Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98378
標題: 非接觸式心率估計及其在健身訓練中的應用
Non-contact Heart Rate Estimation and Its Applications to Fitness Training
作者: 趙昶辰
Chang-chen Zhao
關鍵字: 光體積描記圖法;非接觸式生理訊號偵測;心率估計;影像壓縮;健身訓練;photoplethysmography;non-contact physiological measurement;heart rate estimation;video compression;fitness exercise
引用: [1] Wu, A. T., Blazek, V., Schmitt, H. J., 'Photoplethysmography imaging: a new noninvasive and noncontact method for mapping of the dermal perfusion changes', in Proceedings of SPIE Optical Techniques and Instrumentation for the Measurement of Blood Composition, Structure, and Dynamics, Amsterdam, The Netherlands, vol. 4163, pp. 62-70, 2000. [2] Wieringa, F. P., Mastik, F., Af, V. D. S., 'Contactless multiple wavelength photoplethysmographic imaging: a first step toward 'spo2 camera' technology',Annals of Biomedical Engineering, vol. 33, no. 8, pp. 1034-1041, 2005. [3] Humphreys, K., Ward, T., Markham, C., 'Noncontact simultaneous dual wavelength photoplethysmography: a further step toward noncontact pulse oximetry', Review of Scientific Instruments, vol. 78, no. 4, pp. 004304, 2007. [4] Takano, C., Ohta, Y., 'Heart rate measurement based on a time-lapse image', Medical Engineering & Physics, vol. 29, no. 8, pp. 853-857, 2007. [5] Zheng, J., Hu, S., Azorinperis, V., 'Remote simultaneous dual wavelength imaging photoplethysmography: a further step towards 3-D mapping of skin blood microcirculation', Biomedical Optics, vol. 6850, pp. 68500S-68500S-8. 2008. [6] Verkruysse, W., Svaasand, L. O., Nelson, J. S., 'Remote plethysmographic imaging using ambient light', Optics Express, vol. 16, no. 26, pp. 21434-45, 2008. [7] Poh, M. Z., Mcduff, D. J., Picard, R. W., 'Non-contact, automated cardiac pulse measurements using video imaging and blind source separation', Optics Express, vol. 18, no. 10, pp. 10762-10774, 2010. [8] Feng, L., Po, L. M., Xu, X., Li, Y., Ma, R., 'Motion-resistant remote imaging photoplethysmography based on the optical properties of skin', IEEE Transactions on Circuits & Systems for Video Technology, vol. 25, no. 5, pp. 879-891, 2015. [9] Lewandowska, M., Rumiński, J., Kocejko, T., Nowak, J., 'Measuring pulse rate with a webcam - A non-contact method for evaluating cardiac activity', in Proceedings of Federated Conference on Computer Science and Information Systems, Szczecin, Poland, pp. 405-410, 2011. [10] Tsouri, G. R., Kyal, S., Dianat, S., Mestha, L. K., 'Constrained independent component analysis approach to nonobtrusive pulse rate measurements', Journal of Biomedical Optics, vol. 17, no. 7, pp. 077011, 2012. [11] Macwan, R., Benezeth, Y., Mansouri, A., 'Remote photoplethysmography with constrained ica using periodicity and chrominance constraints', Biomedical Engineering Online, vol. 17, no. 1, pp. 17-22, 2018. [12] Sun, Y., Hu, S., Azorinperis, V., Greenwald, S., Chambers, J., Zhu, Y., 'Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise', Journal of Biomedical Optics, vol. 16, no. 7, pp. 077010, 2011. [13] Macwan, R., Benezeth, Y., Nakamura, K., Gomez, R., Wu, Y., Mansouri, A., 'Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography', in Proceedings of IEEE EMBS International Conference on Biomedical & Health Informatics, Las Vegas, NV, USA, pp. 267-270, 2018. [14] Qi, H., Guo, Z., Chen, X., Shen, Z., Wang, Z. J., 'Video-based human heart rate measurement using joint blind source separation', Biomedical Signal Processing & Control, vol. 31, pp. 309-320, 2017. [15] Wang, W., Brinker, A. C. D., Stuijk, S., Haan, G. D., 'Algorithmic principles of remote ppg', IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1479-1491, 2017. [16] Xu, S., Sun, L., Rohde, G. K., 'Robust efficient estimation of heart rate pulse from video', Biomedical Optics Express, vol. 5, no. 4, pp. 1124-1135, 2014. [17] Lam, A., Kuno, Y., 'Robust heart rate measurement from