PhysPPG: Physics-Informed Contrastive Learning for Photoplethysmography Representation

Photoplethysmography (PPG) enables accessible cardiovascular monitoring, yet motion and acquisition artifacts can obscure the waveform structure needed for reliable physiological inference. We introduce PhysPPG, a physics-informed contrastive learning framework for robust PPG representation learning. A temporal encoder maps PPG signals to latent trajectories, which are jointly shaped by a contrastive objective and a generalized Euler–Lagrange residual. Learnable kinetic and potential energies together with non-conservative forces provide a low-order dynamical inductive bias without requiring physiological labels or identifying latent coordinates with specific hemodynamic variables. Evaluations with frozen encoders highlight benefits for blood-pressure estimation and heart-rate estimation on WildPPG, a challenging dataset with substantial motion and environmental variation. The learned residual also supports PPG–ECG modality discrimination, providing a complementary diagnostic of compatibility with the learned dynamics. These findings suggest that physics-informed latent dynamics can complement contrastive learning for hemodynamic inference and challenging ambulatory PPG analysis.