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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2022-02-04
Noninvasive Continuous Glucose Monitoring on Aqueous Solutions Using Microwave Sensor with Machine Learning
By
Saeed M. Bamatraf,

    Dr. Saeed M. Bamatraf

    Department of Electrical and Computer Engineering

    University of Watreloo

    Canada

    Homepage

Maged A. Aldhaeebi,

    Dr. Maged A. Aldhaeebi

    Electrical and Computer Engineering Department

    University of Waterloo

    Canada

    Homepage

Omar M. Ramahi

    Professor Omar M. Ramahi

    University of Waterloo

    Canada

    Homepage

Progress In Electromagnetics Research Letters, Vol. 102, 127-134, 2022
doi:10.2528/PIERL21110905 | Google Scholar

Abstract
In this paper, an electrically-small microwave dipole sensor is used with machine learning algorithms to build a noninvasive continuous glucose monitoring (CGM) system. As a proof of concept, the sensor is used on aqueous (water-glucose) solutions with different glucose concentrations to check the sensitivity of the sensor. Knowledge-driven and data-driven approaches are used to extract features from the sensor's signals reflected from the aqueous glucose solution. Machine learning is used to build the regression model in order to predict the actual glucose levels. Using more than 19 regression models, the results show a good accuracy with Root Mean Square Error of 1.6 and 1.7 by Matern 5/2 Gaussian Process Regression (GPR) algorithm using the reflection coefficient's magnitude and phase.
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Citation
Saeed M. Bamatraf, Maged A. Aldhaeebi, and Omar M. Ramahi, "Noninvasive Continuous Glucose Monitoring on Aqueous Solutions Using Microwave Sensor with Machine Learning," Progress In Electromagnetics Research Letters, Vol. 102, 127-134, 2022.
doi:10.2528/PIERL21110905
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