用不确定性感知的深度集成模型，提升人脸视频测血压的准确性和可靠性。
U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation
- 基于贝叶斯神经网络建模测量中的随机与认知不确定性。
- 在1197名跨种族受试者上超越当前最佳方法，误差更小。
- 可指导多模态融合、评估预测可信度，适合健康监测应用。
血压测量对日常健康评估至关重要。远程光电容积脉搏波描记法（rPPG）通过摄像头捕捉人脸视频提取脉搏波，有望实现无需医疗设备的便捷血压测量。然而，基于rPPG的血压估计存在多种不确定性，导致性能和可靠性受限。本文提出U-FaceBP，一种面向人脸视频血压估计的不确定性感知贝叶斯集成深度学习方法。U-FaceBP利用贝叶斯神经网络（BNN）建模人脸视频血压估计中的随机不确定性和认知不确定性。同时，设计为集成方法，分别使用多个BNN从rPPG信号、由人脸视频推导出的PPG信号及人脸图像中估计血压。在包含1197名来自不同人种群体的受试者的两个大规模数据集上进行实验，结果表明U-FaceBP优于当前最先进的血压估计方法。此外，我们证明了U-FaceBP提供的不确定性估计具有信息量，可用于指导模态融合、评估预测可靠性，并分析不同人种间的性能差异。
原文摘要 · Abstract (English)
Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face videos captured by a camera, has the potential to enable convenient BP measurement without specialized medical devices. However, there are various uncertainties in BP estimation using rPPG, leading to limited estimation performance and reliability. In this paper, we propose U-FaceBP, an uncertainty-aware Bayesian ensemble deep learning method for face video-based BP estimation. U-FaceBP models aleatoric and epistemic uncertainties in face video-based BP estimation with a Bayesian neural network (BNN). Additionally, we design U-FaceBP as an ensemble method, estimating BP from rPPG signals, PPG signals derived from face videos, and face images using multiple BNNs. Large-scale experiments on two datasets involving 1197 subjects from diverse racial groups demonstrate that U-FaceBP outperforms state-of-the-art BP estimation methods. Furthermore, we show that the uncertainty estimates provided by U-FaceBP are informative and useful for guiding modality fusion, assessing prediction reliability, and analyzing performance across racial groups.
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