ROSE-Net: Leveraging remote photoplethysmography to estimate oxygen saturation using deep learning

Abstract

A method for accurately estimating physiological signals from video streams at a minimal cost holds immense value, particularly in pre-clinical health monitoring applications. This technique is particularly indispensable in scenarios where traditional sensors, such as finger photoplethysmography (PPG), are not viable, such as in cases involving burn victims, premature infants, or individuals with sensitive skin. Remote photoplethysmography (rPPG) is a process of estimating PPG signals using video streams instead of traditional sensors. rPPG has thus been seen as a promising alternative to traditional PPG. As an alternative to using PPG for estimating oxygen saturation (SpO2), we propose ROSE-Net. ROSE-Net, trained on clinical PPG, was tested on an external rPPG dataset, PURE. The model achieved a mean absolute error (MAE) of 1.20 and a root mean square error (RMSE) of 1.86 on clinical PPG. When tested on rPPG, it exhibited an MAE of 1.95 and an RMSE of 2.46 in PURE, an MAE of 0.77, and an RMSE of 0.96 in ARPOS. These results demonstrate the model’s ability to estimate SpO2 levels within acceptable margins when applied to rPPG data. Consequently, rPPG presents a viable approach for estimating SpO2 levels, paving the way for non-contact health tracking applications.