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Communication

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

1
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
2
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
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Author to whom correspondence should be addressed.
Academic Editors: Werner Mäntele and Andrea Facchinetti
Sensors 2021, 21(21), 6989; https://doi.org/10.3390/s21216989
Received: 28 August 2021 / Revised: 14 October 2021 / Accepted: 18 October 2021 / Published: 21 October 2021
(This article belongs to the Section Biomedical Sensors)
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. View Full-Text
Keywords: multisensor fusion; diabetes; metabolic heat production; regression model; noninvasive glucose concentration detection; wrist multisensor fusion; diabetes; metabolic heat production; regression model; noninvasive glucose concentration detection; wrist
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MDPI and ACS Style

Zhu, J.; Zhou, Y.; Huang, J.; Zhou, A.; Chen, Z. Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning. Sensors 2021, 21, 6989. https://doi.org/10.3390/s21216989

AMA Style

Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning. Sensors. 2021; 21(21):6989. https://doi.org/10.3390/s21216989

Chicago/Turabian Style

Zhu, Jianming, Yu Zhou, Junxiang Huang, Aojie Zhou, and Zhencheng Chen. 2021. "Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning" Sensors 21, no. 21: 6989. https://doi.org/10.3390/s21216989

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