video using select random patches', in Proceedings of IEEE International Conference on Computer Vision, Santiago, Chile, pp. 3640-3648, 2015. [18] Aarts, L. A. M., Jeanne, V., Cleary, J. P., Lieber, C., Nelson, J. S., Oetomo, S. B., et al., 'Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit — a pilot study', Early Human Development, vol. 89, no. 12, pp. 943-8, 2013. [19] Van, G. M., Stuijk, S., De, H. G., 'Robust respiration detection from remote photoplethysmography', Biomedical Optics Express, vol. 7, no. 12, pp. 4941-4957, 2016. [20] Klaessens, J. H. G. M., Born, M. V. D., Veen, A. V. D., Kraats, J. S. D., Dungen, F. A. M. V. D., Verdaasdonk, R. M., 'Development of a baby friendly non-contact method for measuring vital signs: First results of clinical measurements in an open incubator at a neonatal intensive care unit'. SPIE BiOS, vol. 8935, pp. 57-62, 2014. [21] Scalise, L., Bernacchia, N., Ercoli, I., Marchionni, P., 'Heart rate measurement in neonatal patients using a webcamera', In Proceedings of IEEE International Symposium on Medical Measurements and Applications, Budapest, Hungary, pp. 1-4, 2012. [22] Villarroel, M., Guazzi, A., Jorge, J., Davis, S., Watkinson, P., Green, G., et al., 'Continuous non-contact vital sign monitoring in neonatal intensive care unit', Healthcare Technology Letters, vol. 1, no. 3, pp. 87-91, 2014. [23] Villarroel, M., Jorge, J., Pugh, C., Tarassenko, L., 'Non-Contact Vital Sign Monitoring in the Clinic', In Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition, Washington, DC, USA , pp. 278-285, 2017. [24] Zhao, F., Li, M., Qian, Y., Tsien, J. Z., 'Remote measurements of heart and respiration rates for telemedicine'. Plos One, vol. 8, no. 10, pp. e71384, 2013. [25] Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., Dubois, J., 'Unsupervised skin tissue segmentation for remote photoplethysmography', Pattern Recognition Letters, 2017, doi: https://doi.org/10.1016/j.patrec.2017.10.017. [26] Gibert, G., D'Alessandro, D., Lance, F., 'Face detection method based on photoplethysmography', In Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, Krakow, Poland, pp. 449-453, 2013. [27] Kitajima, T., Murakami, E. A. Y., Yoshimoto, S., Kuroda, Y., Oshiro, O., 'Privacy-aware face detection using biological signals in camera images', Electronics and Communications in Japan, vol. 101, no. 6, pp. 67-79, 2018. [28] Liu, H., Chen, T., Zhang, Q., Wang, L., 'A New Approach for Face Detection Based on Photoplethysmographic Imaging', in Proceedings of International Conference on Health Information Science, Delhi, India, pp. 79-91, 2015. [29] Luijtelaar, R. V., Wang, W., Stuijk, S., Haan, G. D., 'Automatic RoI Detection for Camera-Based Pulse-Rate Measurement', in Proceedings of Asian Conference on Computer Vision Workshops, Taipei, Taiwan, pp. 360-374, 2014. [30] Wang, W., Stuijk, S., Haan, G. D., 'Unsupervised subject detection via remote ppg', IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2629-2637, 2015. [31] Wang, W., Stuijk, S., Haan, G. D., 'Living-skin classification via remote-PPG', IEEE Transactions on Biomedical Engineering, vol. 64, no. 12, pp. 2781-2792, 2017. [32] Liu, Y., Jourabloo, A., Liu, X., 'Learning deep models for face anti-spoofing: binary or auxiliary supervision', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 389-398, 2018. [33] Liu, S., Yuen, P. C., Zhang, S., Zhao, G., '3D Mask Face Anti-spoofing with Remote Photoplethysmography', in Proceedings of European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 85-100, 2016. [34] Benezeth, Y., Li, P., Macwan, R., Nakamura, K., Gomez, R., Yang, F., 'Remote heart rate variability for emotional state monitoring', In Proceedings of IEEE EMBS International Conference on Biomedical & Health Informatics, pp. 153-156, 2018. [35] Dingli, A., Giordimaina, A., 'Webcam-based detection of emotional states', The Visual Computer, vol. 33, no. 4, pp. 459-469, 2017. [36] Kessler, V., Thiam, P., Amirian, M., Schwenker, F., 'Multimodal fusion including camera photoplethysmography for pain recognition', in Proceedings of International Conference on Companion Technology, pp.1-4, 2017. [37] Lakens, D., 'Using a smartphone to measure heart rate changes during relived happiness and anger', IEEE Transactions on Affective Computing, vol. 4, no. 2, pp. 238-241, 2013. [38] Mcduff, D., Gontarek, S., Picard, R., 'Remote measurement of cognitive stress via heart rate variability'. