ML Anthology

Optimizing Camera Exposure Control Settings for Remote Vital Sign Measurements in Low-Light Environments

Ismoil Odinaev, Jing Wei Chin, Kin Ho Luo, Zhang Ke, Richard Hau Yue So, Kwan Long Wong
CVPRW 2023 pp. 6086-6093
doi:10.1109/CVPRW59228.2023.00648 /cvprw/2023/odinaev2023cvprw-optimizing/

Abstract

Remote photoplethysmography (rPPG) is an optical technique that enables both non-invasive and efficient measurement of vital signs from facial videos. However, the quality of rPPG measurements can be adversely affected by improper camera exposure control and bad lighting conditions. In this paper, we present a systematic study of camera exposure control settings, specifically gain and exposure time, in low-light environments. Our results indicate that manual adjustment of gain and exposure time can significantly improve the quality of rPPG measurements, enabling accurate vital sign measurement even in environments with illuminance levels as low as 25 lux. Furthermore, we demonstrate that the optimal brightness range for rPPG-based vital sign measurement depends on the sensitivity of the vital sign to the shape and peaks of the rPPG signal. These findings have important practical implications for the use of rPPG in healthcare and remote monitoring applications.

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Cite

Text

Odinaev et al. "Optimizing Camera Exposure Control Settings for Remote Vital Sign Measurements in Low-Light Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00648

Markdown

[Odinaev et al. "Optimizing Camera Exposure Control Settings for Remote Vital Sign Measurements in Low-Light Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/odinaev2023cvprw-optimizing/) doi:10.1109/CVPRW59228.2023.00648

BibTeX

@inproceedings{odinaev2023cvprw-optimizing,
  title     = {{Optimizing Camera Exposure Control Settings for Remote Vital Sign Measurements in Low-Light Environments}},
  author    = {Odinaev, Ismoil and Chin, Jing Wei and Luo, Kin Ho and Ke, Zhang and So, Richard Hau Yue and Wong, Kwan Long},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {6086-6093},
  doi       = {10.1109/CVPRW59228.2023.00648},
  url       = {https://mlanthology.org/cvprw/2023/odinaev2023cvprw-optimizing/}
}