Robust pulse rate from chrominance-based rPPG
Abstract
Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement ( ±1.96σ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.
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Numerous other studies that stratify results against Fitzpatrick classification identify the same trend: errors in determining heart rate from wearable PPG devices that use primarily green light are increased in individuals with dark skin tones due to high absorption caused by increased amounts of epidermal melanin.
“In addition to these examples, numerous other studies that stratify results against Fitzpatrick classification identify the same trend; errors in determining heartrate from wearable PPG devices that use primarily green light as their source is increased in individuals with dark skin tones due to the high absorption caused by increased amounts of epidermal melanin [42,43,44,45].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [43]
Cites
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · 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
High-Order Contrasts for Independent Component Analysis
Cardoso, J.-F. (1999) · Neural Computation · doi:10.1162/089976699300016863 · Cited by 1,209 · 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
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
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
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
Dichromatic reflection models for a variety of materials
Tominaga, S. (1994) · Color Research & Application · doi:10.1002/col.5080190408 · Cited by 66 · 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
The wavelength dependence of the photoplethysmogram and its implication to pulse oximetry
Crowe, J. A., & Damianou, D. (1992) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.1992.5761522 · Cited by 32 · see citing works
Human Skin Detection by Visible and Near-Infrared Imaging
Kimura, Y., & Naito, T. (2011) · Machine Vision and Applications · Cited by 31 · see citing works
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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
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Ciftci, U. A., Demir, I., & Yin, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3009287 · Cited by 499 · see citing works
Improved motion robustness of remote-PPG by using the blood volume pulse signature
de Haan, G., & van Leest, A. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/9/1913 · Cited by 491 · see citing works
Self-citationSources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348 · see citing works
Simultaneous monitoring of ballistocardiogram and photoplethysmogram using a camera
Shao, D., Tsow, F., Liu, C., Yang, Y., & Tao, N. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2585109 · Cited by 69 · see citing works
Nowara, E. M., McDuff, D., & Veeraraghavan, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00150 · Cited by 66 · see citing works
Moço, A., & Verkruysse, W. (2021) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-019-00449-y · Cited by 55 · see citing works
Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
Ur Rehman Ahmed, N., Badshah, A., Adeel, H., Tajammul, A., Daud, A., & Alsahfi, T. (2025) · IEEE Access · doi:10.1109/access.2024.3523288 · Cited by 45 · see citing works
Addison, P. S., Jacquel, D., Foo, D. M. H., & Borg, U. R. (2018) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-017-0076-1 · Cited by 24 · see citing works
PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows
Comas, J., Alomar, A., Ruiz, A., & Sukno, F. (2024) · arXiv · doi:10.48550/arxiv.2407.21519 · Cited by 4 · see citing works
“Also, we include some traditional methods such as POS [60] and CHROM [12] as baseline results.”
Method
- Authors
- de Haan, G., & Jeanne, V.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2013-10
- Volume
- 60
- Issue
- 10
- Pages
- 2878-2886
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2069692225
- Cited by
- 1,378
- References
- 13
- Citations by year
- 2026: 135 · 2025: 210 · 2024: 191 · 2023: 194 · 2022: 158 · 2021: 133 · 2020: 89 · 2019: 83 · 2018: 79 · 2017: 49 · 2016: 26 · 2015: 19 · 2014: 10 · 2013: 2
- FWCI
- 10.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- remote photoplethysmography, pulse rate estimation, chrominance, motion robustness, blind source separation, RGB color space, blood volume pulse, non-contact vital sign monitoring, spectral peak detection, exercise monitoring
- MeSH terms
- Algorithms, Blood Flow Velocity, Colorimetry, Diagnosis, Computer-Assisted, Heart Rate, Humans, Photography, Sensitivity and Specificity, Skin Physiological Phenomena, Reproducibility of Results, Photoplethysmography
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