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, pp. 2957-2960, 2014. [39] Lakens, D., 'Using a smartphone to measure heart rate changes during relived happiness and anger', IEEE Transactions on Affective Computing, vol. 4, no. 2, pp. 238-241, 2013. [40] Mcduff, D., Hurter, C., Gonzalez-Franco, M., 'Pulse and vital sign measurement in mixed reality using a HoloLens', In Proceedings of ACM Symposium on Virtual Reality Software and Technology, Gothenburg, Sweden, 2017. [41] Okada, G., Yonezawa, T., Kurita, K., Tsumura, N., 'Monitoring emotion by remote measurement of physiological signals using an RGB camera', ITE Transactions on Media Technology and Applications, vol. 6, no. 1, pp. 131-137, 2018. [42] Tasli, H. E., Gudi, A., Uyl, M. D., 'Integrating remote PPG in facial expression analysis framework', in Proceedings of International Conference on Multimodal Interaction, Istanbul, Turkey, pp. 74-75, 2014. [43] Lin, Y. C., Lin, Y. H., 'Step count and pulse rate detection based on the contactless image measurement method', IEEE Transactions on Multimedia, DOI: 10.1109/TMM.2018.2790172, 2018. [44] Wang, W., Balmaekers, B., Haan, G. D., 'Quality metric for camera-based pulse rate monitoring in fitness exercise', in Proceedings of IEEE International Conference on Image Processing, Phoenix, AZ, USA, pp. 2430-2434, 2016. [45] Wang, W., den Brinker, A. C., Stuijk, S., De, H. G., 'Robust heart rate from fitness videos', Physiological Measurement, vol. 38, no. 6, pp. 1023-1044, 2017. [46] Vogels, T., Gastel, M. V., Wang, W., Haan, G., 'Fully-automatic camera-based pulse-oximetry during sleep', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 1462-1470, 2018. [47] Amelard, R., Pfisterer, K. J., Jagani, S., Clausi, D. A., Wong, A., 'Non-contact assessment of obstructive sleep apnea cardiovascular biomarkers using photoplethysmography imaging', Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, vol.10501, pp. 37, 2018. [48] Wang, W., den Brinker, A. C., de Haan, G., 'Full video pulse extraction,' Biomedical Optics Express, no. 99, pp. 1–1, 2018. [49] Li, Z. G., Pan, F., Lim, K. P., Feng, G., Lin, X., Rahardja, S., 'Adaptive basic unit layer rate control for JVT,' in JVT-G012-r1, 7th Meeting, Pattaya II, Thailand, vol. 14, 2003. [50] Liu, Y., Li, Z. G., Soh, Y. C., 'A novel rate control scheme for low delay video communication of H.264/AVC standard', IEEE Transactions on Circuits & Systems for Video Technology, vol. 17, no. 1, pp. 68-78, 2006. [51] Seepers, R. M., Wang, W., Haan, G. D., Sourdis, I., Strydis, C., 'Attacks on heartbeat-based security using remote photoplethysmography', IEEE Journal of Biomedical & Health Informatics, vol. 22, no. 3, pp. 714-721, 2018. [52] Chen, W., Picard, R. W., 'Eliminating Physiological Information from Facial Videos', in Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition, Washington, DC, USA, pp. 48-55, 2017. [53] Hanfland, S., Paul, M., 'Video format dependency of PPGI signals' in Proceedings of International Conference on Electrical Engineering, 2016. [54] Mcduff, D. J., Blackford, E. B., Estepp, J. R., 'The impact of video compression on remote cardiac pulse measurement using imaging photoplethysmography', in Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition, Washington, DC, USA, pp. 63-70, 2017. [55] Zhao, C., Lin, C.