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

Remote vital sign monitoring has risen in prominence over recent years, with an acceleration in clinical development and deployment due to the COVID-19 pandemic. Previous work has demonstrated the feasibility of estimating subject heart rate from facial videos. However, all previous methods exhibit biased performance towards darker skin tone subjects. In this paper, we present a first attempt to mitigate biases in photoplethysmography performance on darker skin tones by relying on the statistics of imaging physics. In addition to mitigating skin tone bias, we demonstrate that the proposed method mitigates errors due to lighting changes, shadows, and specular highlights. The proposed method not only improves performance for darker skin tones but is the overall top performer on the entire dataset. We report a performance gain of 0.69 beats per minute over the benchmark for dark skin tones and an overall improvement of 0.47 bpm across all skin tones. Assessment of the proposed method is accomplished through the creation of the first telemedicine-focused smartphone camera-based remote vital signs dataset, named the VITAL dataset. A total of 344 videos (~688 minutes) consisting of 43 subjects with diverse skin tones recorded under three lighting conditions, two activity conditions, and two camera angles, is compiled with corresponding vital sign data including non-invasive continuous blood pressure, heart rate, respiratory rate, and oxygen saturation.