Estimation of Blood Glucose Levels Using a Non- Invasive Infrared-Based Optical Sensor: A Pilot Study

https://doi.org/10.56741/IISTR.esl.001218

Authors

  • Husneni Mukhtar Universitas Telkom
  • Muhammad Mugni Zaelani AIBE Laboratory Universitas Telkom
  • Muhammad Rafy Nasrullah BioSPIN Laboratory Universitas Telkom
  • Hesty Susanti Universitas Telkom

Keywords:

Non-invasive Glucose Monitoring, Infrared Sensor, Pilot Testing, Photodetector, Device Validation

Abstract

Blood glucose measurement is critical to diabetes management and prevents chronic complications such as neuropathy, nephropathy, and retinopathy. However, current methods are invasive, uncomfortable, and costly. Although several non-invasive approaches have been explored, no commercially available device offers a simple, affordable, and user-friendly solution for non-invasive blood glucose estimation, particularly one suitable for self-measurement outside clinical settings. This underscores the need for practical and inclusive alternatives. This study aimed to develop and evaluate a blood glucose estimation device using infrared LEDs to measure light transmittance through the fingertip. The research was conducted in two stages: initial testing using glucose solutions with varying concentrations (0.02, 0.06, 0.10, and 0.20 g/ml) and added red dye (0.05, 0.10, and 0.15 ml) to validate the sensor's response, followed by direct validation on human fingers against commercial blood glucose test strip readings. The results showed a strong positive correlation between sensor output and glucose levels, with a Pearson correlation coefficient of r = 0.995. Using a regression-based calibration model, the system achieved a mean absolute error (MAE) of 1.63 mg/dL, and a root mean square error (RMSE) of 1.72 mg/dL. Cross-validation, such as Bland-Altman analysis and Clarke Error Grid, was conducted to verify model robustness. These preliminary results suggest that the developed system holds strong potential as a simple, affordable, and non-invasive tool for blood glucose self-monitoring, especially in resource-limited settings. However, further validation on larger, more diverse populations is necessary.

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Author Biographies

Husneni Mukhtar, Universitas Telkom

Lecturer and Researcher in Electrical Engineering Study Program, Universitas Telkom

Muhammad Mugni Zaelani, AIBE Laboratory Universitas Telkom

student in Telkom University

Muhammad Rafy Nasrullah, BioSPIN Laboratory Universitas Telkom

student in Telkom University

Hesty Susanti, Universitas Telkom

lecturer in Biomedical Engineering Study Program

References

A. Nawaz, P. Øhlckers, S. Sælid, M. Jacobsen, and Nadeem M., “Review: Non-Invasive Continuous Blood Glucose Measurement Techniques,” Bioinforma. Diabetes, vol. 1, no. 3, pp. 1–27, 2016. DOI: https://doi.org/10.14302/issn.2374-9431.jbd-15-647

M. Gusev et al., “Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability,” J. Sensors, vol. 2020, 2020. DOI: https://doi.org/10.1155/2020/9628281

T. Tian, R. E. Aaron, M. A. Kohn, and D. C. Klonoff, “The Need for a Modern Error Grid for Clinical Accuracy of Blood Glucose Monitors and Continuous Glucose Monitors,” J. Diabetes Sci. Technol., vol. 18, no. 1, pp. 3–9, 2024. DOI: https://doi.org/10.1177/19322968231214281

S. Sengupta, A. Handoo, I. Haq, K. Dahiya, S. Mehta, and M. Kaushik, “Clarke Error Grid Analysis for Performance Evaluation of Glucometers in a Tertiary Care Referral Hospital.,” Indian J. Clin. Biochem., vol. 37, no. 2, pp. 199–205, Apr. 2022. DOI: https://doi.org/10.1007/s12291-021-00971-4

A. Tura, A. Maran, and G. Pacini, “Non-invasive glucose monitoring: Assessment of technologies and devices according to quantitative criteria,” Diabetes Res. Clin. Pract., vol. 77, no. 1, pp. 16–40, 2007. DOI: https://doi.org/10.1016/j.diabres.2006.10.027

S. Q. Shokrekhodaei, Maryamsadat, “Review of Non-Invasive Glucose Sensing Techniques :,” Sensors (Switzerland), p. 1251, 2020. DOI: https://doi.org/10.3390/s20051251

Y. F. Su, S. H. Lin, S. L. Ou, and Y. C. Wang, “Development of simple portable blood glucose measuring devices,” Sensors Mater., vol. 33, no. 6, pp. 1809–1815, 2021. DOI: https://doi.org/10.18494/SAM.2021.3228

O. A. Ekun, G. A. Ogunyemi, A. Azenabor, and O. Akinloye, “A Comparative Analysis of Glucose Oxidase Method and Three Point-Of-Care Measuring Devices for Glucose Determination,” Ife J. Sci., vol. 20, no. 1, pp. 43–49, 2018. DOI: https://doi.org/10.4314/ijs.v20i1.4

S. H. Lee, Y. C. Cho, and Y. Bin Choy, “Noninvasive Self-diagnostic Device for Tear Collection and Glucose Measurement,” Sci. Rep., vol. 9, no. 1, pp. 1–8, 2019. DOI: https://doi.org/10.1038/s41598-019-41066-8