-L., Chen, W., Li, Z., 'A novel framework for remote photoplethysmography pulse extraction on compressed videos', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1412-1421, June 18-22, Salt Lake City, USA, 2018. [56] Lin, C.-L., Zhao, C., Chen, W., 'A remote photoplethysmography framework for pulse extraction on compressed videos,' Taiwan patent, no. 107PF0001, 2017. [57] Hadizadeh, H., Bajic, I. V., 'Saliency-preserving video compression', in Proceedings of IEEE International Conference on Multimedia and Expo, Barcelona, Spain, 2011. [58] Hadizadeh, H., Bajic, I. V., 'Saliency-aware video compression', IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 19-33, 2014. [59] Tung, H. Y., Tsang, K. F., Tung, H. C., Chui, K. T., 'The design of dual radio zigbee homecare gateway for remote patient monitoring', IEEE Transactions on Consumer Electronics, vol. 59, no. 4, pp. 756-764, 2013. [60] Spinsante, S., Gambi, E., 'Remote health monitoring by osgi technology and digital tv integration', IEEE Transactions on Consumer Electronics, vol. 58, no. 4, pp. 1434-1441, 2012. [61] Chua, E., Fang, W. C., 'Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems', IEEE Transactions on Consumer Electronics, vol. 57, no. 1, pp. 267-273, 2011. [62] Zhao, Q., Meng, D., Xu, Z., Zuo, W., Zhang, L., 'Robust Principal Component Analysis with Complex Noise', in Proceedings of International Conference on Machine Learning, vol. 32, no. 2, pp. 55-63, 2014. [63] Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P. H. S., 'Staple: complementary learners for real-time tracking', in Proceedings of International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, vol. 38, no. 2, pp. 1401-1409, 2016. [64] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al., 'The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis', in Proceedings of Mathematical Physical & Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998. [65] Rilling, G., Flandrin, P., 'One or two frequencies? the empirical mode decomposition answers', IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 85-95, 2008. [66] Aihara, R., Takiguchi, T., Ariki, Y., 'Activity-mapping non-negative matrix factorization for exemplar-based voice conversion', in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, QLD, Australia, pp. 4899-4903, 2015. [67] S. Wang, H. L. Tang, Y. Hu, S. Sanei, G. M. Saleh, T. Peto et al., 'Localizing microaneurysms in fundus images through singular spectrum analysis', IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, pp. 990–1002, 2017. [68] Vautard, R., Yiou, P., Ghil, M., 'Singular-spectrum analysis: a toolkit for short, noisy chaotic signals', in Proceedings of Interpretation of time series from nonlinear mechanical systems, vol.58, pp. 95-126, 1992. [69] Haan, G. D., Jeanne, V., 'Robust pulse rate from chrominance-based rPPG', IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2878-2886, 2013. [70] Wu, H. Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W., 'Eulerian video magnification for revealing subtle changes in the world', SIGGRAPH, Los Angeles, CA, USA, 2012. [71] Liu, Y., Li, Z. G., Soh, Y. C., 'Region-of-interest based resource allocation for conversational video communication of H.264/AVC', IEEE Transactions on Circuits & Systems for Video Technology, vol. 18, no. 1, pp. 134-139, 2008. [72] Poh, M. Z., Mcduff, D. J., Picard, R. W., 'Advancements in noncontact, multiparameter physiological measurements using a webcam', IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 7-11, 2011. [73] Elhamifar, E., Vidal, R., 'Sparse subspace clustering', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 2790-2797, 2009. [74] You, C., Robinson, D. P., Vidal, R., 'Scalable sparse subspace clustering by orthogonal matching pursuit', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 3918-3927, 