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A Comprehensive Review on Glucobreath Meter
Subject area: Science,Engineering and Technology · Area of research: Internet of Things
Abstract
Non-invasive glucose monitoring through breath analysis represents a breakthrough in diabetes care, offering a pain-free way to track blood sugar levels. This approach creates a unified platform where users can exhale into a device to detect acetone?a key indicator of glucose fluctuations?and receive immediate feedback. The system encourages regular monitoring, early intervention for prediabetes, and data sharing with healthcare providers to support better management strategies. Built with accessible IoT tools like the ESP32 microcontroller for processing, TGS822 sensor for gas detection, SHT31 for environmental adjustments, and Blynk for cloud connectivity, the setup prioritizes ease of use, low cost, and reliability. Elements such as on-device displays, real-time uploads, and trend tracking boost user involvement and practical application. Beyond daily checks, this innovation helps institutions and clinicians monitor patient progress, refine treatment plans, and build supportive networks. In essence, the Gluco-Breath Meter illustrates how sensor technology and IoT can transform diabetes management into a more inclusive, efficient, and patient-centered process.
References
[1] Kapur R, Kumar Y, Sharma R, et al. GlucoBreath: an IoT, ML, and breath-based non-invasive glucose meter. IEEE Access. 2024;12:59357-59370.
[2] Kapur R, et al. GlucoBreath: non-invasive glucometer to detect diabetes using breath. TechRxiv. Preprint posted December 7, 2023. https://www.techrxiv.org/users/706289/articles/691706
[3] An IoT, ML and breath based non-invasive glucose meter. International Research Journal of Modernization in Engineering Technology and Science. Accessed November 8, 2025.
[4] Wei SC, et al. Nanowire Array Breath Acetone Sensor for Diabetes Monitoring. Advanced Science. 2024;11(19):2309481.
[5] Mollick S, et al. Unlocking Diabetic Acetone Vapor Detection by A Portable Metal-Organic Framework-Based Turn-On Optical Sensor Device. Advanced Science. 2024;11(9):2305070.
[6] Gudiño-Ochoa A, et al. Non-Invasive Multiclass Diabetes Classification Using Breath Biomarkers and Machine Learning with Explainable AI. Bioengineering. 2025;12(6):51.
How to cite this paper
@article{1712058,
author = {Rohit Bhanudas Gore, Satish Sandip Jadhav, Pratik Pravin Dhawale, Chaitanya Sandesh Mahale},
title = {A Comprehensive Review on Glucobreath Meter},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {2534-2536},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712058.pdf},
abstract = {Non-invasive glucose monitoring through breath analysis represents a breakthrough in diabetes care, offering a pain-free way to track blood sugar levels. This approach creates a unified platform where users can exhale into a device to detect acetone?a key indicator of glucose fluctuations?and receive immediate feedback. The system encourages regular monitoring, early intervention for prediabetes, and data sharing with healthcare providers to support better management strategies. Built with accessible IoT tools like the ESP32 microcontroller for processing, TGS822 sensor for gas detection, SHT31 for environmental adjustments, and Blynk for cloud connectivity, the setup prioritizes ease of use, low cost, and reliability. Elements such as on-device displays, real-time uploads, and trend tracking boost user involvement and practical application. Beyond daily checks, this innovation helps institutions and clinicians monitor patient progress, refine treatment plans, and build supportive networks. In essence, the Gluco-Breath Meter illustrates how sensor technology and IoT can transform diabetes management into a more inclusive, efficient, and patient-centered process.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712058}
}