Vol. 10 No. 2 (2024)
Articles

IMPLEMENTATION OF A REAL-TIME ARDUINO BASED NON-INVASIVE BLOOD GLUCOSE MONITORING SYSTEM

Mojisayo Feyikemi Owoeye
Department of Electrical/Electronics Engineering Technology, Nigerian Army College of Environmental Science and Technology, Makurdi, Benue State, Nigeria. feyimoji@gmail.com, +2348059007788
Ayodeji Babatunde Owoeye
Department of Electrical and Electronics Engineering, Joseph Sarwuam Tarka University, Makurdi, Benue State, Nigeria.

Published 2024-02-08

Keywords

  • Non-invasive,
  • near infrared spectroscopy,
  • blood glucose,
  • diabetes mellitus

How to Cite

Mojisayo Feyikemi Owoeye, & Ayodeji Babatunde Owoeye. (2024). IMPLEMENTATION OF A REAL-TIME ARDUINO BASED NON-INVASIVE BLOOD GLUCOSE MONITORING SYSTEM. International Journal of Advanced Academic Research, 10(2), 21-31. https://www.openjournals.ijaar.org/index.php/ijaar/article/view/409

How to Cite

Mojisayo Feyikemi Owoeye, & Ayodeji Babatunde Owoeye. (2024). IMPLEMENTATION OF A REAL-TIME ARDUINO BASED NON-INVASIVE BLOOD GLUCOSE MONITORING SYSTEM. International Journal of Advanced Academic Research, 10(2), 21-31. https://www.openjournals.ijaar.org/index.php/ijaar/article/view/409

Abstract

Diabetes mellitus is an acute metabolic disease that can cause damage to the body system and lead to complications if not properly managed. The disease has been classified as one of the world’s killer diseases by the World Health Organization (WHO). Implementing a non-invasive near-infrared monitoring device will make diagnosing and monitoring the diseases highly convenient without causing damage to any body tissue or inflicting pain. This paper presents the development of an optically-based glucose sensor using near-infrared spectroscopy to develop a non-invasive blood glucose monitor. The developed near-infrared spectroscopy device was implemented using an Arduino microcontroller, a 940nm LED, a photodiode, a noise filter, an amplifier circuit, and an LED display screen. An invitro experiment of output voltage against infused glucose solution in water placed within the sensor and photodiode is measured for calibration of the device. Twelve people were randomly selected, and the fasting blood glucose level was monitored by measuring it twice (the second measurement was taken after two hours of the first measurement) to get the mean fasting glucose level. The linear regression model was applied, and a correlation coefficient (R2) of 0.9369 was obtained. The high regression coefficient indicates a high correlation between the device's measurements and actual blood glucose levels. This implies that the device's readings are highly predictive of the true glucose concentration.

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