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Heart rate (HR) is an essential clinical measure for the assessment ofcardiorespiratory instability. Since communities of color aredisproportionately affected by both COVID-19 and cardiovascular disease, thereis a pressing need to deploy contactless HR sensing solutions for high-qualitytelemedicine evaluations. Existing computer vision methods that estimate HRfrom facial videos exhibit biased performance against dark skin tones. Wepresent a novel physics-driven algorithm that boosts performance on darker skintones in our reported data. We assess the performance of our method through thecreation of the first telemedicine-focused remote vital signs dataset, theVITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tonesare recorded under realistic scene conditions with corresponding vital signdata. Our method reduces errors due to lighting changes, shadows, and specularhighlights and imparts unbiased performance gains across skin tones, settingthe stage for making medically inclusive non-contact HR sensing technologies aviable reality for patients of all skin tones.

Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and Scenes