media Hugging Face Forums · 3d ago · open_models

GlucoEdge: on-device glucose trend forecasting with INT8 quantization

from English

Independent researcher Mohamed Menasy has published GlucoEdge, an engineering study detailing an end-to-end on-device machine learning pipeline for five-class, 15-minute glucose trend forecasting from continuous glucose monitor data. The work introduces a compact 1D CNN containing only 2,909 parameters and covers the full workflow from PyTorch to LiteRT conversion.

Static INT8 post-training quantization reduced the model artifact size by approximately 31%.
Quantization caused macro recall to drop from 0.5060 to 0.4135, with performance falling across all four directional classes.
The study analyzes deployment size alongside the impact of quantization on minority-class behavior, including calibration-data coverage and input-range clipping.
Offline inference was tested on a physical Android device, with code and reproducibility materials available on GitHub.

The author explicitly notes limitations such as overlapping time-series windows and context-prefix leakage risk, clarifying that this is a research prototype rather than a medical system.

Importance 1/3 Trust 1/3 Hugging Face Forums Inference efficiency Research paper

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