SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals

arXiv:2404.13449v1 Announce Type: cross
Abstract: Subtle periodic signals, such as blood volume pulse and respiration, can be extracted from RGB video, enabling noncontact health monitoring at low cost. Advancements in remote pulse estimation -- or remote photoplethysmography (rPPG) -- are currently driven by deep learning solutions. However, modern approaches are trained and evaluated on benchmark datasets with ground truth from contact-PPG sensors. We present the first non-contrastive unsupervised learning framework for signal regression to mitigate the need for labelled video …

abstract arxiv cost cs.ai cs.cv cs.lg deep learning enabling health however low modern monitoring solutions type unsupervised unsupervised learning video