Poster: Contactless Cardiovascular Hemodynamics Inference and Hypertension Detection via Ballistocardiogram

Hypertension affects 1.28 billion adults, but blood pressure alone is insufficient for accurate hypertension detection. In this paper, we identified a range of multi-dimensional hemodynamics as novel biomarkers that can offer more comprehensive cardiovascular insights for detecting hypertension. We design a novel system that collects ballistocardiogram (BCG) signals from an optic fiber sensor mat. It features a multi-task, multi-branch, unsupervised domain adaptation learning framework, enabling simultaneous prediction of five hemodynamic biomarkers for hypertension detection. In a 4-month trial with 85 subjects, Hyde achieved 97.65% average accuracy for hypertension detection.