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.

翻译：远程光电容积描记术（rPPG）能够实现非接触式、连续的生理信号监测，为传统健康传感方法提供了实用的替代方案。尽管rPPG在日常健康监测中前景广阔，但其在长期个人护理场景（如高海拔环境下面向镜子的日常活动）中的应用仍面临挑战，主要源于环境光照变化、手部运动导致的频繁遮挡以及动态面部姿态。为应对这些挑战，我们提出了LADH（长期高海拔日常健康）数据集，这是首个长期rPPG数据集，包含来自21名参与者在五种常见个人护理场景下的240段同步RGB与红外（IR）面部视频，并提供了真实的心率变异性（PPG）、呼吸及血氧信号。实验表明，结合RGB与IR视频输入能提升非接触式生理监测的准确性与鲁棒性，在心率估计中实现了4.99 BPM的平均绝对误差（MAE）。此外，我们发现多任务学习能同时提升多项生理指标的监测性能。数据集与代码已开源：https://github.com/McJackTang/FusionVitals。