Enrico Macii
Enrico Macii has 2 papers in the ChatPPG research atlas (2024–2026) and 8 co-authors in the atlas.
Papers
- End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables2026 · arXiv preprint
This work builds compact neural networks for estimating blood pressure from PPG on low-power wearable chips. It combines hardware-aware neural architecture search, pruning and mixed-precision search into one automated pipeline. Starting from top models on four public datasets, it produced networks with up to 7.99% lower error and 7.5 times fewer parameters, or 83 times fewer parameters with almost no loss, and patient-specific fine-tuning improved accuracy by up to 64%.
- Published: ArXiv preprint, 2026
- Signals: PPG, Blood pressure
- Result: Starting from state-of-the-art baseline models on four public datasets, their 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.
- Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables2024 · arXiv preprint
This paper optimizes deep networks for estimating blood pressure from PPG so they fit low-power wearables such as the GAP8 chip. Starting from top models on four public datasets, the optimized versions had up to 4.99% lower error or 73.36% smaller size at the same error. The most accurate one used just 0.37 mJ per estimate and reached a diastolic MAE of 8.08.
- Published: ArXiv preprint, 2024
- Signals: PPG, Blood pressure
- Result: Starting from both regression and signal-to-signal state-of-the-art models on four public datasets, the authors obtain optimized versions that achieve up to 4.99% lower error or 73.36% lower size at iso-error.