. 2311–2321, May 1991. [12] A. Singh, V. Lubecke, and O. Boric-Lubecke, “Pulse Pressure Monitoring Through Non-Contact Cardiac Motion Detection Using 2.45 GHz Microwave Doppler Radar”, in Proc. of Engineering in Medicine and Biology Society, pp. 4336 – 4339, Boston, 2011. [13] D. Devis, G. Gilberto, L. Guido, P. Massimiliano, A. Carlo, B. Sergio, C. Gianna, C. Walter, M. Massimo, and L. D. Juri, “Non-Contact Detection of Breathing Using a Microwave Sensor,“ Sensors, vol. 9, no.4, pp. 2574-2585, Apr. 2009. [14] Y. Chekmenev, H. Rara, A. Farag, “Non-contact, wavelet-based measurement of vital signs using thermal imaging,” Int. J. of Graph Vision Image Process, vol. 6, pp. 25-30, 2006. [15] R. Wareham, J. Lasenby, J. Cameron, P. D. Bridge, and R. Iles, “Structured light plethysmography (SLP) compared to spirometry: a pilot study,” European Respiratory Society Annual Congress, Vienna, 2009, pp. A38-A41. [16] H. Aoki, K. Koshiji, H. Nakamura, Y. Takemura, and M. Nakajima, “Study on respiration monitoring method using near-infrared multiple slit-lights projection,” IEEE International Symposium on Micro-NanoMechatronics and Human Science, Japan, 2005, pp. 291-296. [17] Huijun Chen; Y Cheng; Dongdong Liu; Xiaodong Zhang; Jue Zhang; Chengli Que; Guangfa Wang; Jing Fang Color structured light system of chest wall motion measurement for respiratory volume evaluation. Journal of biomedical optics 2010;15(2):026013. [18] A. Aliverti, R. L. Dellaca, R. Pelosi, D. Chiumello, A. Pedotti, and L. Gatinoni, “Opto-electronic plethysmography in intensive care patients,” Am J. Resp. Critic Care Med., vol. 161, pp. 1546-1552, May 2000. [19] S. J. Cala, C. M. Kenyon, and G. Ferrigno, “Chest wall and lung volume estimation by optical reflectance motion analysis,” J. Appl. Physiol, vol. 81, pp. 2680–2689, Dec. 1996. [20] J. Penne, C. Schaller, J. Hornegger, T. Kuwert: Robust real-time 3D respiratory motion detection using Time-of-flight cameras. International Journal of Computer Assisted Radiology and Surgery, 2008. [21] M. Alnowami, B. Alnwaimi, M. Copland and K. Wells, “A quantitative assessment of using the Kinect for Xbox360TM for respiratory surface motion tracking,” SPIE Medical Imaging Proceeding, San Diego, California, USA, February 5-9, 2011. [22] A. Aliverti, R. Dellacà, P. Pelosi, D. Chiumello, L. Gattinoni, and A. Pedotti, “Compartmental analysis of breathing in the supine and prone position by optoelectronic plethysmography,” Ann. Biomed. Eng., vol. 29, no. 1, pp. 60–70, 2004. [23] M. Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in non-contact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomed. Eng., vol. 58, pp. 7-11, Jan. 2011. [24] OEP, http://www.optoelectronic-plethysmography.com/, 2012. [25] Kinect, Microsoft, http://www.xbox.com/en-US/Kinect [26] Alnowani, M., Lewis, E., Guy, M., and Wells, K., “An observation model for motion correction in nuclear medicine,” SPIE, San Diego, California, USA (2010). [27] OpenNI, http://75.98.78.94/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64042 | - |
dc.description.abstract | 呼吸是很重要的,於是我們在本研究中提出了一套非接觸式呼吸量測系統, 此系統使用深度攝影機 (Kinect)計算從身體形變而得出的呼吸體積,並從呼吸體積的資訊中找出使用者的呼吸狀態和呼吸方式。呼吸狀態包含了呼吸頻率、呼吸深度、以及吸吐氣比例。除此之外,我們的系統不需要貼標記在身上就能動態的追蹤胸腹腔的位置,以用來估算出胸腹腔的個別呼吸體積。對於不同的身體姿態,我們發展出兩種系統架構來個別處理。我們利用單邊深度攝影機系統來量測坐著和躺著時的呼吸,對於站立姿態,我們使用雙邊深度攝影機系統來消除站著時產生的晃動干擾,進而量測呼吸。在應用方面,坐姿偵測與毛公鼎多媒體互動系統做結合在,躺姿偵測應用在加護病房計畫,而站立姿態偵測與站樁系統結合。毛公鼎是感測使用者的呼吸狀態並且做出反應的多媒體互動系統,加護病房計畫則是利用感測患者的呼吸狀況來判定是否有生命危險,而站樁系統是用來輔助使用者在站樁時能看到身體的晃動程度和呼吸的情況。本篇研究在實驗的部分利用肺功能器(spirometer)做準確度評估,分別探討不同衣著對於系統量測準確度的影響以及本篇系統用在坐、站、躺姿態中的準確度評估。本篇研究的目的在於發展出一套低價、容易操作、可測量局部呼吸、並且為非接觸式的系統,讓使用者能很輕鬆地評估自己的呼吸情況,最終達到預防醫學的終極目標。 | zh_TW |
