Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography
2026-08-24 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed Phy-BP, a method to estimate blood pressure non-invasively using triaxial bodyseismography (BSG), which is an advanced form of ballistocardiography (BCG). They created a quality-control step to focus on reliable heart-related signal parts and built a physical model that explains how waves travel in the body-bed system. This model helps a deep learning approach better understand and align signals from three axes, making the blood pressure estimates more accurate and stable even when data is noisy or limited. Testing on hospital data showed their method can filter out poor signals and maintain consistency, leading to reliable blood pressure monitoring in realistic conditions.
Ballistocardiography (BCG)Bodyseismography (BSG)Blood Pressure (BP) EstimationTriaxial SignalsCardiogenic ComponentsDeep LearningPhysical ModelingSignal Quality ControlWave PropagationModel Robustness
Authors
Yuanyuan Zhang, Yida Zhang, Jiahui Li, Yuyan Wu, Fei Dou, Xiao Yin, Zhenlin An, Hae Young Noh, Wenzhan Song
Abstract
Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variations in body-bed interaction with shifted fiducial points in temporal or amplitude axis, and BP varies with personal hemodynamic changes, causing misaligned representations that affect model generalizability and robustness. In this work, we propose a non-invasive BP estimation framework, Phy-BP, based on triaxial bodyseismography (BSG) as an extension of BCG. Firstly, an adaptive quality-control algorithm is designed to select BSG segments enriched with cardiogenic components by jointly considering neighboring beat patterns and universal cardiogenic templates. Furthermore, a physical model is established to describe 3D wave propagation in the body-bed system and is subsequently embedded into the deep learning model to characterize the intrinsic coupling among triaxial BSG signals driven by a single cardiogenic excitation. Thus, multi-axis features are aligned during model training, improving robustness against distortions in real scenarios. Experiments on a 162-hour hospital dataset collected from 21 subjects reveal that the proposed Phy-BP can dynamically filter out low-quality measurements, and the deep learning model training is constrained by physical consistency across different axes to provide faithful BP monitoring, especially when training samples are limited.