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Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience

arXiv 2405.01230 ↗First posted 2 May 2024cs.CV eess.SPcited by 2

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Abstract (arXiv)

Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of vital signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware and network constraints. In this article, we systematically investigate comprehensive investigate these issues, measuring their detrimental effects on the quality of rPPG measurements. Additionally, we propose practical strategies for mitigating these challenges to improve the dependability and resilience of video-based rPPG systems. We detail methods for effective biosignal recovery in the presence of network limitations and present denoising and inpainting techniques aimed at preserving video frame integrity. Through extensive evaluations and direct comparisons, we demonstrate the effectiveness of the approaches in enhancing rPPG measurements under challenging environments, contributing to the development of more reliable and effective remote vital sign monitoring technologies.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 84 references that are in the map. First posted on arXiv 2 May 2024.

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23 of 84 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2022Simple Baselines for Image Restorationcited by 701
  3. 2018DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networkscited by 153
  4. 2020Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurementcited by 133
  5. 2021PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformercited by 118
  6. 2019RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representationcited by 104
  7. 2019Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancementcited by 86
  8. 2019BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUscited by 74
  9. 2019Real-time Facial Surface Geometry from Monocular Video on Mobile GPUscited by 72
  10. 2021The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Videocited by 66
  11. 2020PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmographycited by 59
  12. 2020Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learnercited by 58
11 more
  1. 2018VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Videocited by 57
  2. 2017A Reproducible Study on Remote Heart Rate Measurementcited by 49
  3. 2022Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrastcited by 47
  4. 2020AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searchingcited by 39
  5. 2023Non-Contrastive Unsupervised Learning of Physiological Signals from Videocited by 37
  6. 2022Face2PPG: An unsupervised pipeline for blood volume pulse extraction from facescited by 21
  7. 2019Analysis of CNN-based remote-PPG to understand limitations and sensitivitiescited by 19
  8. 2015DistancePPG: Robust non-contact vital signs monitoring using a cameracited by 16
  9. 2020Remote Photoplethysmography: Rarely Considered Factorscited by 9
  10. 2021A Subjective Study on Videos at Various Bit Depthscited by 3
  11. 2023Non-Contact Heart Rate Measurement from Deteriorated Videoscited by 3

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