Non-Contact Health Monitoring During Daily Personal Care Routines

Abstract--Remote photoplethysmography (rPPG) enables non-contact, continuous monitoring of physiological signals and offers a practical alternative to traditional health sensing methods. Although rPPG is promising for daily health monitoring, its application in long-term personal care scenarios--such as mirror-facing routines in high-altitude environments--remains challenging due to ambient lighting variations, frequent occlusions from hand movements, and dynamic facial postures. T o address these challenges, we present the Long-term Altitude Daily Health (LADH) dataset, the first long-term rPPG dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants across five common personal care scenarios, along with ground-truth PPG, respiration, and blood oxygen signals. Our experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring, achieving a mean absolute error (MAE) of 4.99 BPM in heart rate estimation. Furthermore, we find that multi-task learning enhances performance across multiple physiological indicators simultaneously.