PulseSight : A novel method for contactless oxygen saturation (SpO2) monitoring using smartphone cameras, remote photoplethysmography and machine learning

Abstract

Monitoring oxygen saturation (SpO${}_2$) level is crucial for evaluating the current cardiac and respiratory condition of a person, particularly in medical settings. Conventional pulse oximetry, while efficient, has drawbacks such as the requirement for physical touch and vulnerability to certain environmental influences. In this paper, we propose an innovative approach for estimating SpO${}_2$ levels utilizing smartphone cameras and video-based photoplethysmography (PPG) without physical touch. Our framework consists of an Android mobile application that records 20-second face videos, which a cloud-based backend server then analyzes. The server utilizes deep learning-based facial recognition and signal processing techniques to extract remote photoplethysmography (rPPG) signals from specific facial regions and predict oxygen saturation (SpO${}_2$) levels using a Support Vector Regression (SVR) Machine learning model. Signal noise and motion artifacts are mitigated by extracting relevant characteristics from the rPPG. The system was validated by experimental studies, which contained 40 sets of videos collected from 10 participants. The study was conducted under different illumination conditions, which showed low RMSE score (1.45 ±0.1) and MAE score (0.92 ±0.01). Also, our system shows high usability, as indicated by the System Usability Scale (SUS) score of 80.5. The results demonstrate that our method offers a dependable and contactless substitute for continuous SpO2 monitoring, with potential uses in telemedicine and remote patient monitoring.