Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland.
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland; Division of Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan.
Comput Biol Med. 2024 Sep;179:108873. doi: 10.1016/j.compbiomed.2024.108873. Epub 2024 Jul 24.
Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of vital signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware and network constraints. In this article, a systematic and comprehensive investigation of these issues is conducted, measuring their detrimental effects on the quality of rPPG measurements. Additionally, practical strategies are proposed for mitigating these challenges to improve the dependability and resilience of video-based rPPG systems. Methods for effective biosignal recovery in the presence of network limitations are detailed, along with denoising and inpainting techniques aimed at preserving video frame integrity. Compared to previous studies, this paper addresses a broader range of variables and demonstrates improved accuracy across various rPPG methods, emphasizing generalizability for practical applications in diverse scenarios with varying data quality. Extensive evaluations and direct comparisons demonstrate the effectiveness of these approaches in enhancing rPPG measurements under challenging environments, contributing to the development of more reliable and effective remote vital sign monitoring technologies.
基于视频的远程光电容积脉搏波描记术 (rPPG) 已成为一种很有前途的非接触式生命体征监测技术，尤其是在受控条件下。然而，在真实场景中准确测量生命体征面临着多个挑战，包括视频编解码器引起的伪影、低光噪声、降级、低动态范围、遮挡以及硬件和网络限制。本文对这些问题进行了系统和全面的研究，测量了它们对 rPPG 测量质量的不利影响。此外，还提出了实用的策略来减轻这些挑战，以提高基于视频的 rPPG 系统的可靠性和弹性。本文详细介绍了在存在网络限制的情况下有效恢复生物信号的方法，以及用于保护视频帧完整性的去噪和修复技术。与以前的研究相比，本文解决了更广泛的变量问题，并在各种 rPPG 方法中提高了准确性，强调了在具有不同数据质量的各种场景中进行实际应用的通用性。广泛的评估和直接比较证明了这些方法在增强挑战性环境下的 rPPG 测量方面的有效性，为开发更可靠、更有效的远程生命体征监测技术做出了贡献。