2016. [75] Lu, C. Y., Min, H., Zhao, Z. Q., Zhu, L., Huang, D. S., Yan, S., 'Robust and efficient subspace segmentation via least squares regression', in Proceedings of European Conference on Computer Vision, Florence, Italy, pp. 347-360, 2012. [76] You, C., Li, C. G., Robinson, D. P., Vidal, R., 'Oracle based active set algorithm for scalable elastic net subspace clustering', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 3928-3937, 2016. [77] Yang, X., Lin, W., Lu, Z., Lin, X., Rahardja, S., Ong, E. P., et al., 'Rate control for videophone using local perceptual cues', IEEE Transactions on Circuits & Systems for Video Technology, vol. 15, no. 4, pp. 496-507, 2005. [78] Chen, Z., Guillemot, C., 'Perceptually-friendly h.264/avc video coding based on foveated just-noticeable-distortion model', IEEE Transactions on Circuits & Systems for Video Technology, vol. 20, no. 6, pp. 806-819, 2010. [79] Shen, J., Du, Y., Wang, W., Li, X., 'Lazy random walks for superpixel segmentation', IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1451-1462, 2014. [80] Fan, Z., Zhou, J., Wu, Y., 'Multibody grouping by inference of multiple subspaces from high-dimensional data using oriented-frames', IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. 1, pp. 91-105, 2005. [81] Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J. F., Sebe, N., 'Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions', in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 2396-2404, 2016. [82] Kumar, M., Veeraraghavan, A., Sabharwal, A., 'DistancePPG: robust non-contact vital signs monitoring using a camera', Biomedical Optics Express, vol. 6, no. 5, pp. 1565-1588, 2015. [83] Yang, Y., Feng, J., Jojic, N., Yang, J., Huang, T. S., 'l0-Sparse Subspace Clustering', in Proceedings of European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 731-747, 2016. [84] Chen, S., Donoho, D. L., Saunders, M. A., 'Atomic decomposition by basis pursuit', SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 33-61, 1998. [85] Gorodnitsky, I. F., Rao, B. D., 'Sparse signal reconstruction from limited data using focuss: a re-weighted minimum norm algorithm', IEEE Transactions on Signal Processing, vol. 45, no. 3, pp. 600-616, 2002. [86] Boyd, S., Parikh, N., Chu, E., Peleato, B., 'Distributed optimization and statistical learning via the alternating direction method of multipliers', Foundations & Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2010. [87] McDuff, D., Sarah, G., Picard., R. W., 'Improvements in remote cardiopulmonary measurement using a five band digital camera', IEEE Transactions on Biomedical Engineering, vol. 61, no. 10, pp. 2593-2601, 2014. [88] Li., X., Chen, J., Zhao, G., Pietikainen, M., 'Remote heart rate measurement from face videos under realistic situations', in Proceedings of IEEE Computer Vision and Pattern Recognition, Columbus, Ohio, pp. 4264–4271, 2014. [89] Shi, J., Tomasi, C., 'Good features to track', in Proceedings of IEEE Computer Vision and Pattern Recognition, 1994, p. 593-600. [90] Sharma, K., Sharma, S. P., Lahiri, S. C., 'Estimation of blood alcohol concentration by horizontal attenuated total reflectance-Fourier transform infrared spectroscopy', Alcohol, vol. 44, no. 4, pp. 351-357, 2010. [91] Niu, X., Han, H., Shan, S., Chen, X., 'Continuous heart rate measurement from face: A robust rPPG approach with distribution learning', in Proceedings of IEEE International Joint Conference on Biometrics, Denvor, Colorado, USA, pp. 642-650, 2017. [92] Guo, X., Mandelis, A., Liu, Y., Chen, B., Zhou, Q., Comeau, F., 'Noninvasive in-vehicle alcohol detection with wavelength-modulated differential photothermal radiometry', Biomedical Optics Express, vol. 5, no. 7, pp. 2333-2340, 2014. [93] Lin, C.-L., Zhao, C., Wu, Y.-J., 'Feedback control of fitness machine based on image-based heart rate monitoring,' Taiwan patent, no. I626072, 2017.
