Non-Contact Health Monitoring During Daily Personal Care Routines

Xulin Ma, Jack Tang, Zhang Jiang, Songqin Cheng, Yuanchun Shi, Dong LI, Xin Liu, Daniel McDuff, Xiaojing Liu, Yuntao Wang

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Keywords: non-contact, rPPG, health-monitoring

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. To 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. Dataset and code are open at https://github.com/McJackTang/FusionVitals.

Track: 7. Contact-less solutions for human sensing

NominateReviewer: Jiankai Tang, [e-mail]

Submission Number: 22