用于非接触式健康参数传感的远程光容积脉搏图的最新研究

Shadman Sakib, Zahid Hasan, Nirmalya Roy
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摘要

远程光电容积脉搏波描记(rPPG)已经成为远程医疗的一项重要技术,通过现成的标准摄像机提供非侵入性和可访问的健康监测。rPPG有助于从视频数据中评估心率(HR)、呼吸频率(RR)、血氧饱和度(SpO2)等关键健康指标,为疾病早期诊断和常规健康评估提供优势。认识到rPPG的潜力,来自多个领域的研究人员通过建立强大的信号采集理论基础,开发用于rPPG提取的信号处理和数据驱动算法,大大推进了rPPG的发展。虽然大多数rPPG综述主要集中在HR信号提取方法上,但我们的研究概述了rPPG的潜在范围。我们系统地组织了rPPG信号采集和提取技术的研究,并对rPPG在各种健康参数估计方面的最新进展进行了综述。除了提供全面的HR估计综述外,我们还结合了从rPPG数据中提取衍生信号(如RR和SpO2),包括它们的应用和局限性。我们还强调了机器学习(ML),深度学习(DL)和计算机视觉(CV)技术与rPPG技术的适应,并积累了可用的关键rPPG资源,如数据集,代码和教程。最后,我们确定了挑战和研究差距,如运动伪影,不同的照明条件和肤色的差异。我们的目标是通过概述未来的研究方向来提升rPPG系统的进步。我们的综合综述旨在通过推进非接触式健康参数传感领域来支持稳健和安全应用的发展。本文分类如下:应用领域>;医疗保健技术;机器学习:数据和知识的基本概念以人为中心和用户交互
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A State‐Of‐The‐Art Survey of Remote Photoplethysmography for Contactless Health Parameters Sensing
Remote photoplethysmography (rPPG) has emerged as a vital technology for remote healthcare, offering non‐invasive and accessible health monitoring through off‐the‐shelf standard video cameras. rPPG facilitates the assessment of key health indicators like heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2) from video data, providing advantages in early disease diagnosis and routine health assessments. Recognizing its potential, researchers from multiple fields have substantially progressed rPPG by establishing a strong theoretical basis for signal acquisition and developing signal processing and data‐driven algorithms for rPPG extraction. While most rPPG reviews primarily focus on HR signal extraction methods, our research provides an overview of the potential scope of rPPG. We systematically organize research on rPPG signal acquisition and extraction techniques and provide a critical review of recent rPPG advancements in diverse health parameter estimation. Besides providing a thorough HR estimation review, we incorporate the extraction of derivative signals such as RR and SpO2 from rPPG data, including their applications and limitations. We also highlight the adaptation of Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) techniques with rPPG technologies, and accumulate available critical rPPG resources like datasets, codes, and tutorials. Finally, we identify challenges and research gaps, such as motion artifacts, varying lighting conditions, and differences in skin tone. We aim to uplift advancements in rPPG systems by outlining future research directions. Our comprehensive review aims to support the development of robust and safe applications by advancing the field of contactless health parameter sensing.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
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