Issue |
MATEC Web Conf.
Volume 410, 2025
2025 3rd International Conference on Materials Engineering, New Energy and Chemistry (MENEC 2025)
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Article Number | 04002 | |
Number of page(s) | 7 | |
Section | Intelligent Systems and Sensor Technologies for Autonomous Operations | |
DOI | https://doi.org/10.1051/matecconf/202541004002 | |
Published online | 24 July 2025 |
Non-Invasive Glucose Monitoring Technologies Innovations Applications and Future Directions
Reading College, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu, 210044, China
* Corresponding author: mk808939@student.reading.ac.uk
Diabetes mellitus has emerged as a major chronic disease threatening global health. While traditional glucometers enable point-of- care testing, their invasive nature induces patient resistance that significantly compromises disease management efficacy. Contemporary non-invasive technologies have achieved breakthrough progress, yet critical research gaps persist in multi-physics coupling mechanisms and personalized calibration model development. This comprehensive review analyzes principle innovations and industrial applications of non-invasive glucose monitoring technologies, employing technical evolution pathway analysis and clinical data benchmarking to evaluate seven methodological paradigms - including spectroscopy, electrochemistry, and microwave sensing - along with their translational achievements in wearable devices and healthcare systems. Key findings demonstrate: multi-modal sensing reduces detection errors to 8.7% through signal complementarity, with millimeter-wave radar technology achieving 5-minute continuous monitoring (r=0.912); flexible electronic skin breakthroughs 72-hour operational endurance (sensitivity 0.03nA/(mg/dL)); and intelligent closed-loop systems enhance glycated hemoglobin compliance rates by 42%. Current technical bottlenecks manifest as individual calibration variation coefficients exceeding 12% and blood-interstitial fluid glucose lag (8-15 minutes), with emerging solutions trending toward deep learning-based dynamic compensation models (83% error correction) and terahertz quantum cascade detection (0.1mmol/L detection limit).
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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