Non-Contact Health Monitoring During Daily Personal Care Routines

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 atthis https URL.

@article{ma2025_2506.09718,
  title={ Non-Contact Health Monitoring During Daily Personal Care Routines },
  author={ Xulin Ma and Jiankai Tang and Zhang Jiang and Songqin Cheng and Yuanchun Shi and Dong LI and Xin Liu and Daniel McDuff and Xiaojing Liu and Yuntao Wang },
  journal={arXiv preprint arXiv:2506.09718},
  year={ 2025 }
}