Heart rate (HR) is an essential clinical measure for the assessment of cardiorespiratory instability. Since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease, there is a pressing need to deploy contactless HR sensing solutions for high-quality telemedicine evaluations. Existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. We present a novel physics-driven algorithm that boosts performance on darker skin tones in our reported data. We assess the performance of our method through the creation of the first telemedicine-focused remote vital signs dataset, the VITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tones are recorded under realistic scene conditions with corresponding vital sign data. Our method reduces errors due to lighting changes, shadows, and specular highlights and imparts unbiased performance gains across skin tones, setting the stage for making medically inclusive non-contact HR sensing technologies a viable reality for patients of all skin tones.
@article{arxiv.2010.12769,
title = {Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and Scenes},
author = {Pradyumna Chari and Krish Kabra and Doruk Karinca and Soumyarup Lahiri and Diplav Srivastava and Kimaya Kulkarni and Tianyuan Chen and Maxime Cannesson and Laleh Jalilian and Achuta Kadambi},
journal= {arXiv preprint arXiv:2010.12769},
year = {2020}
}
Comments
49 pages, 6 figures, 3 tables, Supplement with 7 figures