MSMA-PPG: Channel-Aligned Expert Routing for Selected-Wavelength PPG Prediction

Selecting a photoplethysmography (PPG) wavelength by signal quality does not necessarily minimize physiological prediction error. We introduce MSMA-PPG, a framework that learns from synchronized multi-wavelength recordings and predicts from one selected PPG stream and acceleration. Wavelength-specific projections and residual adapters share an optical temporal backbone, full-to-subset alignment links paired training views, and a Channel-Aligned Mixture of Experts combines optical, motion, and fused representations through availability-masked top-2 routing. We evaluate single-stream representation transfer across heart rate (HR), blood pressure (BP), and atrial fibrillation (AF), alongside paired HR diagnostics of wavelength selection and expert routing. Across the seven-model PPG-only test results, with routing disabled, MSMA-PPG has the lowest reported participant-macro HR MAE on EarSet and PPG-DaLiA (5.020 and 9.246 bpm), the lowest systolic BP MAE (15.456 mmHg), and the second-highest AF AUPRC/AUROC (0.5177/0.8909). The paired diagnostics reveal a different ordering after adaptation: although frozen affine probes favor aligned initialization on EarSet and PPG-DaLiA, adapted aligned models have higher window-pooled MAE than reconstruction-only controls by 0.797, 1.485, and 0.373 bpm on WildPPG, EarSet, and PPG-DaLiA. Retrospective selection from recorded wavelengths also produces model-dependent responses: lower composite policy scores sometimes reduce HR error. Frozen expert interventions reveal strong dependence on the trained motion/shared pathways and mixture weights, despite the aligned model's higher absolute error. These findings distinguish competitive optical representation transfer from the separate effects of acquisition choices and fitted expert routing on downstream accuracy.