Remote Photoplethysmography: Rarely Considered Factors
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Abstract (arXiv)
Remote Photoplethysmography (rPPG) is a fast-growing technique of vital sign estimation by analyzing video of a person. Several major phenomena affecting rPPG signals have been studied (e.g. video compression, distance from person to camera, skin tone, head motions). However, to develop a highly accurate rPPG method, new, minor, factors should be investigated. First considered factor is irregular frame rate of video recordings. Despite of PPG signal transformation by frame rate irregularity, no significant distortion of PPG signal spectra was found in the experiments. Second factor is rolling shutter effect which generates tiny phase shift of the same PPG signal in different parts of the frame caused by progressive scanning. In particular conditions effect of this artifact could be of the same order of magnitude as physiologically caused phase shifts. Third factor is a size of temporal windows, which could significantly influence the estimated error of vital sign evaluation. It follows that one should account difference in size of processing windows when comparing rPPG methods. Short series of experiments were conducted to estimate importance of these phenomena and to determine necessity of their further comprehensive study.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 2.
9 papers.
- 2023Non-Contrastive Unsupervised Learning of Physiological Signals from Videocited by 37
- 2022Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videoscited by 26
- 2022Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Losscited by 12
- 2023Full-Body Cardiovascular Sensing with Remote Photoplethysmographycited by 5
- 2023Promoting Generalization in Cross-Dataset Remote Photoplethysmographycited by 2
- 2024SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signalscited by 2
- 2024Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resiliencecited by 2
- 2021Remote Pulse Estimation in the Presence of Face Maskscited by 1
- 2023Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimationcited by 1
- 2024Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience2 May 2024
- 2024SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals20 Apr 2024
- 2023Promoting Generalization in Cross-Dataset Remote Photoplethysmography24 May 2023
- 2023Full-Body Cardiovascular Sensing with Remote Photoplethysmography16 Mar 2023
- 2023Non-Contrastive Unsupervised Learning of Physiological Signals from Video14 Mar 2023
- 2023Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation11 Mar 2023
- 2022Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos30 Nov 2022
- 2022Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss21 Mar 2022
- 2021Remote Pulse Estimation in the Presence of Face Masks11 Jan 2021