人脸视频中心率变异性参数估计方法综述  

Review of heart rate variability parameter estimation methods in facial video

作　　者：周才英[1,2] 占新龙 魏远旺 张先超 李永刚 王超超 叶晓朗 Zhou Caiying;Zhan Xinlong;Wei Yuanwang;Zhang Xianchao;Li Yonggang;Wang Chaochao;Ye Xiaolang(College of Science,Jiangxi University of Science and Technology,Ganzhou 341000,China;Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province,Jiaxing University,Jiaxing 314001,China;Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province,Jiaxing University,Jiaxing 314001,China;Institute of Information Network&Artificialintelugence,Jiaxing University,Jiaxing 314001,China;Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems,Jiaxing University,Jiaxing 314001,China)

机构地区：[1]江西理工大学理学院,赣州341000 [2]嘉兴大学浙江省医学电子与数字健康重点实验室,嘉兴314001 [3]嘉兴大学生命健康智能感知浙江省工程研究中心,嘉兴314001 [4]嘉兴大学信息网络与智能研究院,嘉兴314001 [5]嘉兴大学全省多模态感知与智能系统重点实验室,嘉兴314001

出　　处：《中国图象图形学报》2025年第4期953-976,共24页Journal of Image and Graphics

基　　金：浙江省自然科学基金项目(LTGG24F020001,LQ23F010006);嘉兴市公益性研究计划项目(2023AY11030)。

摘　　要：本综述探讨了基于人脸视频的心率变异性(heart rate variability,HRV)估计技术,突出了其在健康监测和疾病诊断中的无创性和实时监控的优势。首先,解析了HRV的生理学基础和核心参数的定义,阐明了其在医疗保健领域的应用潜力。接着,详细介绍了人脸视频采集的技术细节、数据预处理流程,重点讨论了多种HRV参数估计方法,包括传统信号处理技术和深度学习算法。分析表明,深度学习技术在HRV估计方面因其强大的模式识别能力,能够有效提取复杂视觉特征和处理非线性生理信号,在提高估计精度方面展现出显著优势。本综述还对比了传统方法和深度学习方法在不同应用场景中的表现,指出了各自的优势与局限性,并总结了基于人脸视频HRV估计技术的实际应用案例,如健康评估、情绪识别、精神压力评估、疲劳检测和心血管疾病早期预警等。因此,本综述提出了未来研究的方向,包括降低头部运动和环境光变化的干扰、优化模型选择及减少对训练数据的依赖等,以促进HRV估计技术的发展。本综述旨在提供基于人脸视频的HRV估计技术的全面视角,为学术界和工业界的技术创新和应用拓展提供重要参考。Heart rate variability(HRV)analysis has emerged as a powerful tool in health monitoring and disease diagnosis,offering valuable insights into the autonomic nervous system’s regulation of the cardiovascular system.Estimating HRV from facial video is an innovative approach that combines convenience and non-invasiveness,which holds great promise for advancing personalized healthcare.This method utilizes facial video to capture subtle changes in skin color caused by blood flow variations,allowing for remote and continuous monitoring of heart rate dynamics.HRV reflects the variations in the time intervals,known as RR intervals,between successive heartbeats.It serves as a non-invasive marker of cardiac autonomic function and provides a dynamic assessment of the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.The significance of HRV lies in its ability to reveal underlying physiological conditions that may not be immediately apparent through standard vital sign measurements.For instance,a reduced HRV can indicate stress,fatigue,or the early onset of cardiovascular disease,making it a valuable metric for both preventive and therapeutic health strategies.The key parameters in HRV analysis include both time-domain and frequency-domain metrics.Time-domain measures,such as the standard deviation of NN intervals and the root mean square of successive differences,provide insights into overall heart rate dynamics and short-term variability.Frequency-domain measures,such as low-frequency and high-frequency components and their ratio,help evaluate the balance between sympathetic and parasympathetic activity.These parameters are vital for assessing individual health,particularly in relation to cardiovascular conditions,stress levels,and autonomic nervous system disorders.In healthcare,HRV has a wide range of applications across various domains.In disease prevention,HRV analysis can detect early signs of cardiovascular issues by identifying deviations from normal HRV patterns,potential

关 键 词：心率变异性(HRV) 人脸视频 生理监测 信号处理 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]