IRO Journals
Journal of Electronics and Informatics

GlucoBalance: Non-Invasive Blood Glucose Monitoring via Multi-Wavelength Optical Spectroscopy and Machine Learning

Open Access
Published: 01 October, 2026
Pages: 345-361
  • Mandakini Sapkota , Mandakini Sapkota
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
  • Jayanti Thakur , Jayanti Thakur
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
  • Monika Karki , Monika Karki
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
  • Ukesh Aryal , Ukesh Aryal
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
    Student, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
  • Suraj Basant Tulachan Suraj Basant Tulachan
    Assistant Professor, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
    Assistant Professor, Department of Electronics and Computer Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, Nepal
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How to Cite

Sapkota, Mandakini, Jayanti Thakur, Monika Karki, Ukesh Aryal, and Suraj Basant Tulachan. 2026. “GlucoBalance: Non-Invasive Blood Glucose Monitoring via Multi-Wavelength Optical Spectroscopy and Machine Learning”. Journal of Electronics and Informatics 8 (4): 345-61. https://doi.org/10.36548/jei.2026.4.003.
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Keywords

Non-invasive Glucose Monitoring
Optical Spectroscopy
Near-infrared (NIR)
Multilayer Perceptron (MLP)
AS7341 Spectral Sensor
Photoplethysmography (PPG)
Machine Learning in Healthcare
Embedded Medical Devices

Abstract

Diabetes has become one of the critical health problems globally, with rapidly rising prevalence in developing regions. Nevertheless, many people are still dependent on the finger-pricking method to check blood glucose level. This conventional method is painful and inconvenient for daily use, especially for children and elderly people. To address these limitations, this paper presents GlucoBalance, a non-invasive glucose monitoring system. It uses multi-wavelength optical spectroscopy and machine learning methods to predict blood glucose level. In this device, a multi-wavelength LED array (White, Red, Green, and Infrared), and an AS7341 spectral sensor are used to detect optical signals from the skin. These signals are sent through Bluetooth Low Energy (BLE) to a Flutter-based mobile app by ESP32-C3, which then forwards the data to the model via backend for glucose prediction. With this motive, we tested two machine learning models: a Multiple Linear Regression model and a Multilayer Perceptron (MLP) Regressor. After evaluation, we observed that the MLP results were much better than the simple linear model, achieving an R² of 0.7704 and a Mean Absolute Relative Difference (MARD) of just 5.92%, compared to an R² of 0.2763 and MARD of 12.27% for linear regression. This implies that non-linear models would be more appropriate for the capture of this complicated relationship between the optical signal and glucose. Despite the success achieved by this experiment, there are certain limitations to this experiment such as the limited data and the hardware computing limit. Hence, this approach reflects that affordable, painless, and accessible glucose monitoring solutions are worth developing further.

