UCLA

Heart rate (HR) is an essential clinical measure for the assessment of cardiorespiratory instability. The growing telemedicine market opens up the urgent requirement for scalable yet affordable remote HR estimation. Smartphones that use in-built camera modules to measure HR from facial videos offer a more economical solution in comparison to mass deployment of wearable sensors. However, existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. This is a major concern, since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease. We identify the origin of this bias and 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. 472 videos (~944 minutes) of 59 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 non-contact HR sensing technologies a viable reality for patients across skin tones, using just smartphone cameras.