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Improved motion robustness of remote-PPG by using the blood volume pulse signature
Open accessAbstract
Remote photoplethysmography (rPPG) enables contact-free monitoring of the blood volume pulse using a color camera. Essentially, it detects the minute optical absorption changes caused by blood volume variations in the skin. In this paper, we show that the different absorption spectra of arterial blood and bloodless skin cause the variations to occur along a very specific vector in a normalized RGB-space. The exact vector can be determined for a given light spectrum and for given transfer characteristics of the optical filters in the camera. We show that this 'signature' can be used to design an rPPG algorithm with a much better motion robustness than the recent methods based on blind source separation, and even better than the chrominance-based methods we published earlier. Using six videos recorded in a gym, with four subjects exercising on a range of fitness devices, we confirm the superior motion robustness of our newly proposed rPPG methods. A simple peak detector in the frequency domain returns the correct pulse-rate for 68% of total measurements compared to 60% for the best previous method, while the SNR of the pulse-signal improves from - 5 dB to - 4 dB. For a large population of 117 stationary subjects we prove that the accuracy is comparable to the best previous method, although the SNR of the pulse-signal drops from + 8.4 dB to + 7.6 dB. We expect the improved motion robustness to significantly widen the application scope of the rPPG-technique.
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“Although during exercise, the main potential errors are due to motion artifact, which complicates the use of the pulse oximeter and the accurate interpretation of the data [29,30,31]”
From Skin pigmentation influence on pulse oximetry accuracy: a systematic review and bibliometric analysis (2022) · cites this paper as [31]
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Citations
Cites
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967
Remote plethysmographic imaging using ambient light
Verkruysse, W., Svaasand, L. O., & Nelson, J. S. (2008) · Optics Express · doi:10.1364/oe.16.021434 · Cited by 1,728
Non-contact, automated cardiac pulse measurements using video imaging and blind source separation
Poh, M., McDuff, D., & Picard, R. W. (2010) · Optics Express · doi:10.1364/oe.18.010762 · Cited by 1,652
Robust pulse rate from chrominance-based rPPG
de Haan, G., & Jeanne, V. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2266196 · Cited by 1,378
Self-citationDetecting Pulse from Head Motions in Video
Balakrishnan, G., Durand, F., & Guttag, J. V. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.440 · Cited by 572
Contact-Free Measurement of Cardiac Pulse Based on the Analysis of Thermal Imagery
Garbey, M., Sun, N., Merla, A., & Pavlidis, I. T. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.891930 · Cited by 428
Measuring pulse rate with a webcam — A non-contact method for evaluating cardiac activity
Lewandowska, M., Rumiński, J., Kocejko, T., & Nowak, J. (2011) · Cited by 420
Heart rate measurement based on a time-lapse image.
Takano, C., & Ohta, Y. (2007) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2006.09.006 · Cited by 345
Passive blood plasma separation at the microscale: a review of design principles and microdevices
Tripathi, S., Kumar, Y. V. B. V., Prabhakar, A., Joshi, S. S., & Agrawal, A. (2015) · Journal of Micromechanics and Microengineering · doi:10.1088/0960-1317/25/8/083001 · Cited by 223
Photoelectric Plethysmography of the Fingers and Toes in Man
Hertzman, A. B. (1937) · Experimental Biology and Medicine · doi:10.3181/00379727-37-9630 · Cited by 221
Research works printed but not linked: 2
- Lewandowska M, 2011, Proc. FedCSIS, 18–21, 405
- Glass K A, 2004, Proc. 5th Int. Conf. ICA
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RF-Vital: Radio-Based Contactless Respiration Monitoring for a Moving Individual
Choi, J.-H., Kang, K.-B., & Kim, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329427 · Cited by 13
Artificial Intelligence Modelling Human Mental Fatigue: A Comprehensive Survey
Lambert, A., Soni, A., Soukane, A., Ramdane-Chérif, A., & Rabat, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126999 · Cited by 15
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Zhu, Q., Wong, C.-W., Lazri, Z. M., Chen, M., Fu, C., & Wu, M. H. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3442785 · Cited by 13
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Signal fusion based on intensity and motion variations for remote heart rate determination
Wiede, C., Richter, J., & Hirtz, G. (2016) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2016.7738282 · Cited by 13
Advancements in Remote Photoplethysmography
