Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.
Sensors (Basel). 2021 Sep 20;21(18):6296. doi: 10.3390/s21186296.
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.
心率（HR）是用于指示人体生理健康的基本生命体征之一。传统的 HR 监测器通常需要与皮肤接触，而远程光体积描记术（rPPG）则通过视频摄像机捕捉皮肤的细微光变化来实现非接触式 HR 监测。鉴于这项技术在数字医疗保健未来的巨大潜力，生理信号的远程监测在研究界引起了广泛关注。近年来，深度学习（DL）方法在图像和视频分析方面的成功激发了研究人员将这些技术应用于远程生理信号提取管道的各个部分。在本文中，我们讨论了基于 DL 的方法在远程 HR 测量方面的一些最新进展，根据模型架构和应用对其进行分类。我们进一步详细介绍了远程生理监测的相关实际应用，并总结了用于加速相关研究进展的各种常见资源。最后，我们分析了研究结果的意义，并讨论了研究差距，以指导未来的探索。
