GluQo: IoT-Based Non-invasive Blood Glucose Monitoring


  • M. A. Aizat Rahmat Faculty of Electrical Engineering UTM, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia.
  • E. L. M. Su Department of Electronic and Computer Engineering, Faculty of Electrical Engineering UTM, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia.
  • M. Mohd Addi Department of Electronic and Computer Engineering, Faculty of Electrical Engineering UTM, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia.
  • C. F. Yeong Center of Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia.


Absorption Spectroscopy, Blood Glucose, IoT, Non-invasive,


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.


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How to Cite

Rahmat, M. A. A., Su, E. L. M., Mohd Addi, M., & Yeong, C. F. (2017). GluQo: IoT-Based Non-invasive Blood Glucose Monitoring. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-9), 71–75. Retrieved from

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