U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Measurement
This work addresses the challenge of accurate daily health monitoring through non-invasive blood pressure measurement, representing an incremental improvement with a novel ensemble approach.
The paper tackles the problem of limited performance in blood pressure estimation from face videos using remote photoplethysmography by proposing U-FaceBP, an uncertainty-aware Bayesian ensemble deep learning method, and demonstrates that it outperforms state-of-the-art methods in a large-scale experiment with 786 subjects.
Blood pressure (BP) measurement plays an essential role in assessing health on a daily basis. Remote photoplethysmography (rPPG), which extracts pulse waves from camera-captured face videos, has the potential to easily measure BP for daily health monitoring. However, there are many uncertainties in BP estimation using rPPG, resulting in limited estimation performance. In this paper, we propose U-FaceBP, an uncertainty-aware Bayesian ensemble deep learning method for face video-based BP measurement. U-FaceBP models three types of uncertainty, i.e., data, model, and ensemble uncertainties, in face video-based BP estimation with a Bayesian neural network (BNN). We also design U-FaceBP as an ensemble method, with which BP is estimated from rPPG signals, PPG signals estimated from face videos, and face images using multiple BNNs. A large-scale experiment with 786 subjects demonstrates that U-FaceBP outperforms state-of-the-art BP estimation methods. We also show that the uncertainties estimated from U-FaceBP are reasonable and useful for prediction confidence.