IEEE J Biomed Health Inform. 2021 May;25(5):1397-1408. doi: 10.1109/JBHI.2020.3026481. Epub 2021 May 11.
Remote photoplethysmography (rPPG) is an unobtrusive solution to heart rate monitoring in drivers. However, disturbances that occur during driving such as driver behavior, motion artifacts, and illuminance variation complicate the monitoring of heart rate. Faced with disturbance, one commonly used assumption is heart rate periodicity (or spectrum sparsity). Several methods improve stability at the expense of tracking sensitivity for heart rate variation. Based on statistical signal processing (SSP) and Monte Carlo simulations, the outlier probability is derived and ADaptive spectral filter banks (AD) is proposed as a new algorithm which provides an explicable tuning option for spectral filter banks to strike a balance between robustness and sensitivity in remote monitoring for driving scenarios. Moreover, we construct a driving database containing over 23 hours of data to verify the proposed algorithm. The influence on rPPG from driver habits (both amateurs and professionals), vehicle types (compact cars and buses), and routes are also evaluated. In comparison to state-of-the-art rPPG for driving scenarios, the mean absolute error in the Passengers, Compact Cars, and Buses scenarios is 3.43, 7.85, and 5.02 beats per minute, respectively. Moreover, AD also won the top third place in the first challenge on remote physiological signal sensing (RePSS) with relative low computational complexity.
远程光电容积脉搏波描记术（rPPG）是一种用于监测驾驶员心率的非侵入性解决方案。然而，在驾驶过程中会出现驾驶员行为、运动伪影和光照变化等干扰，这使得心率监测变得复杂。面对干扰，人们通常使用心率周期性（或频谱稀疏性）的假设。有几种方法可以提高稳定性，但会牺牲对心率变化的跟踪灵敏度。基于统计信号处理（SSP）和蒙特卡罗模拟，得出了异常值概率，并提出了自适应频谱滤波器组（AD）作为一种新算法，该算法为频谱滤波器组提供了可解释的调整选项，以在驾驶场景的远程监测中在稳健性和灵敏度之间取得平衡。此外，我们构建了一个包含超过 23 小时数据的驾驶数据库，以验证所提出的算法。还评估了驾驶员习惯（业余和专业驾驶员）、车辆类型（紧凑型汽车和公共汽车）和路线对 rPPG 的影响。与驾驶场景的最新 rPPG 相比，在乘客、紧凑型汽车和公共汽车场景中的平均绝对误差分别为 3.43、7.85 和 5.02 次/分钟。此外，AD 在第一个远程生理信号感应挑战赛（RePSS）中也以相对较低的计算复杂度获得了第三名。
