mmWave-Based Contactless BP Monitoring with Physio-Model-Guided Deep Learning

Blood pressure (BP) is a critical indicator for life-threatening conditions. While invasive catheter-based methods offer high accuracy, non-invasive techniques typically require placement on specific body areas, introducing discomfort and rendering their accuracy sensitive to wearing conditions. To overcome these limitations, recent efforts have explored contactless BP monitoring using RF sensing. However, existing approaches often rely on deep learning models without grounding in physiological principles, resulting in poor generalization and limited clinical trustworthiness. In this paper, we propose hBP-Fi, a contactless BP measurement system driven by hemodynamics acquired via RF sensing. In addition to its contactless convenience, hBP-Fi outperforms existing RF-based approaches by i) employing a physiologically grounded hemodynamic model of pulse generation that forms the basis for RF-based BP estimation, ii) enabling super-resolution arterial pulse tracking via beam-steerable RF scanning, iii) ensuring output trustworthiness through an interpretable (transparent-by-design) deep learning model, and iv) achieving robust generalizability to unseen users and scenarios via a CycleGAN-based training strategy. Extensive experiments with 35 subjects under practical scenarios demonstrate that hBP-Fi can achieve errors of -2.95\pm7.66 mmHg and 2.63\pm6.05 mmHg for systolic and diastolic blood pressures, respectively.