dc.description.abstract | Breathing is important. In this study, a noncontact respiration measurement technique using depth cameras, Kinect camera, is developed to measure respiratory volume from the morphological changes of chest wall region. Then, user’s breathing status, i.e. the respiratory rate, respiratory depth, and inhale-to-exhale ratio, and breathing methods, i.e. thoracic breathing and abdominal breathing, can be measured. For measuring the chest wall movements, a dynamic thoracic and abdominal region of interest (ROI) tracking technique is used. In this study, two frameworks of noncontact respiratory measurement are proposed. One is a single-sided depth camera system to measure respiratory volume in sitting and lying postures. The other is a double-sided depth camera system to measure respiratory volume in standing posture. Through experiments, the system was evaluated in three different wearing conditions (necked, thin clothing, and thick coat) and three different postures (sitting, lying, and standing). Among these experiments, the measured respiratory volumes are compared by our method and a reference device, spirometer.
Finally, three noncontact respiratory measurement applications are developed, including a Mao-Kung Ting system, an Intensive Care Unit (ICU) respiratory monitoring system, and a standing meditation system. In conclusion, a low-cost and easy-operating noncontact regional respiratory measurement system is developed, and the contribution of this study is to develop a system which could help users aware of their breathing conditions, and toward the ultimate goal of preventive medicine. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:27:39Z (GMT). No. of bitstreams: 1 ntu-101-R99922124-1.pdf: 1568935 bytes, checksum: fe6310a32e3b8ae121b2b39ac1824c04 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 INTRODUCTION 1 Chapter 2 RELATED WORK 3 2.1 Color Camera Based Method 4 2.2 Optoelectronic Plethysmography (OEP) 5 2.3 Structured Light Plethysmography (SLP) 6 2.4 Depth Camera 7 Chapter 3 RESPIRATORY VOLUME MEASUREMENT 9 3.1 System Spec 9 3.2 Region of Interest (ROI) 10 3.3 Respiratory Volume Estimation 13 3.4 Respiratory Signal Processing 18 3.4.1 Peak Detection 18 3.4.2 Respiratory method 21 Chapter 4 EXPERIMENTS 23 4.1 Validation Test 23 4.1.1 Clothing Test 23 4.1.2 Posture Test 24 4.2 Results of Validation Test 26 4.2.1 Clothing Test 26 4.2.2 Single-Sided Setup for Sitting Posture 30 4.2.3 Inclined single-Sided Setup for Lying Posture 31 4.2.4 Double-Sided Setup for Standing Posture 32 4.3 Isovolume Test 34 4.4 Regional Pulmonary Measurement Test 35 Chapter 5 APPLICATIONS 36 5.1 Sitting Posture - Mao-Kung Ting 36 5.2 Lying Posture - Intensive Care Unit (ICU) 37 5.3 Standing Posture - Zhan Zhuang (站樁) 38 Chapter 6 CONCLUSION AND FUTURE WORK 39 6.1 Discussion 39 6.2 Conclusion 40 6.3 Future Work 40 REFERENCE 42 | |
dc.language.iso | en | |
dc.title | 利用深度攝影機做非接觸式呼吸體積測量 | zh_TW |
dc.title | Noncontact Respiratory Volume Measurement Using Depth Camera | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 江政杰,邱志義,石勝文 | |
dc.subject.keyword | 非接觸式;深度攝影機;呼吸體積;局部量測, | zh_TW |
dc.subject.keyword | Noncontact; depth camera; respiratory volume; regional measurement, | en |
dc.relation.page | 45 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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