Compact PPG-Based Blood Pressure Estimation Using Hardware-Efficient Knowledge Distillation
Continuous blood pressure (BP) monitoring from photoplethysmography (PPG) is compelling for wearable health applications, yet deep learning models for cuffless BP estimation are rarely evaluated under edge-deployment constraints. We propose a hardware-oriented knowledge distillation framework for compact PPG-only BP estimation, where deployable students are trained using ground-truth BP labels, teacher predictions, and feature-level supervision from either PPG-only or hybrid ECG–PPG teachers. Evaluated on PhysioNet PTT and VitalDB, knowledge distillation improves or maintains the accuracy–efficiency tradeoff of compact students, especially in datasets with minimal data. A compact 1-depth 16-channel Residual UNet student model maintained a deployable size of 20.7 k parameters with minimal penalty on average error. Hardware evaluation shows that the smallest, yet acceptable, student model fits within the VCU709 FPGA budget, while nRF5340 microcontroller unit deployment remains RAM-limited under full-window inference.