  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.
