臺灣博碩士論文加值系統

遠程光電容積脈搏波（rPPG）技術可以通過使用常規RGB相機分析人體皮膚上的重要信號顏色變化來監測瞬時心率。測量是在人臉上執行的。利用濾波器和信號處理來克服運動引起的和光誘導的偽影。然後對濾波後的RGB信號進行處理並利用來提取生命信號。為此，通過對生命信號頻譜應用快速傅立葉變換（FFT），將提取的生命信號級聯以估計心率。所提出的算法是實時工作的，並在中等範圍的計算機上實現。所提出的心率估計算法的性能使用H7心率傳感器來評估，該心率傳感器是利用彈性帶附接到對象的胸部周圍的接觸裝置。它以心電圖（ECG）的準確性和可靠性來檢測受檢者的心跳，心臟的電信號。實驗結果表明，該方法與極性H7接觸器件在自然運動對象評估方法中具有較高的相關性，實現了極化H7接觸器件的平均均方根誤差和r-平方更偉大 。由於這樣一個好的結果，設置簡單，工作原理簡單，我們的算法很容易在現實中應用，從而有可能在各種應用中進一步發展，即實時檢測，欺騙人臉檢測，面部表情檢測。

Remote photoplethysmography (rPPG) techniques can monitor the instantaneous heart rate by analyzing vital signal color variations on human skin utilizing a regular RGB camera. The measurements are executed on human faces. A filter and signal processing is utilized to overcome motion-induced and light-induced artifacts. The filtered RGB signal then is processed and exploited to extract the vital signal. To this end, the extracted vital signal is concatenated to estimate heart rate by applying fast Fourier transform (FFT) on the vital signal spectrum. The proposed algorithm works in real-time and, is implemented on a mid-range computer. The performance of the presented Heart rate estimation algorithm was evaluated using an H7 heart rate sensor, a contact device, which is attached around the chest of the subject with an elastic strap. It detects subject’s heartbeat, the electric signal of the heart, at the accuracy and reliability of the electrocardiogram (ECG). Experiment results show high degrees of correlation between the proposed method and the polar H7 contact device when evaluating this method with the naturally moving subject, we achieve an average root mean square error of and r-square of and the agreement between the polar H7 contact device is greater. With such a good result and simple setup, simple working principle, our algorithm is easy to apply in reality, and thus potentially to further develop in various application i.e. live detection, spoof face detection, face expression detection.

Contents
Acknowledgment i
Abstract ii
Contents iii
List of Figures v
List of Tables vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Research 2
1.3 Contribution 4
Chapter 2 Methodology 7
2.1 Skin Characteristic in Ambient Light 8
2.2 Images Acquisition and ROI tracking 8
2.2.1 Camera setting 8
2.2.2 Viola-Jones face detection 9
2.2.3 ROI Tracking 10
2.3 Heart Beat Rate Estimation Algorithmic Principles 11
2.4 Operation Procedure 23
Chapter 3 Experimental Results 26
3.1 Effect of Lighting Condition 28
3.2 Effect of Forehead Covering 30
3.3 Effect of Face Region 32
3.4. Effect of Distance between Subject and Camera 33
3.5. Effect of Head Movement 35
3.6. Effect of Face Posture 36
3.7. Effect of Body Movement 37
3.8. Effect of Facial Expression 39
Chapter 4 Discussion 41
4.1. Effect of Lighting Condition 41
4.2. Effect of Forehead Covering 41
4.3. Effect of Face Regions 41
4.4 Effect of Distance between Subject and Camera 42
4.5. Effect of Head Movements 42
4.6. Effect of face postures 43
4.7. Effect of Body Movements 43
4.8. Effect of Facial Expressions 43
Chapter 5 Conclusion 45
References 46
 

