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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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.
“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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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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 402 · see citing works
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 · see citing works
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 · see citing works
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 217 · see citing works
Sun, Y., Hu, S., Azorin-Peris, V., Chambers, J. A., Zhu, Y., & Greenwald, S. E. (2011) · Journal of Biomedical Optics · doi:10.1117/1.3602852 · Cited by 213 · see citing works
Radar sensing of heartbeat and respiration at a distance with applications of the technology
Greneker, E. F. (1997) · doi:10.1049/cp:19971650 · Cited by 149 · see citing works
Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI
Huelsbusch, M., & Blažek, V. (2002) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.463573 · Cited by 110 · see citing works
Optimal wavelength selection for noncontact reflection photoplethysmography
Corral-Martínez, L. F., Паез, Г., & Strojnik, M. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.903190 · Cited by 55 · see citing works
A Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG
Glass, K., Frishkoff, G. A., Frank, R. M., Davey, C., Dien, J., Malony, A. D., & Tucker, D. M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30110-3_130 · Cited by 42 · see citing works
Flagged first, then by citations
1 of the 16 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Algorithmic principles of remote PPG
Wang, W., den Brinker, A. C., Stuijk, S., & de Haan, G. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2609282 · Cited by 1,234 · see citing works
Self-citationDeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
Chen, W., & McDuff, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_22 · Cited by 716 · see citing works
Unsupervised skin tissue segmentation for remote photoplethysmography
Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., & Dubois, J. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.10.017 · Cited by 619 · see citing works
Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging
Sun, Y., & Thakor, N. V. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2476337 · Cited by 567 · see citing works
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation
Niu, X., Shan, S., Han, H., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2947204 · Cited by 421 · see citing works
A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation
Wang, W., Stuijk, S., & de Haan, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2508602 · Cited by 369 · see citing works
Self-citationNew principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring
van Gastel, M., Stuijk, S., & de Haan, G. (2016) · Scientific Reports · doi:10.1038/srep38609 · Cited by 253 · see citing works
Self-citationCurrent progress of photoplethysmography and SPO2 for health monitoring
TAMURA, T. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-019-00097-w · Cited by 260 · see citing works
Video-Based Heart Rate Measurement: Recent Advances and Future Prospects
Chen, X., Cheng, J., Song, R., Liu, Y., Ward, R. K., & Wang, Z. J. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2879706 · Cited by 228 · see citing works
A survey of remote optical photoplethysmographic imaging methods
McDuff, D., Estepp, J. R., Piasecki, A. M., & Blackford, E. B. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319857 · Cited by 212 · see citing works
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement
Lü, H., Han, H., & Zhou, S. K. (2021) · 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/cvpr46437.2021.01222 · Cited by 204 · see citing works
Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
Almarshad, M. A., Islam, M. S., Al-Ahmadi, S. A., & BaHammam, A. S. (2022) · Healthcare · doi:10.3390/healthcare10030547 · Cited by 200 · see citing works
Remote heart rate measurement using low-cost RGB face video: a technical literature review
Rouast, P. V., Adam, M., Chiong, R., Cornforth, D., & Lux, E. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-6243-6 · Cited by 188 · see citing works
Heart rate estimation using facial video: A review
Hassan, M. A., Malik, A. S., David Fofi, B., Saad, M. N. M., Karasfi, B., Ali, Y. S., & Mériaudeau, F. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.07.004 · Cited by 183 · see citing works
SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared
Nowara, E. M., Marks, T. K., Mansour, H., & Veeraraghavan, A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00174 · Cited by 174 · see citing works
Camera Measurement of Physiological Vital Signs
McDuff, D. (2022) · ACM Computing Surveys · doi:10.1145/3558518 · Cited by 167 · see citing works
Non-contact measurement of oxygen saturation with an RGB camera
Guazzi, A., Villarroel, M., Jorge, J., Daly, J., Frise, M. C., Robbins, P. A., & Tarassenko, L. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.003320 · Cited by 151 · see citing works
Local Group Invariance for Heart Rate Estimation from Face Videos in the Wild
Pilz, C. S., Zaunseder, S., Krajewski, J., & Blažek, V. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00172 · Cited by 149 · see citing works
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
Schrumpf, F., Frenzel, P., Aust, C., Osterhoff, G., & Fuchs, M. (2021) · Sensors · doi:10.3390/s21186022 · Cited by 146 · see citing works
Monitoring of Cardiorespiratory Signal: Principles of Remote Measurements and Review of Methods
Al‐Naji, A., Gibson, K., Lee, S., & Chahl, J. (2017) · IEEE Access · doi:10.1109/access.2017.2735419 · Cited by 133 · see citing works
Motion Robust Remote-PPG in Infrared
van Gastel, M., Stuijk, S., & de Haan, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2390261 · Cited by 126 · see citing works
Self-citationRobust respiration detection from remote photoplethysmography.