摘要: 
遠程光體積描記圖法(rPPG)指一種利用攝像頭非接觸式的偵測人體心跳波形及心率估計方法。人體的心臟活動在體內產生生理訊號,即皮下微血管充血量之變化導致血液對光的吸收率產生變化。rPPG 通過分析皮膚區域的顏色變化偵測生理訊號。此項技術的主要難點包括光源變化與運動干擾。過去的二十年,研究者已經提出了許多方法來解決這些困難並且在許多應用領域中取得了突出的成績,應用場合包括:遠程醫療、臉部偵測、睡眠監控等。
本論文首先關注脈搏擷取與影像壓縮之間的關係,然後解決脈搏擷取在健身訓練中的應用。首先,本文提出一種演算法可以在壓縮視頻中有效擷取脈搏波形。基於對壓縮算法在原始測量信號上產生的影響的分析,本文提出一種單通道脈搏擷取演算法來彌補影響壓縮帶來的影響。其次,提出一種保存生理訊號的視頻壓縮算法。該算法可以將生理訊號保留在壓縮後的視頻中,使得現有脈搏擷取算法可以直接應用在壓縮視頻上。最後,提出一種在健身訓練時的心率偵測方法,並且將之應用在跑步機上。此外,本文還提出一種心率反饋控制演算法來提高運動效率。此演算法的性能在自建的數據集上得到了很好的驗證。本文建立了一種基於改進跑步機的心率反饋控制系統原型機來驗證心率反饋控制算法。
本論文探索rPPG技術與影像壓縮與運動干擾之間的關係,並且在健身運動中提出新的解決方案。希望本論文所發現的現象、提出的解決方案與得到的結論會給此領域的後繼研究者提供幫助。

Remote photoplethysmography (rPPG) means to detect human pulse waveform and to estimate the heart rate in a non-contact way by means of a digital camera. Human cardiac activity causes periodic physiological signals in the human body, e.g., the blood volume fluctuation in the micro vessels beneath the skin, leading to the absorbance changes of the incident light. rPPG then extracts this physiological signal by analyzing the pixel intensity variations within the skin region. The major challenges of this technique so far include illumination changes and motion interference. In the past two decades, researchers have proposed numerous approaches to deal with these challenges and have found significant success in various applications such as telemedicine, face detection, sleep monitoring, etc.
This thesis first addresses the relationship between pulse extraction and video compression, and then deals with its application to the fitness exercise. First, an algorithm is proposed to extract pulse waveform on the compressed video. Based on the analysis of the impact of popular video compression methods on the rPPG measurements, a single-channel pulse extraction approach is proposed to compensate for the quality loss of rPPG signal induced by the video compression methods. Second, a physiological signal preserving compression method is proposed such that existing rPPG extraction algorithms can be applied directly on the compressed video without modification and equivalent results can be achieved. Finally, we address the problem of extracting heart rate when the subject is performing fitness training. Furthermore, a novel concept of heart rate feedback control is proposed to promote the efficiency of the training. The proposed algorithms have been validated on several self-established dataset. A prototype hardware system based on modified treadmill is established to support the concept of heart rate feedback control.
This thesis exploits the knowledge of rPPG technique with respect to video compression and contributes to the application of extracting heart rate during fitness exercise. The author hopes to provide some insights to the subsequent researchers in this field.
URI: http://hdl.handle.net/11455/98378
Rights: 不同意授權瀏覽/列印電子全文服務
Appears in Collections:電機工程學系所

Files in This Item:
File SizeFormat Existing users please Login
nchu-107-8104064008-1.pdf4.45 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.