2025/03/03 by Tang, Jiankai, Liu, Xin, McDuff, Daniel +11 · 2 citations
#Computational Engineering #FOS: Computer and information sciences #Finance #and Science (cs.CE)
paper · doi:10.48550/arxiv.2503.01699
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.