在約莫十年前國外學者提出使用一般消費型攝影機即可遠距非接觸式的量測心率後，近年非接觸式心率量測的方法，已從受測者靜止狀態下的量測，發展至允許受測者輕微動態狀態下的量測。而為了使受測者在量測過程中不須刻意維持靜止狀態，且可以更加的輕鬆自在地進行量測，本論文針對現有基於影像的即時動態量測心率的相關技術進行實作與分析，由實驗得到的結果顯示使用光流法作ROI (Region of Interest)的定位與追蹤的方法，較適合應用於受測者維持靜態或輕微轉動的狀況下。而使用人臉地標點(facial landmark)作ROI的定位與追蹤的方法，則較適合應用於受測者非靜態的狀況下。

About ten years ago, the remote measurement of the cardiac pulse via a consumer camera was proposed by some scholar. Recent studies showed the method of contactless heart rate measurement has been developed from the measurement of the subject's quiescent state to the measurement of the subject in a slight dynamic state. In order to let the subject do not need to deliberately maintain the static state during the measurement process, and can measure more easily and freely. This thesis focuses on the heart rate measurement technique which allows measure from subject movement, and implement and analysis these techniques. The experiment result shows the ROI positioning and tracking method based on optical flow is more suitable to measure from the subject stay still or small motion state. And the ROI positioning and tracking method based on facial landmark is more suitable to measure from the subject movement state.

[1] W.Verkruysse, L. O.Svaasand, andJ. S.Nelson, “Remote plethysmographic imaging using ambient light,” Opt. Express, vol. 16, no. 26, pp. 21434–21445, 2008.
[2] “Anatomy of the Heart.” [Online]. Available: https://commons.wikimedia.org/wiki/File:Anatomy_of_the_Heart.jpg.
[3] D.Agro et al., “PPG embedded system for blood pressure monitoring,” in 2014 AEIT Annual Conference - From Research to Industry: The Need for a More Effective Technology Transfer, AEIT 2014, 2015.
[4] T.Tamura, Y.Maeda, M.Sekine, andM.Yoshida, “Wearable photoplethysmographic sensors—past and present,” Electronics, vol. 3, no. 2, pp. 282–302, 2014.
[5] M.Garbey, N.Sun, A.Merla, andI.Pavlidis, “Contact-free measurement of cardiac pulse based on the analysis of thermal imagery,” IEEE Trans. Biomed. Eng., vol. 54, no. 8, pp. 1418–1426, 2007.
[6] M. A.Hassan et al., “Heart rate estimation using facial video: A review,” Biomed. Signal Process. Control, vol. 38, pp. 346–360, 2017.
[7] M.-Z.Poh, D. J.McDuff, andR. W.Picard, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation,” Opt. Express, vol. 18, no. 10, pp. 10762–10774, 2010.
[8] “OpenCV (Open Source Computer Vision Library).” [Online]. Available: https://opencv.org/.
[9] P.Viola andM. J.Jones, “Robust Real-Time Face Detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, 2004.
[10] R.Stricker, S.Muller, andH.-M.Gross, “Non-contact video-based pulse rate measurement on a mobile service robot,” in IEEE RO-MAN 2014 - 23rd IEEE International Symposium on Robot and Human Interactive Communication: Human-Robot Co-Existence: Adaptive Interfaces and Systems for Daily Life, Therapy, Assistance and Socially Engaging Interactions, 2014, pp. 1056–1062.
[11] Z.Guo, Z. J.Wang, andZ.Shen, “Physiological parameter monitoring of drivers based on video data and independent vector analysis,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2014, pp. 4374–4378.
[12] X.Li, J.Chen, G.Zhao, andM.Pietikäinen, “Remote Heart Rate Measurement from Face Videos under Realistic Situations,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 4264–4271.
[13] H.Qi, Z. J.Wang, andC.Miao, “Non-contact driver cardiac physiological monitoring using video data,” in 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, 2015, pp. 418–422.
[14] S.Tulyakov, X.Alameda-Pineda, E.Ricci, L.Yin, J. F.Cohn, andN.Sebe, “Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2396–2404.
[15] M.-C.Li andY.-H.Lin, “A real-time non-contact pulse rate detector based on smartphone,” in 4th International Symposium on Next-Generation Electronics, IEEE ISNE 2015, 2015.
[16] V.Kazemi andJ.Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 1867–1874.
[17] G.Tzimiropoulos andM.Pantic, “Optimization problems for fast AAM fitting in-the-wild,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 593–600.
[18] S.Ren, X.Cao, Y.Wei, andJ.Sun, “Face alignment at 3000 FPS via regressing local binary features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 1685–1692.
[19] “Facial point annotations.” [Online]. Available: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/.
[20] J.Shi, “Good features to track,” in Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94., 1994 IEEE Computer Society Conference on, 1994, pp. 593–600.
[21] B. D.Lucas andT.Kanade, “An iterative image registration technique with an application to stereo vision,” 1981, vol. 2, pp. 674–679.
[22] N.Ho, “UNDERSTANDING OPENCV CV::ESTIMATERIGIDTRANSFORM.” [Online]. Available: http://nghiaho.com/?p=2208.