van Gastel, M. V., Stuijk, S., & de Haan, G. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.004941 · Cited by 126 · see citing works
Self-citationContrast-Phys: Unsupervised Video-Based Remote Physiological Measurement via Spatiotemporal Contrast
Sun, Z., & Li, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_29 · Cited by 127 · see citing works
Cabanas, A. M., Fuentes-Guajardo, M., Latorre, K., León, D., & Martín-Escudero, P. (2022) · Sensors · doi:10.3390/s22093402 · Cited by 124 · see citing works
Cardiovascular assessment by imaging photoplethysmography – a review
Zaunseder, S., Trumpp, A., Wedekind, D., & Malberg, H. (2018) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2017-0119 · Cited by 121 · see citing works
Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda
Cheng, C.-H., Wong, K. L., Chin, J. W., Chan, T. T., & So, R. H. Y. (2021) · Sensors · doi:10.3390/s21186296 · Cited by 122 · see citing works
Robust heart rate from fitness videos
Wang, W., Brinker, A. C. D., Stuijk, S., & de Haan, G. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa6d02 · Cited by 114 · see citing works
Self-citationAn Open Framework for Remote-PPG Methods and Their Assessment
Boccignone, G., Conte, D., Cuculo, V., D’Amelio, A., Grossi, G., & Lanzarotti, R. (2020) · IEEE Access · doi:10.1109/access.2020.3040936 · Cited by 109 · see citing works
Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces
Casado, C. Á., & López, M. B. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3307942 · Cited by 109 · see citing works
Remote photoplethysmography for heart rate measurement: A review
Xiao, H., Liu, T., Sun, Y., Li, Y., Zhao, S., & Avolio, A. P. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105608 · Cited by 109 · see citing works
McDuff, D., Blackford, E. B., & Estepp, J. R. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.17 · Cited by 103 · see citing works
Evaluation of biases in remote photoplethysmography methods
Dasari, A., Prakash, S. K. A., Jeni, L. A., & Tucker, C. S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00462-z · Cited by 98 · see citing works
Song, R., Zhang, S., Cheng, J., Li, C., & Chen, X. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103535 · Cited by 96 · see citing works
Khanam, F.-T.-Z., Al‐Naji, A., & Chahl, J. (2019) · Applied Sciences · doi:10.3390/app9204474 · Cited by 95 · see citing works
rPPG-MAE: Self-Supervised Pretraining With Masked Autoencoders for Remote Physiological Measurements
Liu, X., Zhang, Y., Yu, Z., Lü, H., Yue, H., & Yang, J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3363660 · Cited by 97 · see citing works
Discriminative Signatures for Remote-PPG
Wang, W., Brinker, A. C. D., & de Haan, G. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2938564 · Cited by 86 · see citing works
Self-citationJAMSNet: A Remote Pulse Extraction Network Based on Joint Attention and Multi-Scale Fusion
Zhao, C., Wang, H., Chen, H.-L., Shi, W., & Feng, Y.-J. (2023) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2022.3227348 · Cited by 77 · see citing works
Analysis of CNN-based remote-PPG to understand limitations and sensitivities
Zhan, Q., Wang, W., & de Haan, G. (2020) · Biomedical Optics Express · doi:10.1364/boe.382637 · Cited by 82 · see citing works
Self-citationNon-Contrastive Unsupervised Learning of Physiological Signals from Video
Speth, J., Vance, N., Flynn, P. J., & Czajka, A. (2023) · 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/cvpr52729.2023.01390 · Cited by 80 · see citing works
Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle
Al‐Naji, A., Perera, A. G., & Chahl, J. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0395-y · Cited by 79 · see citing works
Михайлович, У. А. (2018) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/aabd09 · Cited by 78 · see citing works
Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation
Gudi, A., Bittner, M., & van Gemert, J. C. (2020) · Applied Sciences · doi:10.3390/app10238630 · Cited by 77 · see citing works
pyVHR: a Python framework for remote photoplethysmography
Boccignone, G., Conte, D., Cuculo, V., D’Amelio, A., Grossi, G., Lanzarotti, R., & Mortara, E. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.929 · Cited by 76 · see citing works
Wang, W., Brinker, A. C. D., & de Haan, G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2882396 · Cited by 76 · see citing works
Self-citationMMPD: Multi-Domain Mobile Video Physiology Dataset
Tang, J., Chen, K., Wang, Y., Shi, Y., Patel, S., McDuff, D., & Liu, X. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340857 · Cited by 75 · see citing works
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Kim, D., Lee, K., & Sohn, C.-B. (2021) · Sensors · doi:10.3390/s21237923 · Cited by 72 · see citing works
