The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Non-contact, automated cardiac pulse measurements using video imaging and blind source separation
Open accessAbstract
Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.
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Citations
Cites
1 retracted
RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis
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RetractedSTATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
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Rapid object detection using a boosted cascade of simple features
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Independent component analysis, A new concept?
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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
Removing electroencephalographic artifacts by blind source separation
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Remote plethysmographic imaging using ambient light
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Cited by
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
Wearable photoplethysmographic sensors—past and present
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014) · Electronics · doi:10.3390/electronics3020282 · Cited by 912
Review on Psychological Stress Detection Using Biosignals
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2927337 · Cited by 764
DeepPhys: 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
Self-citationFakeCatcher: 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
Metadata
- Authors
- Poh, M.-Z., McDuff, D. J., & Picard, R. W.
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2010-05-07
- Volume
- 18
- Issue
- 10
- Pages
- 10762
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W1984554603
- Cited by
- 1,643
- References
- 33
- FWCI
- 16.02
- Type
- journal article
- Language
- en
- Open access
- gold
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- Poh, M.-Z., McDuff, D. J., & Picard, R. W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express. https://doi.org/10.1364/oe.18.010762
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
