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Non-invasive glucometer monitoring system through optical based near-infrared sensor method

  • S. Vanaja
  • , T. Ravi Babu
  • , M. Malathi
  • , Kuldeep K. Saxena
  • , J. Joselin Jeya Sheela
  • , S. Suruthi
  • , B. Stalin
  • , N. Nagaprasad
  • , Ramaswamy Krishnaraj*
  • , Din Bandhu*
  • , Uma Reddy
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

Original languageEnglish
Article number2327423
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Volume12
Issue number1
DOIs
Publication statusPublished - 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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