References

  1. World Health Organization. "Diabetes." WHO Fact Sheets (2024). Available https://www.who.int/news-room/fact-sheets/detail/diabetes
  2. Shrestha, Dhan Bahadur, Pravash Budhathoki, Yub Raj Sedhai, Achyut Marahatta, Samit Lamichhane, Sarbin Nepal, Anurag Adhikari, Ayusha Poudel, Samata Nepal, and Alok Atreya. "Type 2 Diabetes Mellitus in Nepal from 2000 to 2020: a systematic review and meta-analysis." F1000Research 2021, vol. 10, 543.
  3. Shrestha, Nipun, Shiva Raj Mishra, Saruna Ghimire, Bishal Gyawali, and Suresh Mehata. "Burden of Diabetes and Prediabetes in Nepal: a Systematic Review and Meta-Analysis." Diabetes Therapy 2020, vol. 11, no. 9, 1935-1946.
  4. Battelino, Tadej, Thomas Danne, Richard M. Bergenstal, Stephanie A. Amiel, Roy Beck, Torben Biester, Emanuele Bosi "Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations from the International Consensus on Time in Range." Diabetes Care 2019, vol. 42, no. 8, 1593-1603.
  5. Ferreira, Rafael Oliva Morgado, Talita Trevisan, Eric Pasqualotto, Matheus Pedrotti Chavez, Beatriz Friedrichsen Marques, Rodrigo Nunes Lamounier, and Simone van de Sande-Lee. “Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.” Diabetes Technology & Therapeutics 2024, vol. 26, no. 4, 252–262.
  6. Heinemann, Lutz, and Guido Freckmann. "CGM versus FGM; or, continuous Glucose Monitoring is Not Flash Glucose Monitoring." Journal of Diabetes Science and Technology 2015, vol. 9, no. 5, 947-950.
  7. Barchiesi, Maria Assunta, Armando Calabrese, Roberta Costa, Francesca Di Pillo, Antonio D’Uffizi, Luigi Tiburzi, and Erum Zahid. "Continuous Glucose Monitoring in Type 2 Diabetes: a Systematic Review of Barriers and Opportunities for Care Improvement." International Journal for Quality in Health Care 2025, vol. 37, no. 3, mzaf046.
  8. Anderson, R. R., J. Hu, and J. A. Parrish. "Optical Radiation Transfer in the Human Skin and Applications in Vivo Remittance Spectroscopy." In Bioengineering and the Skin: Based on the Proceedings of the European Society for Dermatological Research Symposium, held at the Welsh National School of Medicine, Cardiff, 19–21 July 1979, 253-265.
  9. Payne, Peter A. "Measurement of Properties and Function of Skin." Clinical Physics and Physiological Measurement 1991, vol. 12, no. 2, 105-129.
  10. Hina, Aminah, and Wala Saadeh. "Noninvasive Blood Glucose Monitoring Systems using Near-Infrared Technology—A review." Sensors 2022, vol. 22, no. 13, 4855.
  11. Saptari, Vidi Alfandi. "A spectroscopic System for Near Infrared Glucose Measurement." PhD diss., Massachusetts Institute of Technology, 2004.
  12. Smith, John L. "The Pursuit of Noninvasive Glucose: Hunting the Deceitful Turkey." Revised and Expanded, copyright (2015).
  13. Ahmad, M., A. M. Kamboh, and A. Khan. "Non-Invasive Blood Glucose Monitoring Using Near-Infrared Spectroscopy." EDN Magazine 2013, 1–6. https://www.edn.com/non-invasive-blood-glucose-monitoring-using-near-infrared-spectroscopy/
  14. Aoyagi, Takuo. "Improvement of the Ear-Piece Oximeter." In 13th Annual Meeting of Japanese Soc. Med. Electronics and Biomed. Engr., 1974, 90-91.
  15. Leppänen, Timo, Samu Kainulainen, Henri Korkalainen, Saara Sillanmäki, Antti Kulkas, Juha Töyräs, and Sami Nikkonen. "Pulse Oximetry: the Working Principle, Signal Formation, and Applications." In Advances in the Diagnosis and Treatment of Sleep Apnea: Filling the Gap Between Physicians and Engineers, Cham: Springer International Publishing, 2022, 205-218.
  16. Maeda, Yuka, Masaki Sekine, and Toshiyo Tamura. "The Advantages of Wearable Green Reflected Photoplethysmography." Journal of medical systems 2011, vol. 35, no. 5, 829-834.
  17. Matsumura, Kenta, Peter Rolfe, Jihyoung Lee, and Takehiro Yamakoshi. “iPhone 4s Photoplethysmography: Which Light Color Yields the Most Accurate Heart Rate and Normalized Pulse Volume Using the iPhysioMeter Application in the Presence of Motion Artifact?” PLoS ONE 2014, vol. 9, no. 3, e91205.
  18. Lawand, Komal, Mahesh Parihar, and Shital N. Patil. "Design and Development of Infrared LED based Non Invasive Blood Glucometer." In 2015 Annual IEEE India conference (INDICON), IEEE, 2015, 1-6.