{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T03:09:43Z","timestamp":1771124983985,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T00:00:00Z","timestamp":1558051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1502310"],"award-info":[{"award-number":["1502310"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an embedded system-based solution for sensor arrays to estimate blood glucose levels from volatile organic compounds (VOCs) in a patient\u2019s 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\u2019s breath.<\/jats:p>","DOI":"10.3390\/s19102283","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T11:06:46Z","timestamp":1558091206000},"page":"2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds"],"prefix":"10.3390","volume":"19","author":[{"given":"Matthew","family":"Boubin","sequence":"first","affiliation":[{"name":"Intelligent Systems Laboratory, Department of Engineering Science, Sonoma State University, Rohnert Park, CA 94928, USA"},{"name":"Department of Electrical and Computer Engineering, Miami University, Oxford, OH 45056, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudhir","family":"Shrestha","sequence":"additional","affiliation":[{"name":"Intelligent Systems Laboratory, Department of Engineering Science, Sonoma State University, Rohnert Park, CA 94928, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,17]]},"reference":[{"key":"ref_1","unstructured":"Center for Disease Control and Prevention (2017, August 04). 2017 Statistics on Diabetes, Available online: http:\/\/www.cdc.gov\/diabetes\/statistics\/prev\/national\/gifpersons.htm."},{"key":"ref_2","unstructured":"(2017, August 04). 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