U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation

TL;DR

U-FaceBP is an uncertainty-aware Bayesian ensemble deep learning approach that improves face video-based blood pressure estimation by modeling uncertainties and fusing multiple modalities, demonstrating superior performance and reliability.

Contribution

The paper introduces U-FaceBP, a novel Bayesian ensemble method that models uncertainties and fuses multiple modalities for more accurate and reliable BP estimation from face videos.

Findings

U-FaceBP outperforms existing BP estimation methods on large diverse datasets.

Uncertainty estimates help guide modality fusion and assess prediction reliability.

The method improves BP estimation accuracy across different racial groups.

Abstract

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.