自适应调参让摄像头无接触测心跳更准，无需训练也能实时运行。
Adaptive Parameter Optimization for Robust Remote Photoplethysmography
- 基于信号质量在线调整参数，自动优化光照和相机差异影响。
- 在PURE和UBFC-rPPG数据集上误差仅0.77和0.66 bpm，准确率超97%。
- 无需训练、实时运行，适合部署在不同光照和设备环境的场景。
远程光电容积脉搏波（rPPG）利用普通RGB摄像头实现无接触生命体征监测。但现有方法依赖固定参数，仅适用于特定光照与摄像机配置，难以适应多样部署环境。本文提出无需训练的投影鲁棒信号混合（PRISM）算法，通过信号质量评估在线联合优化光度去趋势与颜色混合参数。PRISM在无监督方法中达到领先性能，在PURE数据集上平均绝对误差（MAE）为0.77 bpm，UBFC-rPPG为0.66 bpm；在5 bpm阈值下准确率分别为97.3%和97.5%。统计分析表明，其表现与顶尖有监督方法相当（p > 0.2），且保持实时CPU运算速度。结果验证了自适应时序优化能显著提升rPPG在多样化条件下的鲁棒性。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG) enables contactless vital sign monitoring using standard RGB cameras. However, existing methods rely on fixed parameters optimized for particular lighting conditions and camera setups, limiting adaptability to diverse deployment environments. This paper introduces the Projection-based Robust Signal Mixing (PRISM) algorithm, a training-free method that jointly optimizes photometric detrending and color mixing through online parameter adaptation based on signal quality assessment. PRISM achieves state-of-the-art performance among unsupervised methods, with MAE of 0.77 bpm on PURE and 0.66 bpm on UBFC-rPPG, and accuracy of 97.3\% and 97.5\% respectively at a 5 bpm threshold. Statistical analysis confirms PRISM performs equivalently to leading supervised methods ($p > 0.2$), while maintaining real-time CPU performance without training. This validates that adaptive time series optimization significantly improves rPPG across diverse conditions.
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