通过融合可见光与红外视频，实现高原环境下长期日常护理中的无接触健康监测。
Non-Contact Health Monitoring During Daily Personal Care Routines
- 融合RGB与红外视频输入，提升信号鲁棒性。
- 心率估计平均绝对误差达4.99 BPM，性能显著提升。
- 适用于长期居家或高海拔场景的健康监护，适合医疗可穿戴研究者。
远程光体积描记法（rPPG）实现了非接触式、连续的生理信号监测，为日常健康传感提供了实用替代方案。尽管rPPG在日常监测中前景广阔，但在长时间个人护理场景（如高原环境下的镜前操作）中仍面临挑战，包括环境光照变化、手部动作导致的频繁遮挡以及动态面部姿态。为此，我们提出了LADH（Long-term Altitude Daily Health）数据集，包含21名参与者在五种常见个人护理场景下的240段同步的RGB与红外（IR）面部视频，并附带真实心电、呼吸和血氧信号。实验表明，结合RGB与IR视频可显著提高非接触生理监测的准确性和鲁棒性，心率估计的平均绝对误差（MAE）降至4.99 BPM。此外，多任务学习能同时提升多种生理指标的性能。数据集与代码已开源：https://github.com/McJackTang/FusionVitals。
原文摘要 · Abstract (English)
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.
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