用面部超像素分析远程测心率变异性，驾驶模拟中精度达临床标准
A Signal Extraction Approach for Remote Heart Rate Variability Assessment Using Proxy Measure in a Driving Simulator
- 选20个面部超像素区域，结合Lp范数与分数阶微分增强波峰
- 最佳配置下心率误差仅1.92 bpm，HRV指标与真实心电图高度一致
- 为驾驶场景中的无接触生理监测提供可复现的方法指南
本研究评估了四种远程光电容积脉搏波（rPPG）算法——空间子空间旋转（2SR）、基于色度的方法（CHROM）、皮肤正交平面（POS）和主成分分析（PCA），应用于选定的超像素面部区域（目标数量为10和20），以在驾驶模拟器中进行监测。引入两种新型峰值增强方法：基于Lp范数和分数阶微分（FOD），以实现稳健的心率变异性（HRV）估计。采用信噪比为基础的质量评估对20秒片段进行数据清洗，减轻动态记录条件下的运动伪影。在29名参与者基线和驾驶模拟条件下记录的数据中，脉搏率（PR）在各配置下均达到临床可接受精度（与同步心电图对照），使用2SR结合FOD和20个超像素区域时取得最低平均绝对误差（MAE）1.92 bpm（sd = 1.72）。最佳情况下，正常间期标准差（SDNN）MAE达0.061秒，相邻差值均方根（RMSSD）MAE为0.081秒，间搏间隔检测的F1得分为0.93。最优参数集中在Lp范数的p = 6–7，分数阶微分阶数1.0–1.4。所有rPPG推导参数在不同条件和配置下均再现了参考心电图的统计结构。使用FOD需谨慎，因其对rPPG波形缓慢变化敏感。总体而言，2SR推荐用于脉搏率估计，而CHROM配合Lp范数与20个超像素区域适用于HRV估计，为驾驶模拟器中的rPPG监测提供了清晰的方法指导。
原文摘要 · Abstract (English)
This study evaluates remote Photopletismography (rPPG) algorithms, Spatial Subspace Rotation (2SR), Chrominance-based method (CHROM), Plane-Orthogonal-to-Skin (POS), and Principal Component Analysis (PCA), applied to selected superpixel-based facial regions (with target counts of 10 and 20 regions) for monitoring in a driving simulator. Two novel peak enhancement approaches, based on the Lp norm and Fractional-Order Derivative (FOD), are introduced to enable robust Heart Rate Variability (HRV) estimation. A signal-to-noise ratio-based quality assessment of 20 s segments serves as a data cleaning mechanism to mitigate motion artifacts inherent to dynamic recording conditions. In a sample of 29 participants recorded during baseline and driving simulation conditions, Pulse Rate (PR) is calculated with clinically acceptable accuracy across configurations (validated against simultaneous Electrocardiography (ECG) recordings), achieving the lowest Mean Absolute Error (MAE) of 1.92 bpm (sd = 1.72) using 2SR with FOD and 20 superpixel regions. The best-case MAE reached 0.061 s for Standard Deviation of Normal-to-Normal intervals (SDNN) and 0.081 s for Root Mean Square of Successive Differences (RMSSD), with inter-beat interval detection yielding an F1 score of 0.93. Optimal parameters clustered around p = 6-7 for Lp norm and fractional orders of 1.0-1.4. All rPPG-derived parameters reproduced the statistical structure of the reference ECG across conditions and configurations. Caution is advised when using FOD due to slow changes in the rPPG waveform. Overall, 2SR is recommended for PR, while CHROM for HRV estimation, using Lp norm with 20 superpixels, providing clear methodological guidance for rPPG monitoring in driving simulators
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