End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
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 ult...
| Authors | Francesco Carlucci, Giovanni Pollo, Xiaying Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 April 2026 |
| Last updated at source | 25 April 2026 |
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