Motion-Based Confidence Score to Support the Practical Application of rPPG Methods in Health Monitoring

Remote photoplethysmography (rPPG) has gained popularity as a non-invasive technique for remote monitoring, as it can provide accurate measurements of an individual's physiological signals under controlled conditions. However, the accuracy of rPPG can be affected by various factors, such as movement artifacts, changes in skin tone, and the presence of other sources of light in the environment. To improve the reliability of rPPG measurements in real-world monitoring settings and reduce the frequency of false alarms in health monitoring settings, we propose a confidence score indicating the quality of the predictions. This score was built by identifying meaningful variables related to motion that strongly correlate with the accuracy of the measurements and training a classifier with data coming from 3 distinct datasets, to improve the model's robustness, reproducibility, and generalizability. Despite that only motion-related features have been considered, the high AUC values obtained in all cases were always above 0.93, demonstrating the model's ability to detect inaccurate heart rate measurements.