Saikevičius, L., Raudonis, V., Kozlovskaja-Gumbrienė, A., & Šakalytė, G. (2025) · Electronics · doi:10.3390/electronics14051015 · Cited by 12
Guo, J., Dai, Y., Wang, C., Wu, H., Xu, T., & Lin, K. B. (2019) · Software Practice and Experience · doi:10.1002/spe.2730 · Cited by 12
Dedicated Exposure Control for Remote Photoplethysmography
Laurie, J., Higgins, N., Peynot, T., & Roberts, J. M. (2020) · IEEE Access · doi:10.1109/access.2020.3003548 · Cited by 12
Fundamentals of Camera-PPG Based Magnetic Resonance Imaging
Wang, W., Weiß, S., Brinker, A. C. D., Wülbern, J. H., Tormo, A. G. I., Pappous, I., & Sénégas, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3136603 · Cited by 12
Multispectral Depolarization Based Living-Skin Detection: A New Measurement Principle
Wang, W., Shu, H., Lu, H., Xu, M. F., & Ji, X. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3356410 · Cited by 12
Blackford, E. B., & Estepp, J. R. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253460 · Cited by 12
Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts
Chen, Q., Wang, Y., Liu, X., Long, X., Yin, B., Chen, C., & Chen, W. (2021) · BioMedical Engineering OnLine · doi:10.1186/s12938-021-00958-5 · Cited by 12
Qayyum, A., Malik, A. S., Shuaibu, A. N., & Nasir, N. (2017) · IEEE Life Sciences Conference (LSC) · doi:10.1109/lsc.2017.8268178 · Cited by 12
A survey of blood oxygen saturation assessment from video
Wuyart, A., Vuillaume, L. A., Maaoui, C., & Bousefsaf, F. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108069 · Cited by 11
Soli-enabled noncontact heart rate detection for sleep and meditation tracking
Xu, L., Lien, J., Li, H., Gillian, N. E., Nongpiur, R. C., Li, J., Zhang, Q., Cui, J., Jorgensen, D., Bernstein, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-44714-2 · Cited by 13
Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring
Zhu, S., Liu, S., Jing, X., Yang, Y., & She, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-70192-1 · Cited by 11
Robust Imaging Photoplethysmography in Long-Distance Motion
Kong, L., Wu, Y., Zhao, Y., Dong, L., Hui, M., Liu, M., & Liu, X. (2020) · IEEE photonics journal · doi:10.1109/jphot.2020.2995397 · Cited by 11
Identifying Rhythmic Patterns for Face Forgery Detection and Categorization
Liang, J., & Deng, W. (2021) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484400 · Cited by 11
Assessment of physiological states from contactless face video: a sparse representation approach
Qayyum, A., Mazher, M., Shuaibu, A. N., Benzinou, A., Malik, A. S., & Razzak, I. (2022) · Computing · doi:10.1007/s00607-021-01028-3 · Cited by 11
Implementation of smart phone video plethysmography and dependence on lighting parameters
Fletcher, R., Chamberlain, D. B., Paggi, N., & Deng, X. L. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319208 · Cited by 11
Wang, Z., Yang, X. Y., & Cheng, K.-T. T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197275 · Cited by 11
Agile mobile robotic platform for contactless vital signs monitoringPreprint
Huang, H., Chai, P. R., Ehmke, C., Merewether, G. B., Dadabhoy, F. Z., Feng, A., Thomas, A. J., Li, C., Silva, M. J. T. D., Raibert, M. H., et al. (2020) · doi:10.36227/techrxiv.12811982 · Cited by 11
Mehta, A. D., & Sharma, H. K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105193 · Cited by 10
Liu, Y., Guo, X., & Zhang, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105461 · Cited by 10
Realistic Pulse Waveforms Estimation via Contrastive Learning in Remote Photoplethysmography
Dong, B., Liu, Y., Yang, K., & Cao, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3411135 · Cited by 10
RS+rPPG: Robust Strongly Self-Supervised Learning for rPPG
Savić, M., & Zhao, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3544676 · Cited by 10
Probabilistic Signal Quality Metric for Reduced Complexity Unsupervised Remote Photoplethysmography
Benezeth, Y., Bobbia, S., Nakamura, K., Gómez, R., & Dubois, J. (2019) · International Symposium on Medical Information and Communication Technology · doi:10.1109/ismict.2019.8744004 · Cited by 10
Zhang, Q., Lin, X., Zhang, Y., Liu, Q., & Cai, F. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02884-1 · Cited by 10
Saikia, T., Birla, L., Gupta, A. K., & Gupta, P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3334359 · Cited by 10
Lomaliza, J.-P., Park, H., & Billinghurst, M. (2020) · IEEE Access · doi:10.1109/access.2020.3045387 · Cited by 10
Recovery of Independent Sparse Sources From Linear Mixtures Using Sparse Bayesian Learning
Fouladi, S. H., Chiu, S.-E., Rao, B. D., & Balasingham, I. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2875419 · Cited by 10
Xiang, G., Ou, Y., Li, J., Wang, L., Hu, Y., Wang, X., Wu, X., & Peng, Y. L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3386859 · Cited by 9
Liu, I., Liu, F., Zhong, Q., & Ni, S. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01365-w · Cited by 13
