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网络出版日期： 2024-10-11 ，
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周才英,占新龙,魏远旺等.人脸视频中心率变异性参数估计方法综述[J].中国图象图形学报,
Zhou Caiying,Zhan Xinlong,Wei Yuanwang,et al.A Review of Heart Rate Variability Parameter Estimation Methods in Facial Video[J].Journal of Image and Graphics,
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HRV参数估计的多种方法，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， potentially indicating autonomic dysfunction or underlying heart conditions. For example， individuals with lower HRV may be at a higher risk of sudden cardiac death or myocardial infarction. Continuous monitoring of HRV can therefore serve as a predictive marker for these events， enabling earlier interventions that could save lives. During rehabilitation， HRV monitoring assists in tracking recovery progress and adjusting treatment plans. Changes in HRV can guide modifications in exercise regimens， physiotherapy， or medication dosages， offering a more personalized approach to patient care and optimizing recovery outcomes. HRV also plays a crucial role in mental health， emotional management， and stress monitoring. Analyzing HRV allows healthcare providers to better understand a patient's stress levels， emotional state， and overall cardiovascular health， enabling more tailored and effective treatment strategies. Facial video acquisition and data preprocessing are critical steps in HRV estimation. Obtaining high-quality RGB image data requires video capture devices with appropriate resolution and frame rate. Stable and consistent video capture conditions are essential to ensure accurate HRV estimation. Technical requirements for video frame extraction include precise synchronization and alignment of frames to maintain consistency across analyses. Data cleaning and normalization processes involve removing artifacts， correcting for illumination variations， and standardizing the data for analysis. Effective preprocessing ensures that the facial video accurately reflects the physiological signals needed for HRV estimation. Various methods are used for HRV parameter estimation. Traditional signal processing techniques， such as blind source separation and skin model-based methods， have been employed for years. Blind source separation aims to isolate the desired physiological signal from noise and interference， while skin model-based methods leverage physiological models to estimate heart rate from subtle changes in facial color due to blood flow variations. Frequency-domain analysis decomposes the HRV signal into its frequency components to assess autonomic function， while time-frequency analysis provides a comprehensive view of HRV dynamics by combining time and frequency information. Emerging deep learning algorithms have shown great promise in HRV estimation from facial videos. Supervised convolutional neural networks can learn complex features from labeled data， enhancing the ability to extract relevant information from facial videos. Recurrent neural networks are effective for modeling temporal dependencies in sequential data， which is particularly useful for HRV estimation where time-series analysis is critical. Transformer models， known for their capacity to handle long-range dependencies and capture intricate patterns， offer further advantages in this domain. Although less commonly used for HRV estimation， unsupervised generative adversarial networks provide potential for generating synthetic data to augment training datasets， improving model robustness and reducing the reliance on large-scale labeled datasets. The performance of traditional and deep learning methods varies across different application scenarios. Traditional methods often perform well in controlled environments but may struggle with complex scenes or dynamic changes， such as varying lighting conditions or head movements. On the other hand， deep learning methods， while more adept at handling complex and noisy data， require large amounts of labeled training data and significant computational resources. This trade-off highlights the strengths and limitations of each approach and underscores the importance of selecting appropriate methods based on specific application needs. Facial video-based HRV estimation has several practical applications. In health assessment， continuous HRV monitoring can provide real-time insights into a patient's health status， enabling timely interventions and personalized treatment adjustments. Emotional recognition involves analyzing facial expressions and HRV to understand emotional states， which can be particularly useful in mental health diagnostics and therapy. Mental stress evaluation uses HRV data to identify individuals at risk of stress-related conditions， which is crucial for preventing burnout and promoting workplace well-being. Fatigue detection is vital for ensuring safety in various professional settings， such as aviation， transportation， and healthcare， where fatigue-related errors could have serious consequences. Early warning of cardiovascular diseases can be achieved through HRV monitoring， providing early alerts for potential health issues and enabling preventative measures. Despite the progress made in facial video-based HRV estimation， there are still challenges to overcome. Subject head movements and different lighting conditions can affect estimation accuracy， making it essential to develop robust algorithms that can handle these variations. Model selection and training strategies need to be optimized to improve performance in diverse real-world scenarios. Enhancing the real-time performance and robustness of these algorithms is crucial for their practical application， particularly in wearable and mobile health monitoring devices. Reducing dependency on large-scale labeled datasets through semi-supervised or unsupervised learning approaches could make these technologies more accessible and scalable， expanding their use in both clinical and consumer health settings. In conclusion， facial video-based HRV estimation technology holds great promise for health monitoring and disease diagnosis. By addressing current challenges and exploring future research directions， this technology can be further refined and integrated into everyday health practices. The ability to estimate HRV non-invasively from facial video has the potential to revolutionize the field of telemedicine and personalized health， offering a convenient， cost-effective， and accessible tool for continuous health monitoring. As research progresses， this innovative approach may become a standard component of modern healthcare， providing valuable insights into individual health status and enhancing overall quality of life.
心率变异性（HRV）人脸视频生理监测信号处理深度学习
Heart rate variability （HRV）face videophysiological monitoringsignal processingdeep learning
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