Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML

Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals. We also extend our testing to additional datasets to assess the robustness of our models against unseen distributions, thereby providing a comprehensive evaluation of the models' generalizability…