ACuRE: Accurate Continuity-Regularized SpO2 Estimation Using Liquid Time-Constant Networks

Blood oxygen saturation (SpO$_2$) is a vital measure of respiratory and circulatory health, essential for detecting hypoxemia in conditions like chronic obstructive pulmonary disease and heart failure. Current non-contact SpO$_2$ estimation methods using remote photoplethysmography (rPPG) struggle with motion artifacts, illumination variability, and limited temporal modeling, hindering their practical use. We propose ACuRE, a novel framework that integrates a two-branch 3D-ResNet-18 for AC/DC signal separation, Liquid Time-Constant (LTC) networks for continuous-time dynamics, and a physics-informed partial differential equation (PDE) loss based on mass conservation. ACuRE overcomes these challenges by isolating pulsatile (AC) and baseline (DC) signals for enhanced robustness, using LTC networks to capture nonlinear physiological dynamics, and applying PDE regularization to ensure signal continuity. This achieves a significant reduction in mean absolute error compared to baselines, with strong performance under motion and illumination stress. Evaluated across multiple datasets, ACuRE demonstrates robust accuracy and generalization, offering a scalable solution for video-based health monitoring in telemedicine and low-resource settings.