Modeling Localized PPG for Blood Pressure Forecasting With MoE and Quantile Regression

Accurate blood pressure (BP) monitoring using photoplethysmography (PPG) remains challenging due to noise, individual variability, and nonlinear signal dynamics. In this work, we present FBPP-Net, a lightweight temporal-mixing framework that integrates sparse and shared Mixture-of-Experts (MoE) modules with quantile regression. The model enables specialized subnetworks to capture diverse temporal dependencies while providing robust probabilistic estimation of systolic and diastolic BP. Without requiring GPU acceleration, FBPP-Net achieves efficient training and inference, making it suitable for real-time wearable applications. Experiments on the UQVS dataset show that FBPP-Net-MoE attains SBP/DBP errors of 2.83/3.54 mmHg, and FBPP-Net-TM achieves 3.08/3.18 mmHg, outperforming XGBoost, LSTM, MLP, Informer, and TSMixer baselines. Furthermore, the analysis of expert activations and temporal segments provides interpretable insights into the localized dynamics driving near-future BP variations, supporting intelligent and explainable physiological monitoring.