The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
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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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
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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
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Passive blood plasma separation at the microscale: a review of design principles and microdevices
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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
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- Lewandowska M, 2011, Proc. FedCSIS, 18–21, 405
- Glass K A, 2004, Proc. 5th Int. Conf. ICA
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
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
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
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 589
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
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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