Cardio-mmFlow: A Gaussian-Prior-Free Physics-Informed Flow Matching Framework for Electrocardiogram to mmWave Radar Synthesis.

Abstract

Continuous ECG monitoring is clinically valuable, but scaling it beyond electrodes to comfortable long-term use motivates contactless mmWave sensing. In practice, mmWave-to-ECG reconstruction is severely constrained by the scarcity of high-quality synchronized recordings and poor cross-subject generalization. To overcome these bottlenecks, we propose \textbf{Cardio-mmFlow}, a Gaussian-prior-free physics-informed flow matching framework that synthesizes realistic mmWave radar signals from abundant clinical ECG corpora. It learns a direct transport trajectory between the latent manifolds of ECG and radar. To capture subject-dependent propagation differences, we incorporate a simplified mass--spring--damper inspired physical prior and inject it into the flow dynamics via feature-wise linear modulation for personalization. Extensive experiments show that our system have generate high fidelity radar data in both signal and latent domains. It significantly improve zero-shot downstream mmWave$\rightarrow$ECG task, and enable Atrial Fibrillation classification with synthetic data. Further analysis evaluate the model interpretability.