Non-Contact Health Monitoring During Daily Personal Care Routines
This work addresses the problem of reliable health monitoring for individuals in daily personal care settings, particularly in challenging environments, but it is incremental as it builds on existing rPPG methods with a new dataset and fusion approach.
The paper tackled the challenge of applying remote photoplethysmography (rPPG) for non-contact health monitoring in long-term personal care scenarios, such as mirror-facing routines in high-altitude environments, by introducing the LADH dataset and demonstrating that combining RGB and IR video inputs improves accuracy, achieving a mean absolute error of 4.99 BPM in heart rate estimation.
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. To address these challenges, we present LADH (Long-term Altitude Daily Health), 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. Dataset and code are open at https://github.com/McJackTang/FusionVitals.