Blood oxygen saturation (SpO2) is a crucial vital sign routinely monitored in medical settings. Traditional methods require dedicated contact sensors, limiting accessibility and comfort. This study presents a deep learning framework for contactless SpO2 measurement using an off-the-shelf camera, addressing challenges related to lighting variations and skin tone diversity. We conducted two large-scale studies with diverse participants and evaluated our method against traditional signal processing approaches in intra- and inter-dataset scenarios. Our approach demonstrated consistent accuracy across demographic groups, highlighting the feasibility of camera-based SpO2 monitoring as a scalable and non-invasive tool for remote health assessment.
@article{arxiv.2503.01699,
title = {Camera Measurement of Blood Oxygen Saturation},
author = {Jiankai Tang and Xin Liu and Daniel McDuff and Zhang Jiang and Hongming Hu and Luxi Zhou and Nodoka Nagao and Haruta Suzuki and Yuki Nagahama and Wei Li and Linhong Ji and Yuanchun Shi and Izumi Nishidate and Yuntao Wang},
journal= {arXiv preprint arXiv:2503.01699},
year = {2025}
}