O. S. Khalil, “Non-Invasive Glucose Measurement Technologies: An Update from 1999 to the Dawn of the New Millennium,” Diabetes Technol. Ther., vol. 6, no. 5, pp. 660–697, 2004. DOI: https://doi.org/10.1089/dia.2004.6.660

J. Kim, B. K. Kim, M.-R. Park, H. Cho, and C. Huh, “Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe,” Biosensors, vol. 15, no. 7, p. 406, 2025. DOI: https://doi.org/10.3390/bios15070406

P. Jain, R. Maddila, and A. M. Joshi, “A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model,” Opt. Quantum Electron., vol. 51, no. 2, pp. 1–15, 2019. DOI: https://doi.org/10.1007/s11082-019-1766-3

C. A. Haque, S. Hossain, T. H. Kwon, and K. D. Kim, “Noninvasive in vivo estimation of blood‐glucose concentration by Monte Carlo simulation,” Sensors, vol. 21, no. 14, 2021. DOI: https://doi.org/10.3390/s21144918

C. Trejo-Soto and A. Hernández-Machado, “Normalization of Blood Viscosity According to the Hematocrit and the Shear Rate,” Micromachines, vol. 13, no. 3, pp. 1–20, 2022. DOI: https://doi.org/10.3390/mi13030357

R. Khnouf, D. Karasneh, E. Abdulhay, A. Abdelhay, W. Sheng, and Z. Fan, “Microfluidics-based device for the measurement of blood viscosity and its modeling based on shear rate, temperature, and heparin concentration,” Biomed. Microdevices, vol. 21, Aug. 2019. DOI: https://doi.org/10.1007/s10544-019-0426-5

S. Kim, B. Prasad, and J. Kim, “Alignment of Microbeads Using Spinning Helical Minichannel Cartridge,” J. Korean Soc. Vis., vol. 14, pp. 38–45, Jan. 2016. DOI: https://doi.org/10.5407/JKSV.2016.14.3.038

T. T. Wong and P. Y. Yeh, “Reliable Accuracy Estimates from k-Fold Cross Validation,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 8, pp. 1586–1594, 2020. DOI: https://doi.org/10.1109/TKDE.2019.2912815

S. Bates, T. Hastie, and R. Tibshirani, “Cross-validation: what does it estimate and how well does it do it?,” J. Am. Stat. Assoc., vol. 119, no. 546, pp. 1434–1445, 2024. DOI: https://doi.org/10.1080/01621459.2023.2197686

M. I. Solihin, Y. Shameem, T. Htut, C. K. Ang, and M. Bt Hidayab, “Non-invasive blood glucose estimation using handheld near infra-red device,” Int. J. Recent Technol. Eng., vol. 8, no. 3 Special Issue, pp. 16–19, 2019. DOI: https://doi.org/10.35940/ijrte.C1004.1083S19

C. Dias, S. Kamath, and S. Vidyasagar, “Statistical analysis using Bland-Altman agreement for technical methods of measuring blood glucose,” in 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017, pp. 1–6. DOI: https://doi.org/10.1109/ICEECCOT.2017.8284592

Y. Essack, M. Hoffman, M. Rensburg, J. Van Wyk, C. S. Meyer, and R. Erasmus, “A comparison of five glucometers in South Africa,” J. Endocrinol. Metab. Diabetes South Africa, vol. 14, no. 2, pp. 102–105, 2009. DOI: https://doi.org/10.1080/22201009.2009.10872202

Y. (Joseph) Segman, “Device and Method for Noninvasive Glucose Assessment,” J. Diabetes Sci. Technol., vol. 12, no. 6, pp. 1159–1168, 2018. DOI: https://doi.org/10.1177/1932296818763457

L. N. Bachache, A. Q. Al-Neami, and J. A. Hasan, “Error grid analysis evaluation of noninvasive blood glucose monitoring system of diabetic Covid-19 patients,” Int. J. Nonlinear Anal. Appl., vol. 13, no. 1, pp. 3697–3706, 2022.

V. C. Bavkar and A. A. Shinde, “Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement,” Indian J. Sci. Technol., vol. 14, no. 10, pp. 869–880, 2021. DOI: https://doi.org/10.17485/IJST/v14i10.2187

O. I. Coronado Reyes, A. del C. Téllez Anguiano, J. A. Gutiérrez Gnecchi, L. A. Castro Pimentel, and E. García Rodríguez, “Comparison between mathematical methods to estimate blood glucose levels from ECG signals,” Biosens. Bioelectron. X, vol. 18, no. March, 2024. DOI: https://doi.org/10.1016/j.biosx.2024.100474

Published

2025-08-25

How to Cite

Mukhtar, H., Zaelani, M. M., Nasrullah, M. R., & Susanti, H. (2025). Estimation of Blood Glucose Levels Using a Non- Invasive Infrared-Based Optical Sensor: A Pilot Study. Engineering Science Letter, 4(02), 67–76. https://doi.org/10.56741/IISTR.esl.001218

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