Enhancing the accuracy of non-invasive glucose sensing in aqueous solutions using combined millimeter wave and near infrared transmission

Helena Cano-Garcia; Rohit Kshirsagar; Roberto Pricci; Ahmed Teyeb; Fergus O’brien; Shimul Saha; Panagiotis Kosmas; Efthymios Kallos

doi:10.3390/s21093275

Enhancing the accuracy of non-invasive glucose sensing in aqueous solutions using combined millimeter wave and near infrared transmission

Helena Cano-Garcia^*, Rohit Kshirsagar, Roberto Pricci, Ahmed Teyeb, Fergus O’brien, Shimul Saha, Panagiotis Kosmas, Efthymios Kallos

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Original language	English
Article number	3275
Journal	SENSORS
Volume	21
Issue number	9
DOIs	https://doi.org/10.3390/s21093275
Publication status	Published - 1 May 2021

Keywords

Diabetes
Glucose
Monitor
Near infrared
Non-invasive
Radiofrequency

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/s21093275

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abstract = "We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.",

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note = "Funding Information: Funding: This research was funded partially by Innovate UK project 104554 (2019–2021). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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AU - Cano-Garcia, Helena

AU - Kshirsagar, Rohit

AU - Pricci, Roberto

AU - Teyeb, Ahmed

AU - O’brien, Fergus

AU - Saha, Shimul

AU - Kosmas, Panagiotis

AU - Kallos, Efthymios

N1 - Funding Information: Funding: This research was funded partially by Innovate UK project 104554 (2019–2021). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

AB - We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

KW - Diabetes

KW - Glucose

KW - Monitor

KW - Near infrared

KW - Non-invasive

KW - Radiofrequency

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Enhancing the accuracy of non-invasive glucose sensing in aqueous solutions using combined millimeter wave and near infrared transmission

Abstract

Keywords

UN SDGs

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