Wang, Z., Yang, X., Lu, H., Shan, C., & Wang, W. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095078 · Cited by 9
Gao, H., Zhang, C., Pei, S., & Wu, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3363786 · Cited by 9
PhySU-Net: Long Temporal Context Transformer for rPPG with Self-supervised Pre-training
Savić, M., & Zhao, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78341-8_15 · Cited by 9
Fukunishi, M., Kurita, K., Yamamoto, S., & Tsumura, N. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00180 · Cited by 9
Motion-tolerant heart rate estimation from face videos using derivative filter
Zhao, Y. S., Yang, X., & Wu, X. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07849-x · Cited by 9
Borik, Š., Lyra, S., Paul, M., Antink, C. H., Leonhardt, S. K., & Blažek, V. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab87b3 · Cited by 9
‘No Touch’ Vitals: A Pilot Study of Non-contact Vital Signs Acquisition in Exercising Volunteers
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Non-Contact Heart Rate Measurement from Deteriorated Videos
Nguyen, N. V., Nguyen, L. N., Casado, C. Á., Sílven, O., & López, M. B. (2023) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa54631.2023.10275673 · Cited by 9
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Sarkar, M., & Assaad, M. M. (2022) · Sensors · doi:10.3390/s22207796 · Cited by 9
A multispectral testbed for cardiovascular sensing using imaging photoplethysmography
Blackford, E. B., & Estepp, J. R. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253447 · Cited by 8
Ryu, J., Hong, S., Liang, S., Pak, S., Chen, Q., & Yan, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3083917 · Cited by 8
Digital and Physical-World Attacks on Remote Pulse Detection
Speth, J., Vance, N., Flynn, P. J., Bowyer, K. W., & Czajka, A. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00285 · Cited by 8
He, L., Alam, K. S., Ma, J., Burkholder, E., Chu, W. C., Iqbal, A., & Ahamed, S. I. (2021) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh52753.2021.00025 · Cited by 8
Pruning rPPG Networks: Toward Small Dense Network with Limited Number of Training Samples
Zhao, C., Cao, P., Xu, S., Li, Z. G., & Feng, Y. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00223 · Cited by 9
Varma, M. D., Sequeira, T. F., Naidu, N. K. S., Mallya, Y., Sunkara, A., Patil, P., Poojary, N., Vaidyanathan, M. K., Balmaekers, B., Thomas, J., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-065790 · Cited by 8
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Wang, Y., Wang, W., van Gastel, M., & de Haan, G. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00212 · Cited by 8
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Chari, P., Kabra, K., Karinca, D., Lahiri, S., Srivastava, D., Kulkarni, K., Chen, T., Cannesson, M. P., Jalilian, L., & Kadambi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12769 · Cited by 12
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Cao, M., Cheng, X., Liu, X., Jiang, Y. L., Yu, H., & Shi, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3400869 · Cited by 7
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Zhao, C., Wang, H., & Feng, Y. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.07.001 · Cited by 7
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Chen, X., Yang, G., Li, Y., Xie, Q., & Liu, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106650 · Cited by 7
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Volkov, I. Y., Сагайдачный, А. А., & Фомин, А. В. (2022) · Optics and Spectroscopy · doi:10.1134/s0030400x22080057 · Cited by 7
Zheng, K., Kong, J., Li, T., Li, B., Li, H., & Zhou, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104898 · Cited by 7
A fast non-contact imaging photoplethysmography method using a tissue-like model
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Direct-Global Separation for Improved Imaging Photoplethysmography
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SHINE: Synergizing transformers with contrastive learning for thriving rPPG-based SpO2 estimation
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Huang, H., Rupp, P., Chen, J., Kemkar, A., Khandelwal, N., Ballinger, I., Chai, P. R., & Traverso, G. (2022) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors52175.2022.9967120 · Cited by 6
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Xu, Q., Qiao, H., Liu, S., & Liu, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14744-z · Cited by 6
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Remote Photoplethysmography Enhancement with Machine Leaning Methods
Wu, B., Huang, P., He, D.-H., Lin, C.-H., & Chen, K. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914554 · Cited by 6
Qiu, Z., Lin, L., Sun, H., Liu, J., & Chen, Y. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_11 · Cited by 6
