Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentrati...

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Autores principales:	Jianming Zhu, Yu Zhou, Junxiang Huang, Aojie Zhou, Zhencheng Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multisensor fusion diabetes metabolic heat production regression model noninvasive glucose concentration detection wrist Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/930e1634e73b4af28d1ff0e6af93973b
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spelling	oai:doaj.org-article:930e1634e73b4af28d1ff0e6af93973b2021-11-11T19:01:55ZNoninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning10.3390/s212169891424-8220https://doaj.org/article/930e1634e73b4af28d1ff0e6af93973b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6989https://doaj.org/toc/1424-8220Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.Jianming ZhuYu ZhouJunxiang HuangAojie ZhouZhencheng ChenMDPI AGarticlemultisensor fusiondiabetesmetabolic heat productionregression modelnoninvasive glucose concentration detectionwristChemical technologyTP1-1185ENSensors, Vol 21, Iss 6989, p 6989 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multisensor fusion diabetes metabolic heat production regression model noninvasive glucose concentration detection wrist Chemical technology TP1-1185
spellingShingle	multisensor fusion diabetes metabolic heat production regression model noninvasive glucose concentration detection wrist Chemical technology TP1-1185 Jianming Zhu Yu Zhou Junxiang Huang Aojie Zhou Zhencheng Chen Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
description	Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.
format	article
author	Jianming Zhu Yu Zhou Junxiang Huang Aojie Zhou Zhencheng Chen
author_facet	Jianming Zhu Yu Zhou Junxiang Huang Aojie Zhou Zhencheng Chen
author_sort	Jianming Zhu
title	Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
title_short	Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
title_full	Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
title_fullStr	Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
title_full_unstemmed	Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning
title_sort	noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/930e1634e73b4af28d1ff0e6af93973b
work_keys_str_mv	AT jianmingzhu noninvasivebloodglucoseconcentrationmeasurementbasedonconservationofenergymetabolismandmachinelearning AT yuzhou noninvasivebloodglucoseconcentrationmeasurementbasedonconservationofenergymetabolismandmachinelearning AT junxianghuang noninvasivebloodglucoseconcentrationmeasurementbasedonconservationofenergymetabolismandmachinelearning AT aojiezhou noninvasivebloodglucoseconcentrationmeasurementbasedonconservationofenergymetabolismandmachinelearning AT zhenchengchen noninvasivebloodglucoseconcentrationmeasurementbasedonconservationofenergymetabolismandmachinelearning
_version_	1718431659219484672

Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

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