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Huang, H., Chen, J., Chai, P. R., Ehmke, C., Rupp, P., Dadabhoy, F. Z., Feng, A., Li, C., Thomas, A. J., Silva, M. J. T. D., et al. (2022) · Cyborg and Bionic Systems · doi:10.34133/2022/9780497 · Cited by 72 · see citing works
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Lu, H. F., Yu, Z., Niu, X., & Chen, Y.-C. (2023) · 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/cvpr52729.2023.01783 · Cited by 71 · see citing works
Sun, Z., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367910 · Cited by 71 · see citing works
An open-source remote heart rate imaging method with practical apparatus and algorithms
van der Kooij, K. M., & Naber, M. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01256-8 · Cited by 70 · see citing works
Assessment of deep learning based blood pressure prediction from PPG and rPPG signals
Schrumpf, F., Frenzel, P., Aust, C., Osterhoff, G., & Fuchs, M. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00423 · Cited by 70 · see citing works
Challenges and prospects of visual contactless physiological monitoring in clinical study
Huang, B., Hu, S., Liu, Z., Lin, C., Su, J., Zhao, C., Wang, L., & Wang, W. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00973-x · Cited by 72 · see citing works
Analysis of vital signs using remote photoplethysmography (RPPG)
Karthick, R. V., Dawood, M. S., & Meenalochini, P. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04683-w · Cited by 67 · see citing works
Remote photoplethysmography with constrained ICA using periodicity and chrominance constraints
Macwan, R., Benezeth, Y., & Mansouri, A. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0450-3 · Cited by 65 · see citing works
Khanam, F.-T.-Z., Perera, A. G., Al‐Naji, A., Gibson, K., & Chahl, J. (2021) · Journal of Imaging · doi:10.3390/jimaging7080122 · Cited by 63 · see citing works
Heart rate estimation using remote photoplethysmography with multi-objective optimization
Macwan, R., Benezeth, Y., & Mansouri, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.10.012 · Cited by 61 · see citing works
Living-Skin Classification via Remote-PPG
Wang, W., Stuijk, S., & de Haan, G. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2676160 · Cited by 60 · see citing works
Self-citationNon-contact breathing rate monitoring in newborns: A review
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Skin inhomogeneity as a source of error in remote PPG-imaging
Moço, A., Stuijk, S., & de Haan, G. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.004718 · Cited by 57 · see citing works
Self-citationAmplitude-selective filtering for remote-PPG
Wang, W., Brinker, A. C. D., Stuijk, S., & de Haan, G. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.001965 · Cited by 55 · see citing works
Self-citationRhythmFormer: Extracting patterned rPPG signals based on periodic sparse attention
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Motion robust PPG-imaging through color channel mapping
Moço, A., Stuijk, S., & de Haan, G. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.001737 · Cited by 53 · see citing works
Self-citationFM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction
Zhu, F., Niu, Q., Li, X., Zhao, Q., Su, H., & Shuai, J. (2024) · Research · doi:10.34133/research.0361 · Cited by 51 · see citing works
Fouad, R. M., Omer, O. A., & Aly, M. H. (2019) · IEEE Access · doi:10.1109/access.2019.2922304 · Cited by 51 · see citing works
Contactless Patient Care Using Hospital IoT: CCTV-Camera-Based Physiological Monitoring in ICU
Wang, H., Huang, J., Wang, G., Lu, H., & Wang, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308477 · Cited by 50 · see citing works
Deep Super Resolution for Recovering Physiological Information from Videos
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RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photoplethysmography
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Review on remote heart rate measurements using photoplethysmography
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Illumination variation-resistant video-based heart rate monitoring using LAB color space
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Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability
Gudi, A., Bittner, M., Lochmans, R., & van Gemert, J. C. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00196 · Cited by 43 · see citing works
Intelligent Recognition of Fatigue and Sleepiness Based on InceptionV3-LSTM via Multi-Feature Fusion
Zhao, Y., Xie, K., Zou, Z., & He, J. (2020) · IEEE Access · doi:10.1109/access.2020.3014508 · Cited by 43 · see citing works
Luguern, D., Macwan, R., Benezeth, Y., Moser, V., Dunbar, L. A., Braun, F., Lemkaddem, A., & Dubois, J. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102263 · Cited by 41 · see citing works