Sun, Y., Zhang, Z., Qiu, C., Wang, L., Sun, L., & Wang, Z. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778420 · Cited by 10
Skin tissue perfusion mapping triggered by an audio-(de)modulated reference signal
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Exploiting Multiwavelength Morphological Features of Camera-PPG for Blood Pressure Estimation
Hou, Z., Huang, D., Huang, Y., Huang, J., Zhao, N., Pan, L., Lu, H., Shan, C., & Wang, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551005 · Cited by 6
MSPhys: multiscale fusing-based diffusion model for remote physiological measurement
Su, G., Qian, W., Li, Q., Wu, Y., Feng, W., & Guo, D. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01720-w · Cited by 5
Arrow, C., Ward, M., Eshraghian, J. K., & Dwivedi, G. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110956 · Cited by 5
Huo, C., Yin, P., & Fu, B. (2024) · Sensors · doi:10.3390/s25010100 · Cited by 5
Brinker, A. C. D., & Wang, W. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822281-2.00011-1 · Cited by 5
Firmansyah, R. A., Prabowo, Y. A., Suheta, T., & Muharom, S. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v29.i3.pp1326-1335 · Cited by 5
Lingqin, 孔. K., Yuejin, 赵. Z., Dong, L., Ming, 刘. L., Ge, 徐. X., Mei, 惠. H., & Xuhong, 褚. C. (2023) · Acta Optica Sinica · doi:10.3788/aos230755 · Cited by 5
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STFPNet: A simple temporal feature pyramid network for remote heart rate measurement
Ли, Ж., Xiao, H., Xia, Z., Liu, T., Huangang, X., Zhou, F., & Jiang, J. (2025) · Measurement · doi:10.1016/j.measurement.2025.117287 · Cited by 5
Continuous-Spectrum Infrared Illuminator for Camera-PPG in Darkness
Wang, W., Vosters, L., & Brinker, A. C. D. (2020) · Sensors · doi:10.3390/s20113044 · Cited by 5
A Multiresolution Method for Non-Contact Heart Rate Estimation Using Facial Video Frames
Das, M., Choudhary, T., Bhuyan, M. K., Sharma, L. N., & Dutta, H. (2022) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet54241.2022.9767186 · Cited by 5
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Zhou, K., Schinle, M., Weimar, S. N., Gerdes, M., Stock, S., & Stork, W. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995577 · Cited by 5
Yue, H., Li, X., Wang, H., Chen, H., Wang, X., & Cai, K. (2019) · IEEE Access · doi:10.1109/access.2019.2932204 · Cited by 5
Kobayashi, L., Chuck, C. C., Kim, C. K., Luchette, K. R., Oster, A., Merck, D. L., Kirenko, I. O., van Zon, K., Bartula, M., Rocque, M., et al. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993087 · Cited by 5
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Wedekind, D., Trumpp, A., Andreotti, F., Gaetjen, F., Rasche, S., Matschke, K., Malberg, H., & Zaunseder, S. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362420 · Cited by 5
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Ba, Y., Wang, Z., Karinca, D., Bozkurt, O. D., & Kadambi, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06007 · Cited by 10
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Jiang, T., Ma, Y., Li, J., Dong, Q., Ji, X., & Liu, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3538474 · Cited by 4
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Qiu, X., Ye, L.-T., Liu, X.-P., Teng, L.-Z., Tian, S.-Q., & Jiang, S. (2025) · Asian Journal of Surgery · doi:10.1016/j.asjsur.2025.04.010 · Cited by 4
Zhan, X., Zhou, C., Wei, Y., Dahlweid, F.-M., Sun, H., Wang, C., Xie, B., & Zhang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108407 · Cited by 4
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Li, H., Lü, H., & Chen, Y.-C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73247-8_21 · Cited by 6
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Kim, S., Lee, K., Hwang, J., Lee, H., & Lee, E. C. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.6.063058 · Cited by 4
Zhang, Z., Lu, H., He, Z., Al-Azzawi, A., Ma, S., & Lin, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129717 · Cited by 4
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Li, Q., Guo, D., Qian, W., Zhou, Y., Zhao, H., Sun, X., & Wang, M. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3559385 · Cited by 4
Metadata
- Authors
- de Haan, G., & van Leest, A.
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2014-08-27
- Volume
- 35
- Issue
- 9
- Pages
- 1913-1926
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2044935899
- Cited by
- 491
- References
- 16
- Citations by year
- 2026: 39 · 2025: 77 · 2024: 67 · 2023: 56 · 2022: 70 · 2021: 42 · 2020: 28 · 2019: 32 · 2018: 34 · 2017: 26 · 2016: 12 · 2015: 8
- FWCI
- 7.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Keywords
- remote photoplethysmography, blood volume pulse, motion robustness, RGB color space, pulse rate detection, signal-to-noise ratio, non-contact vital sign monitoring, exercise monitoring
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