Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds.

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ID: 22346
2019
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Abstract
This paper presents an embedded system-based solution for sensor arrays to estimate blood glucose levels from volatile organic compounds (VOCs) in a patient's breath. Support vector machine (SVM) was trained on a general-purpose computer using an existing SVM library. A training model, optimized to achieve the most accurate results, was implemented in a microcontroller with an ATMega microprocessor. Training and testing was conducted using artificial breath that mimics known VOC footprints of high and low blood glucose levels. The embedded solution was able to correctly categorize the corresponding glucose levels of the artificial breath samples with 97.1% accuracy. The presented results make a significant contribution toward the development of a portable device for detecting blood glucose levels from a patient's breath.
Reference Key
boubin2019microcontrollersensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boubin, Matthew;Shrestha, Sudhir;
Journal sensors
Year 2019
DOI
E2283
URL
https://www.mdpi.com/1424-8220/19/10/2283
Keywords Keywords not found

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