A Non-Invasive Measuring and Monitoring Blood Glucose System Based on Deep Learning Algorithm

Abstract

The persistent challenge of achieving accurate, painless, and continuous blood glucose monitoring in diabetes management has driven intensive research toward non-invasive technological solutions that balance precision, accessibility, and patient comfort. This work examines recent advancements in sensor-based glucose estimation and emphasizes the strong synergy between modern sensing technologies and deep learning methodologies. Emerging techniques such as bioimpedance analysis, near-infrared (NIR), and mid-infrared (Mid-IR) spectroscopy have demonstrated promising potential for glucose monitoring without the need for invasive blood sampling. In parallel, hybrid sensor architectures combined with artificial intelligence–driven predictive models have shown the ability to mitigate traditional limitations and enhance real-time monitoring performance. Despite these advances, significant challenges remain, including environmental sensitivity, inter-individual physiological variability, and calibration stability. To address these issues, adaptive machine learning frameworks are being developed to improve system robustness and reliability. This review critically evaluates current progress, identifies existing limitations, and outlines future research directions toward clinically viable, FDA-compliant non-invasive glucose monitoring systems.