Non-invasive, Intelligent Blood Glucose Monitoring on Fingertip Using Dual-band Fusion and LSTM-R Network
Non-invasive, painless, and continuous monitoring sensing systems are needed to enhance the life quality of the diabetic. In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-...
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description | Non-invasive, painless, and continuous monitoring sensing systems are needed to enhance the life quality of the diabetic. In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-short-term memory (LSTM-R) are introduced based on UWB microwave technique, to overcome the negative effects of antenna miniaturization. The proposed composite end-to-end framework can achieve the high accuracy and robustness of BGL prediction. The safety and reliability of proposed microwave-based sensor were verified by simulation and experiment. The reported sensing system is validated on the medium under tests (MUT) with different glucose solutions and the human trials. For the measurement of MUT, the testing results achieve a root mean squared error (RMSE) of 4.2367 mg/dL and a mean absolute relative difference (MARD) of 2.79%. During human trials, the non-invasive estimation results obtain a low MARD of 5.9%, and are fallen 100% into the clinically acceptable zones by Clark error grid analysis. |
doi_str_mv | 10.1109/JSEN.2023.3344230 |
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In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-short-term memory (LSTM-R) are introduced based on UWB microwave technique, to overcome the negative effects of antenna miniaturization. The proposed composite end-to-end framework can achieve the high accuracy and robustness of BGL prediction. The safety and reliability of proposed microwave-based sensor were verified by simulation and experiment. The reported sensing system is validated on the medium under tests (MUT) with different glucose solutions and the human trials. For the measurement of MUT, the testing results achieve a root mean squared error (RMSE) of 4.2367 mg/dL and a mean absolute relative difference (MARD) of 2.79%. 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In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-short-term memory (LSTM-R) are introduced based on UWB microwave technique, to overcome the negative effects of antenna miniaturization. The proposed composite end-to-end framework can achieve the high accuracy and robustness of BGL prediction. The safety and reliability of proposed microwave-based sensor were verified by simulation and experiment. The reported sensing system is validated on the medium under tests (MUT) with different glucose solutions and the human trials. For the measurement of MUT, the testing results achieve a root mean squared error (RMSE) of 4.2367 mg/dL and a mean absolute relative difference (MARD) of 2.79%. During human trials, the non-invasive estimation results obtain a low MARD of 5.9%, and are fallen 100% into the clinically acceptable zones by Clark error grid analysis.</description><subject>Glucose</subject><subject>Glucose sensor</subject><subject>Intelligent sensors</subject><subject>Long short term memory (LSTM)</subject><subject>Microstrip slot antenna</subject><subject>Microwave antennas</subject><subject>Microwave measurement</subject><subject>Microwave theory and techniques</subject><subject>Monitoring</subject><subject>Non-invasive detection</subject><subject>Sensors</subject><subject>Ultra-wideband microwave (UWB)</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFistOwzAQRS0EEuXxAUgs5gNwsDOJnGyBhodoFrRI7CpDh2rA2JXtFPH3NBJ7VufonivEmVaF1qq9fJhP-6JUJRaIVVWi2hMTXdeN1KZq9kdHJSs0L4fiKKUPpXRrajMR3Acv2W9t4i1dwL3P5ByvyWe4ciGs4NYNbyERzILnHCL7NQQP3Y4UM2_gOY3TzWCdfLV-Bd2QeHcY9XG-mMkn6Cl_h_h5Ig7erUt0-sdjcd5NF9d3kolouYn8ZePPUis02KgW_8m_YbhIcg</recordid><startdate>20231223</startdate><enddate>20231223</enddate><creator>Wang, Zengxiang</creator><creator>Xiao, Xia</creator><creator>Pang, Yanwei</creator><creator>Su, Wenling</creator><creator>Kikkawa, Takamaro</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><orcidid>https://orcid.org/0000-0001-6670-3727</orcidid><orcidid>https://orcid.org/0000-0001-5497-9734</orcidid><orcidid>https://orcid.org/0000-0001-6689-2396</orcidid></search><sort><creationdate>20231223</creationdate><title>Non-invasive, Intelligent Blood Glucose Monitoring on Fingertip Using Dual-band Fusion and LSTM-R Network</title><author>Wang, Zengxiang ; Xiao, Xia ; Pang, Yanwei ; Su, Wenling ; Kikkawa, Takamaro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_103738093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Glucose</topic><topic>Glucose sensor</topic><topic>Intelligent sensors</topic><topic>Long short term memory (LSTM)</topic><topic>Microstrip slot antenna</topic><topic>Microwave antennas</topic><topic>Microwave measurement</topic><topic>Microwave theory and techniques</topic><topic>Monitoring</topic><topic>Non-invasive detection</topic><topic>Sensors</topic><topic>Ultra-wideband microwave (UWB)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Zengxiang</creatorcontrib><creatorcontrib>Xiao, Xia</creatorcontrib><creatorcontrib>Pang, Yanwei</creatorcontrib><creatorcontrib>Su, Wenling</creatorcontrib><creatorcontrib>Kikkawa, Takamaro</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Zengxiang</au><au>Xiao, Xia</au><au>Pang, Yanwei</au><au>Su, Wenling</au><au>Kikkawa, Takamaro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non-invasive, Intelligent Blood Glucose Monitoring on Fingertip Using Dual-band Fusion and LSTM-R Network</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2023-12-23</date><risdate>2023</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Non-invasive, painless, and continuous monitoring sensing systems are needed to enhance the life quality of the diabetic. In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-short-term memory (LSTM-R) are introduced based on UWB microwave technique, to overcome the negative effects of antenna miniaturization. The proposed composite end-to-end framework can achieve the high accuracy and robustness of BGL prediction. The safety and reliability of proposed microwave-based sensor were verified by simulation and experiment. The reported sensing system is validated on the medium under tests (MUT) with different glucose solutions and the human trials. For the measurement of MUT, the testing results achieve a root mean squared error (RMSE) of 4.2367 mg/dL and a mean absolute relative difference (MARD) of 2.79%. 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subjects | Glucose Glucose sensor Intelligent sensors Long short term memory (LSTM) Microstrip slot antenna Microwave antennas Microwave measurement Microwave theory and techniques Monitoring Non-invasive detection Sensors Ultra-wideband microwave (UWB) |
title | Non-invasive, Intelligent Blood Glucose Monitoring on Fingertip Using Dual-band Fusion and LSTM-R Network |
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