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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/6989 |
| _version_ | 1797511793564712960 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:53:04Z |
| format | Article |
| id | doaj.art-930e1634e73b4af28d1ff0e6af93973b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:04Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-930e1634e73b4af28d1ff0e6af93973b2023-11-22T21:34:54ZengMDPI AGSensors1424-82202021-10-012121698910.3390/s21216989Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine LearningJianming Zhu0Yu Zhou1Junxiang Huang2Aojie Zhou3Zhencheng Chen4School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaBlood 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.https://www.mdpi.com/1424-8220/21/21/6989multisensor fusiondiabetesmetabolic heat productionregression modelnoninvasive glucose concentration detectionwrist |
| spellingShingle | Jianming Zhu Yu Zhou Junxiang Huang Aojie Zhou Zhencheng Chen Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning Sensors multisensor fusion diabetes metabolic heat production regression model noninvasive glucose concentration detection wrist |
| title | 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_short | 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 |
| topic | multisensor fusion diabetes metabolic heat production regression model noninvasive glucose concentration detection wrist |
| url | https://www.mdpi.com/1424-8220/21/21/6989 |
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