Addressing healthcare needs of the elderly in contactless blood oxygen saturation estimation with rPPG

Yuqing Cao; Yuxing Li; Kai Hang Yiu; Edmund Y. Lam

doi:10.1117/12.3000619

13 March 2024 Addressing healthcare needs of the elderly in contactless blood oxygen saturation estimation with rPPG

Yuqing Cao, Yuxing Li, Kai Hang Yiu, Edmund Y. Lam

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570C (2024) https://doi.org/10.1117/12.3000619
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Remote photoplethysmography (rPPG) is an effective technique for non-contact estimation of vital signs using video-based detection of exposed skin. It enables contactless healthcare services for clinical measurements and medical diagnosis. While existing rPPG methods primarily focus on enhancing robustness against interference factors like motion artifacts and illumination changes, limited attention has been given to the influence of the age factor on rPPG model performance. This study explicitly analyzed the impact of the age factor on the application of an rPPG method to the blood oxygen saturation (SpO₂) estimation. Two key observations were made. First, it was more challenging to estimate SpO₂ with rPPG for the elderly than for young individuals. Second, the performance of SpO₂ estimation for the elderly could be improved by dividing the training data into different age groups and exclusively training the model on data collected from the elderly. These observations highlighted that the age factor had a significant impact on rPPG methods, emphasizing the need for explicit consideration of the age factor in rPPG method design.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuqing Cao, Yuxing Li, Kai Hang Yiu, and Edmund Y. Lam "Addressing healthcare needs of the elderly in contactless blood oxygen saturation estimation with rPPG", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570C (13 March 2024); https://doi.org/10.1117/12.3000619

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