Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecti...

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Bibliographic Details
Main Authors: Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Jorge Ivan Cuevas-Chávez, Raquel Ochoa-Ornelas, Antonio Navarrete-Guzmán, Carlos Vidrios-Serrano, Daniel Alejandro Sánchez-Arias
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Bioengineering
Subjects:
electronic nose
blood glucose prediction
diabetes
TinyML
exhaled breath analysis
machine learning
Online Access:https://www.mdpi.com/2306-5354/11/11/1065
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