PhysioGMC: Generalizable Multi-modal Coordination for Physiological Signals

Abstract

Physiological signals are widely used for health assessment in clinical and daily-life settings. Established physiological signals collected for inpatient and clinical use are often impractical for patients' daily home use due to their complexity and resource demands. In contrast, wearable signals enable continuous monitoring in everyday life, but many have limited reliability and are not widely understood or accepted in clinical practices. To leverage complementary strengths of clinical and wearable physiological signals, we propose PhysioGMC, a Generalizable Multi-modal Coordination framework for Physiological signals that explicitly accounts for their strong inter-subject variability. PhysioGMC incorporates both clinical and wearable modalities into the training process to improve cross-subject performance when only a single wearable modality is available at deployment. The framework introduces a cross-modal contrastive learning module comprising two contrastive losses to jointly learn label-relevant, subject-agnostic representations across modalities. The self-supervised contrastive loss aligns latent features across modalities, while the supervised contrastive loss encourages learning label-discriminative features that are invariant to subject identity. Experiments on cardiovascular health monitoring and sleep staging tasks demonstrate that PhysioGMC consistently outperforms existing methods, achieving superior cross-subject performance at test time using only wearable modalities, such as photoplethysmography (PPG).