References
[1]S. Mendis, P. Puska, and B. Norrving, Global atlas on cardiovascular disease prevention and control: World Health Organization, 2011.
[2]I. Abubakar, T. Tillmann, and A. Banerjee, "Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013," Lancet, vol. 385, pp. 117-171, 2015.
[3]A. Mattu and W. J. Brady, ECGs for the emergency physician 2: John Wiley & Sons, 2011.
[4]Portable Smart Electrocardiograph Garment and Measurement. Available: https://stonybrook.digication.com/2012biodesign2/need/
[5]M. Garbey, N. Sun, A. Merla, and I. Pavlidis, "Contact-free measurement of cardiac pulse based on the analysis of thermal imagery," IEEE Transactions on Biomedical Engineering, vol. 54, pp. 1418-1426, 2007.
[6]S. S. Ulyanov and V. V. Tuchin, "Pulse-wave monitoring by means of focused laser beams scattered by skin surface and membranes," in Static and Dynamic Light Scattering in Medicine and Biology, 1993, pp. 160-168.
[7]A. B. Hertzman, "Observations on the finger volume pulse recorded photoelectrically," Am. J. Physiol., vol. 119, pp. 334-335, 1937.
[8]G. Balakrishnan, F. Durand, and J. Guttag, "Detecting pulse from head motions in video," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3430-3437.
[9]M.-Z. Poh, D. J. McDuff, and R. W. Picard, "Advancements in noncontact, multiparameter physiological measurements using a webcam," IEEE transactions on biomedical engineering, vol. 58, pp. 7-11, 2011.
[10]W. Verkruysse, L. O. Svaasand, and J. S. Nelson, "Remote plethysmographic imaging using ambient light," Optics express, vol. 16, pp. 21434-21445, 2008.
[11]M.-Z. Poh, D. J. McDuff, and R. W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation," Optics express, vol. 18, pp. 10762-10774, 2010.
[12]M. Lewandowska, J. Rumiński, T. Kocejko, and J. Nowak, "Measuring pulse rate with a webcam—a non-contact method for evaluating cardiac activity," in Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on, 2011, pp. 405-410.
[13]H.-Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. Freeman, "Eulerian video magnification for revealing subtle changes in the world," 2012.
[14]G. de Haan and V. Jeanne, "Robust pulse rate from chrominance-based rPPG," IEEE Transactions on Biomedical Engineering, vol. 60, pp. 2878-2886, 2013.
[15]W. Wang, S. Stuijk, and G. De Haan, "Exploiting spatial redundancy of image sensor for motion robust rPPG," IEEE transactions on Biomedical Engineering, vol. 62, pp. 415-425, 2015.
[16]W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, "Algorithmic principles of remote PPG," IEEE Transactions on Biomedical Engineering, vol. 64, pp. 1479-1491, 2017.
[17]L. Tarassenko, M. Villarroel, A. Guazzi, J. Jorge, D. Clifton, and C. Pugh, "Non-contact video-based vital sign monitoring using ambient light and auto-regressive models," Physiological measurement, vol. 35, p. 807, 2014.
[18]A. R. Guazzi, M. Villarroel, J. Jorge, J. Daly, M. C. Frise, P. A. Robbins, et al., "Non-contact measurement of oxygen saturation with an RGB camera," Biomedical optics express, vol. 6, pp. 3320-3338, 2015.
[19]I. C. Jeong and J. Finkelstein, "Introducing contactless blood pressure assessment using a high speed video camera," Journal of medical systems, vol. 40, p. 77, 2016.
[20]L. K. Mestha, S. Kyal, B. Xu, L. E. Lewis, and V. Kumar, "Towards continuous monitoring of pulse rate in neonatal intensive care unit with a webcam," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, pp. 3817-3820.
[21]S. Fernando, W. Wang, I. Kirenko, G. de Haan, S. Bambang Oetomo, H. Corporaal, et al., "Feasibility of contactless pulse rate monitoring of neonates using google glass," in Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare, 2015, pp. 198-201.
[22]J.-P. Couderc, S. Kyal, L. K. Mestha, B. Xu, D. R. Peterson, X. Xia, et al., "Detection of atrial fibrillation using contactless facial video monitoring," Heart Rhythm, vol. 12, pp. 195-201, 2015.
[23]D. McDuff, S. Gontarek, and R. Picard, "Remote measurement of cognitive stress via heart rate variability," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, pp. 2957-2960.
[24]H. A. W. Schmeitz, M. J. Bartula, T. J. W. Tijs, G. A. F. Van Elswijk, and M. F. Gillies, "Device and method for vital sign measurement of a person," ed: Google Patents, 2014.
[25]I. O. Kirenko, A. J. Van Leest, and G. De Haan, "Device and method for monitoring vital signs," ed: Google Patents, 2016.
[26]E. Bresch, J. Muehlsteff, T. Tigges, A. Dubielczyk, and C. Shan, "System for camera-based vital sign measurement," ed: Google Patents, 2015.
[27]P. O. Isaacson and J. D. Schilling, "Led control utilizing ambient light or signal quality," ed: Google Patents, 2009.
[28]J. A. J. Thijs, J. Muhlsteff, and R. Pinter, "Method and system for monitoring vital body signs of a seated person," ed: Google Patents, 2010.
[29]B. A. Mulder, G. Z. Angelis, P. H. A. Dillen, A. Van Dalfsen, A. Sevo, R. Pinter, et al., "Device, system and method for measuring vital signs," ed: Google Patents, 2013.
[30]Y. Li, B. Chen, E. M. McKenna, and P. S. Addison, "Systems and methods for combined physiological sensors," ed: Google Patents, 2011.
[31]H. E. Tasli, A. Gudi, and M. den Uyl, "Remote PPG based vital sign measurement using adaptive facial regions," in Image Processing (ICIP), 2014 IEEE International Conference on, 2014, pp. 1410-1414.
[32]Video-image-based method and system for detecting non-contact vital sign. Available: https://patents.google.com/patent/CN102499664A/en?oq=CN+102499664+A
[33]P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, pp. 137-154, 2004.
[34]B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," 1981.
[35]O. Mamontov, R. Giniatullin, M. Volynsky, I. Sidorov, A. Kamshilin, and L. Babayan, "Accurate measurement of the pulse wave delay with imaging photoplethysmography," 2016.
[36]P. V. Rouast, M. T. Adam, V. Dorner, and E. Lux, "Remote Photoplethysmography: Evaluation of Contactless Heart Rate Measurement in an Information Systems Setting."
[37]G. De Haan and A. Van Leest, "Improved motion robustness of remote-PPG by using the blood volume pulse signature," Physiological measurement, vol. 35, p. 1913, 2014.
[38]A. G. Barnston, "Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score," Weather and Forecasting, vol. 7, pp. 699-709, 1992.