Frequency Tracker for Unsupervised Heart Rate Estimation

Iskander Zhalbekov, Leonid Beynenson, Alexey Trushkov, Ivan Bulychev, Wenshuai Yin

CVPRW 2023 pp. 6024-6033

doi:10.1109/CVPRW59228.2023.00641 /cvprw/2023/zhalbekov2023cvprw-frequency/

Abstract

We present frequency tracking for extracting heart rate trace from blood volume pulse (BVP) signal that can be used as an alternative for commonly used approach based on the mode of the BVP signal power spectral density. Our approach is based on particle filtering framework which provides smooth heart rate estimate, it is robust to motion-induced artifacts and noise. The method could be easily tuned and can be coupled with unsupervised BVP extraction approaches without the need for training. We evaluate our method on publicly available part of LGI dataset. Proposed algorithm shows competitive results comparing to argmax approach.

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Cite

Text

Zhalbekov et al. "Frequency Tracker for Unsupervised Heart Rate Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00641

Markdown

[Zhalbekov et al. "Frequency Tracker for Unsupervised Heart Rate Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/zhalbekov2023cvprw-frequency/) doi:10.1109/CVPRW59228.2023.00641

BibTeX

@inproceedings{zhalbekov2023cvprw-frequency,
  title     = {{Frequency Tracker for Unsupervised Heart Rate Estimation}},
  author    = {Zhalbekov, Iskander and Beynenson, Leonid and Trushkov, Alexey and Bulychev, Ivan and Yin, Wenshuai},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {6024-6033},
  doi       = {10.1109/CVPRW59228.2023.00641},
  url       = {https://mlanthology.org/cvprw/2023/zhalbekov2023cvprw-frequency/}
}