Abstract：
Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.
摘要：
远程光电容积描记(rPPG)技术是一种非接触式生理信号测量方法,特点是非侵入性和易用性。在医疗卫生领域具有广泛的应用潜力,人为因素工程，和其他领域。然而,当前的rPPG技术极易受到照明条件变化的影响，头部姿势改变,和部分闭塞，对其广泛应用提出了重大挑战。为了提高远程心率估计的准确性,增强模型的泛化,我们提议PulseFormer,基于变压器的双路径网络。通过集成本地和全球信息，并利用快速和慢速路径，PulseFormer有效地捕获关键区域的时间变化和全球区域的空间变化，便于提取rPPG特征信息，同时减轻背景噪声变化的影响。流行的rPPG数据集上的心率估计结果表明，PulseFormer在公共数据集上实现了最先进的性能。此外,我们建立了一个包含驾驶场景中的面部表情和同步生理信号的数据集,并在这个收集的数据集上测试来自公共数据集的预训练模型.结果表明，PulseFormer在跨场景设置中跨不同数据分布表现出强大的泛化能力。因此,该模型适用于各种场景下个体的心率估计。
