Demographic bias in public remote photoplethysmography datasets

Abstract Remote photoplethysmography (rPPG) is gaining traction for non-contact heart rate estimation, yet most publicly available datasets are demographically biased. In this study, we analyze 100 rPPG studies, providing the first quantitative cross-model audit of demographic bias in rPPG and demonstrating significant underrepresentation of darker skin tones and gender imbalance. Our findings reveal how this bias limits model fairness and accuracy and propose steps to improve dataset inclusivity and algorithmic robustness.