GluQo: IoT-based non-invasive blood glucose monitoring

Diabetes is one of the deadliest diseases worldwide. To prevent further complications due to diabetes, it is vital to regularly monitor the blood glucose level. Conventional way to measure blood glucose is invasive, which involves finger puncturing. This method is painful and increases risk of infec...

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Main Authors: Aizat Rahmat, M. A., Su, E. L. M., Mohd. Addi, M., Yeong, C. F.
Format: Article
Published: Universiti Teknikal Malaysia Melaka 2017
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Online Access:http://eprints.utm.my/id/eprint/76575/
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.765752018-05-31T09:26:09Z http://eprints.utm.my/id/eprint/76575/ GluQo: IoT-based non-invasive blood glucose monitoring Aizat Rahmat, M. A. Su, E. L. M. Mohd. Addi, M. Yeong, C. F. TK Electrical engineering. Electronics Nuclear engineering Diabetes is one of the deadliest diseases worldwide. To prevent further complications due to diabetes, it is vital to regularly monitor the blood glucose level. Conventional way to measure blood glucose is invasive, which involves finger puncturing. This method is painful and increases risk of infection. In this project, GluQo, a noninvasive method to monitor glucose level was proposed. Near Infrared LED was placed over the fingertip to measure blood glucose optically and the glucose concentration of the blood was calculated depending on the intensity of the received light. The signal was then filtered and amplified before being fed into the microcontroller to be displayed on an LCD display. The glucose level of a person was predicted based on the analyzed voltages received. The glucose readings were also sent to a phone via WiFi and displayed through an Android application. Validation and calibration were performed for the prototype. The percentage error of the glucose reading for the designed method was 7.20% compared to the prick method. The correlation coefficient obtained from the calibration graph of voltage versus glucose concentration was 0.9642, which indicated a strong relationship. Therefore, it can be concluded that there is a high correlation between the predicted glucose values and the voltage signals from the sensor. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed Aizat Rahmat, M. A. and Su, E. L. M. and Mohd. Addi, M. and Yeong, C. F. (2017) GluQo: IoT-based non-invasive blood glucose monitoring. Journal of Telecommunication, Electronic and Computer Engineering, 9 (3-9). pp. 71-75. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041914487&partnerID=40&md5=ef4e196009ccec79266dad03aa1428ab
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Aizat Rahmat, M. A.
Su, E. L. M.
Mohd. Addi, M.
Yeong, C. F.
GluQo: IoT-based non-invasive blood glucose monitoring
description Diabetes is one of the deadliest diseases worldwide. To prevent further complications due to diabetes, it is vital to regularly monitor the blood glucose level. Conventional way to measure blood glucose is invasive, which involves finger puncturing. This method is painful and increases risk of infection. In this project, GluQo, a noninvasive method to monitor glucose level was proposed. Near Infrared LED was placed over the fingertip to measure blood glucose optically and the glucose concentration of the blood was calculated depending on the intensity of the received light. The signal was then filtered and amplified before being fed into the microcontroller to be displayed on an LCD display. The glucose level of a person was predicted based on the analyzed voltages received. The glucose readings were also sent to a phone via WiFi and displayed through an Android application. Validation and calibration were performed for the prototype. The percentage error of the glucose reading for the designed method was 7.20% compared to the prick method. The correlation coefficient obtained from the calibration graph of voltage versus glucose concentration was 0.9642, which indicated a strong relationship. Therefore, it can be concluded that there is a high correlation between the predicted glucose values and the voltage signals from the sensor.
format Article
author Aizat Rahmat, M. A.
Su, E. L. M.
Mohd. Addi, M.
Yeong, C. F.
author_facet Aizat Rahmat, M. A.
Su, E. L. M.
Mohd. Addi, M.
Yeong, C. F.
author_sort Aizat Rahmat, M. A.
title GluQo: IoT-based non-invasive blood glucose monitoring
title_short GluQo: IoT-based non-invasive blood glucose monitoring
title_full GluQo: IoT-based non-invasive blood glucose monitoring
title_fullStr GluQo: IoT-based non-invasive blood glucose monitoring
title_full_unstemmed GluQo: IoT-based non-invasive blood glucose monitoring
title_sort gluqo: iot-based non-invasive blood glucose monitoring
publisher Universiti Teknikal Malaysia Melaka
publishDate 2017
url http://eprints.utm.my/id/eprint/76575/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041914487&partnerID=40&md5=ef4e196009ccec79266dad03aa1428ab
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