ST2S-rPPG: A Spatiotemporal Two-Stage Learning Approach for Pulse Estimation Using Video

Eirini Kateri, Katayoun Farrahi
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:550-562, 2025.

Abstract

Remote physiological monitoring presents an opportunity to enhance patient care, particularly in scenarios where traditional monitoring methods are impractical or unavailable. Heart rate, being a principal indicator of health, has been a focal point of video-based monitoring systems. Despite significant advancements in remote photoplethysmography technology, several challenges persist, including motion artifacts, data homogeneity and availability, which impact the accuracy and reliability of such solutions. In this work, we introduce a novel framework aimed at addressing these challenges, ST2S-rPPG. Our methodology involves a stabilization method to mitigate motion artifacts. We propose a spatiotemporal representation of video data, which captures predictive available information in the video and assists in transforming the input video. We present a unique approach to ground truth representation for capturing more informative features. Finally, we incorporate a two-stage learning component into our framework to optimize estimation accuracy. Through evaluations on benchmark datasets, we demonstrate the effectiveness of our contributions and their practical relevance in healthcare applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-kateri25a, title = {ST2S-rPPG: A Spatiotemporal Two-Stage Learning Approach for Pulse Estimation Using Video}, author = {Kateri, Eirini and Farrahi, Katayoun}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {550--562}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/kateri25a/kateri25a.pdf}, url = {https://proceedings.mlr.press/v259/kateri25a.html}, abstract = {Remote physiological monitoring presents an opportunity to enhance patient care, particularly in scenarios where traditional monitoring methods are impractical or unavailable. Heart rate, being a principal indicator of health, has been a focal point of video-based monitoring systems. Despite significant advancements in remote photoplethysmography technology, several challenges persist, including motion artifacts, data homogeneity and availability, which impact the accuracy and reliability of such solutions. In this work, we introduce a novel framework aimed at addressing these challenges, ST2S-rPPG. Our methodology involves a stabilization method to mitigate motion artifacts. We propose a spatiotemporal representation of video data, which captures predictive available information in the video and assists in transforming the input video. We present a unique approach to ground truth representation for capturing more informative features. Finally, we incorporate a two-stage learning component into our framework to optimize estimation accuracy. Through evaluations on benchmark datasets, we demonstrate the effectiveness of our contributions and their practical relevance in healthcare applications.} }
Endnote
%0 Conference Paper %T ST2S-rPPG: A Spatiotemporal Two-Stage Learning Approach for Pulse Estimation Using Video %A Eirini Kateri %A Katayoun Farrahi %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-kateri25a %I PMLR %P 550--562 %U https://proceedings.mlr.press/v259/kateri25a.html %V 259 %X Remote physiological monitoring presents an opportunity to enhance patient care, particularly in scenarios where traditional monitoring methods are impractical or unavailable. Heart rate, being a principal indicator of health, has been a focal point of video-based monitoring systems. Despite significant advancements in remote photoplethysmography technology, several challenges persist, including motion artifacts, data homogeneity and availability, which impact the accuracy and reliability of such solutions. In this work, we introduce a novel framework aimed at addressing these challenges, ST2S-rPPG. Our methodology involves a stabilization method to mitigate motion artifacts. We propose a spatiotemporal representation of video data, which captures predictive available information in the video and assists in transforming the input video. We present a unique approach to ground truth representation for capturing more informative features. Finally, we incorporate a two-stage learning component into our framework to optimize estimation accuracy. Through evaluations on benchmark datasets, we demonstrate the effectiveness of our contributions and their practical relevance in healthcare applications.
APA
Kateri, E. & Farrahi, K.. (2025). ST2S-rPPG: A Spatiotemporal Two-Stage Learning Approach for Pulse Estimation Using Video. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:550-562 Available from https://proceedings.mlr.press/v259/kateri25a.html.

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