Design of non-invasive blood glucose sensor and development of machine learning technique for detection

Blood glucose level monitoring advanced over the years due to an increase in health issues related to sugar, blood pressure, and so on. The Basic way is to test by a single drop of blood and other blood tests, but what if you do not need to poke or inject a needle to find an insulin level in your b...

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Main Author: Narayanan Revathi Sibi
Other Authors: Muhammad Faeyz Karim
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/156178
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1561782023-07-04T17:49:55Z Design of non-invasive blood glucose sensor and development of machine learning technique for detection Narayanan Revathi Sibi Muhammad Faeyz Karim School of Electrical and Electronic Engineering faeyz@ntu.edu.sg Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio Science::Medicine::Biosensors Blood glucose level monitoring advanced over the years due to an increase in health issues related to sugar, blood pressure, and so on. The Basic way is to test by a single drop of blood and other blood tests, but what if you do not need to poke or inject a needle to find an insulin level in your body. In a non-invasive way, we can calculate and estimate the accurate amount of insulin sugar level in the blood glucose by using cutting-edge technology and engineering. This Dissertation aims to develop the mm-wave at 60 GHz as a non-invasive sensor because the easurement method would use a machine learning technique to increase the accuracy of the glucose level estimation and then calculate the insulin level. In this work, we develop the antenna design and the thumb design to simulate the sensor using CST microwave studio. Cole-cole model for the blood and the analysis by varying the blood permittivity related to the glucose level and using machine learning technique to find the glucose level. The results that were obtained were promising and very relatable to the actual readings. The critical factor of this approach that made machine learning necessary to this project is a number of external independent variables that will influence the effect of output to the desired level; that’s where the machine learning helps out with great power, the accuracy of machine learning is very high with respect to your data input and validation set. The results percentage and accuracy are discussed in the conclusion . Master of Science (Computer Control and Automation) 2022-04-05T06:46:17Z 2022-04-05T06:46:17Z 2022 Thesis-Master by Coursework Narayanan Revathi Sibi (2022). Design of non-invasive blood glucose sensor and development of machine learning technique for detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156178 https://hdl.handle.net/10356/156178 en ISM-DISS-02257 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Science::Medicine::Biosensors
spellingShingle Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Science::Medicine::Biosensors
Narayanan Revathi Sibi
Design of non-invasive blood glucose sensor and development of machine learning technique for detection
description Blood glucose level monitoring advanced over the years due to an increase in health issues related to sugar, blood pressure, and so on. The Basic way is to test by a single drop of blood and other blood tests, but what if you do not need to poke or inject a needle to find an insulin level in your body. In a non-invasive way, we can calculate and estimate the accurate amount of insulin sugar level in the blood glucose by using cutting-edge technology and engineering. This Dissertation aims to develop the mm-wave at 60 GHz as a non-invasive sensor because the easurement method would use a machine learning technique to increase the accuracy of the glucose level estimation and then calculate the insulin level. In this work, we develop the antenna design and the thumb design to simulate the sensor using CST microwave studio. Cole-cole model for the blood and the analysis by varying the blood permittivity related to the glucose level and using machine learning technique to find the glucose level. The results that were obtained were promising and very relatable to the actual readings. The critical factor of this approach that made machine learning necessary to this project is a number of external independent variables that will influence the effect of output to the desired level; that’s where the machine learning helps out with great power, the accuracy of machine learning is very high with respect to your data input and validation set. The results percentage and accuracy are discussed in the conclusion .
author2 Muhammad Faeyz Karim
author_facet Muhammad Faeyz Karim
Narayanan Revathi Sibi
format Thesis-Master by Coursework
author Narayanan Revathi Sibi
author_sort Narayanan Revathi Sibi
title Design of non-invasive blood glucose sensor and development of machine learning technique for detection
title_short Design of non-invasive blood glucose sensor and development of machine learning technique for detection
title_full Design of non-invasive blood glucose sensor and development of machine learning technique for detection
title_fullStr Design of non-invasive blood glucose sensor and development of machine learning technique for detection
title_full_unstemmed Design of non-invasive blood glucose sensor and development of machine learning technique for detection
title_sort design of non-invasive blood glucose sensor and development of machine learning technique for detection
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156178
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