End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
For wearable device developers, this enables accurate, low-cost, and privacy-preserving blood pressure monitoring by automating the design of efficient deep neural networks.
This work introduces an automated DNN optimization pipeline combining NAS, pruning, and mixed-precision search to generate compact BP estimation models for wearables. The optimized models achieve up to 7.99% lower error with 7.5x parameter reduction, or up to 83x fewer parameters with negligible accuracy loss, fitting within 512 kB memory and achieving 142 ms latency and 7.25 mJ energy on a target SoC.
Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is desirable to ensure user data confidentiality. Recent deep neural networks (DNNs) have achieved high BP estimation accuracy by reconstructing BP waveforms or directly regressing BP values, but their large memory, computation, and energy requirements hinder deployment on wearables. This work introduces a fully automated DNN design pipeline that combines hardware-aware neural architecture search (NAS), pruning, and mixed-precision search (MPS) to generate accurate yet compact BP prediction models optimized for ultra-low-power multicore systems-on-chip (SoCs). Starting from state-of-the-art baseline models on four public datasets, our optimized networks achieve up to 7.99% lower error with a 7.5x parameter reduction, or up to 83x fewer parameters with negligible accuracy loss. All models fit within 512 kB of memory on our target SoC (GreenWaves' GAP8), requiring less than 55 kB and achieving an average inference latency of 142 ms and energy consumption of 7.25 mJ. Patient-specific fine-tuning further improves accuracy by up to 64%, enabling fully autonomous, low-cost BP monitoring on wearables.