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Lin, Y.-C., & Lin, Y. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037807 · Cited by 40 · see citing works
Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement
McDuff, D., Blackford, E. B., & Estepp, J. R. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2771518 · Cited by 40 · see citing works
Wu, B., Chu, Y.-W., Huang, P., & Chung, M.-L. (2019) · IEEE Access · doi:10.1109/access.2019.2913664 · Cited by 40 · see citing works
Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions
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Kamshilin, A. A., Zaytsev, V. V., Lodygin, A. V., & Кащенко, В. А. (2022) · Scientific Reports · doi:10.1038/s41598-022-05080-7 · Cited by 39 · see citing works
Fully-Automatic Camera-Based Pulse-Oximetry During Sleep
Vogels, T., van Gastel, M., Wang, W., & de Haan, G. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00183 · Cited by 38 · see citing works
Self-citationGupta, A. R., Ravelo-García, A. G., & Morgado‐Dias, F. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106771 · Cited by 38 · see citing works
Zhang, X., Hu, M., Zhang, Y., Zhai, G., & Zhang, X.-P. S. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200345 · Cited by 37 · see citing works
Camera-based physiological measurement: Recent advances and future prospects
Wang, J., Shan, C., Liu, L., & Hou, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127282 · Cited by 40 · see citing works
iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels
Joshi, J., & Cho, Y. (2024) · Electronics · doi:10.3390/electronics13071334 · Cited by 37 · see citing works
Liu, Y., Qin, B., Li, R., Li, X., Huang, A., Liu, H., Lv, Y., & Liu, M. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3060572 · Cited by 38 · see citing works
Premkumar, S., & Hemanth, D. J. (2022) · Informatics · doi:10.3390/informatics9030057 · Cited by 37 · see citing works
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Haugg, F., Elgendi, M., & Menon, C. (2022) · Bioengineering · doi:10.3390/bioengineering9100485 · Cited by 37 · see citing works
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Blocher, T., Schneider, J., Schinle, M., & Stork, W. (2017) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2017.7894052 · Cited by 36 · see citing works
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Pirzada, P., Wilde, A., & Harris‐Birtill, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3405414 · Cited by 38 · see citing works
Wang, W., Brinker, A. C. D., & de Haan, G. (2018) · Biomedical Optics Express · doi:10.1364/boe.9.003898 · Cited by 34 · see citing works
Self-citationRemote Heart Rate Measurement from RGB-NIR Video Based on Spatial and Spectral Face Patch Selection
Kado, S., Monno, Y., Moriwaki, K., 吉崎和徳, Tanaka, M., & Okutomi, M. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513464 · Cited by 34 · see citing works
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McDuff, D., Nishidate, I., Nakano, K., Haneishi, H., Aoki, Y., Tanabe, C., Niizeki, K., & Aizu, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-67647-6 · Cited by 34 · see citing works
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Speth, J., Vance, N., Flynn, P. J., Bowyer, K. W., & Czajka, A. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103246 · Cited by 34 · see citing works
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Wang, W., Brinker, A. C. D., Stuijk, S., & de Haan, G. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.18 · Cited by 34 · see citing works
Self-citationVideo pulse rate variability analysis in stationary and motion conditions
Rodríguez, A., & Ramos-Castro, J. J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0437-0 · Cited by 34 · see citing works
Qian, W., Guo, D., Li, K., Zhang, X., Tian, X., Yang, X., & Wang, M. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3356713 · Cited by 33 · see citing works
Macwan, R., Bobbia, S., Benezeth, Y., Dubois, J., & Mansouri, A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00181 · Cited by 33 · see citing works
NAS-HR: Neural architecture search for heart rate estimation from face videos
Lü, H., & Han, H. (2021) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.10.002 · Cited by 33 · see citing works
Zheng, K., Ci, K., Li, H., Shao, L., Sun, G., Liu, J., & Cui, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103609 · Cited by 33 · see citing works
Al‐Naji, A., Al-Askery, A. J., Gharghan, S. K., & Chahl, J. (2019) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan8020032 · Cited by 33 · see citing works
An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring
Sinhal, R. A., Singh, K., & Raghuwanshi, M. M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-8798-2_3 · Cited by 32 · see citing works
- 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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