Data Processing in Multisensory Systems for Noninvasive Glucose Monitoring
( Pp. 51-60)
More about authors
Pozhar Kirill V.
Cand. Sci. (Eng.), Associate Professor; associate professor, Institute of Biomedical Systems
National Research University of Electronic Technology (MIET)
Zelenograd, Moscow, Russian Federation Mikhailov Mikhail O. senior lecturer, Institute of Biomedical Systems; National Research University of Electronic Technology (MIET); Zelenograd, Moscow, Russian Federation
National Research University of Electronic Technology (MIET)
Zelenograd, Moscow, Russian Federation Mikhailov Mikhail O. senior lecturer, Institute of Biomedical Systems; National Research University of Electronic Technology (MIET); Zelenograd, Moscow, Russian Federation
Abstract:
Objective. This paper proposes to investigate the challenges and develop methods for data processing in the development of a multisensor system for noninvasive blood glucose monitoring, integrating near-infrared diffuse scattering spectroscopy, impedance spectroscopy, and precision thermometry in the context of a significant data flow that is incompatible with the limited resources of computers and communication channels of portable devices and the low accuracy of each of the listed methods individually. Methods. To address the problem of working with large data streams generated by measuring sensors, a hierarchical serial-parallel data processing structure is proposed. This structure includes data decimation, outlier detection and removal, filtering, and calculation using an approximate physically based mathematical model on the sensor microcontrollers. This structure also includes parametric identification of the Randles circuit using the complex nonlinear least squares method and the construction of a multivariate regression model based on gradient boosting on an external device. Results. The proposed approach is shown to reduce the initial data stream of the optical subsystem by several orders of magnitude. A conclusion is drawn regarding the fundamental computational feasibility of a portable multisensory noninvasive glucose monitoring system based on the proposed architecture.
How to Cite:
Pozhar K.V. and Mikhailov M.O. Data processing in multisensory systems for noninvasive glucose monitoring. Computational Nanotechnology. 13, 2 (2026), 51–60. DOI: 10.33693/2313-223X-2026-13-2-51-60. EDN: ZNDSRJ
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Randles J.E.B. Kinetics of rapid electrode reactions. Discussions of the Faraday Society. 1947. Vol. 1. Pp. 11–19. DOI: 10.1039/DF9470100011.
Sanai F., Sahid A.S., Huvanandana J. et al. Evaluation of a continuous blood glucose monitor: a novel and non-invasive wearable using bioimpedance technology. Journal of Diabetes Science and Technology. 2023. Vol. 17. No. 2. Pp. 336–344. DOI: 10.1177/19322968211054110.
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Todaro B., Begarani F., Sartori F., Luin S. Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management? Frontiers in Chemistry. 2022. Vol. 10 (2022). Art. 994272. DOI: 10.3389/fchem.2022.994272.
Pozhar K.V., Chuprakov D.A. Problems and methods of blood glucose control systems automation. Instruments and systems. Monitoring, control, and diagnostics. 2025. No. 12. Pp. 11–18. (In Rus.). DOI: 10.25791/pribor.12.2025.1635. EDN: UBRALV.
Cobelli C., Kovatchev B. Developing the UVA/Padova type 1 diabetes simulator: modeling, validation, refinements, and utility. Journal of Diabetes Science and Technology. 2023. Vol. 17. No. 6. Pp. 1493–1505. DOI: 10.1177/19322968231195081.
Davison N.B., Gaffney C.J., Kerns J.G., Zhuang Q.D. Recent progress and perspectives on non-invasive glucose sensors. Diabetology. 2022. Vol. 3. No. 1. Pp. 56–71. DOI: 10.3390/diabetology3010005.
El Dimassi S., Gautier J., Zalc V. et al. Body water volume estimation using bio impedance analysis: Where are we? IRBM. 2024. Vol. 45. No. 3. Art. 100839. DOI: 10.1016/j.irbm.2024.100839.
Foo J.Y., Wilson S.J. A computational system to optimize noise rejection in photoplethysmography signals during motion or poor perfusion states. Medical & Biological Engineering & Computing. 2006. Vol. 44. No. 1–2. Pp. 140–145. DOI: 10.1007/s11517-005-0008-y.
Gorlachuk P.V., Ivanov A.V., Kurnosov V.D. et al. Simulation of power-current characteristics of high-power semiconductor lasers emitting in the range 1.5–1.55 μm. Quantum Electronics. 2014. Vol. 44. No. 2. Pp. 149–156. DOI: 10.1070/QE2014v044n02ABEH015251.
Harvie A.J., de Mello J.C. OLIA: An open-source digital lock-in amplifier. Frontiers in Sensors. 2023. Vol. 4. Art. 1102176. DOI: 10.3389/fsens.2023.1102176.
Hillier T.A., Abbott R.D., Barrett E.J. Hyponatremia: Evaluating the correction factor for hyperglycemia. The American Journal of Medicine. 1999. Vol. 106. No. 4. Pp. 399–403. DOI: 10.1016/S0002-9343(99)00055-8.
Hina A., Saadeh W. Noninvasive blood glucose monitoring systems using near-infrared technology. A review. Sensors. 2022. Vol. 22. No. 13. Art. 4855. Pp. 1–22. DOI: 10.3390/s22134855.
Huang J., Zhang Y., Wu J. Review of non-invasive continuous glucose monitoring based on impedance spectroscopy. Sensors and Actuators A: Physical. 2020. Vol. 311. Art. 112103. DOI: 10.1016/j.sna.2020.112103.
Hughes M.S., Levy C.J. The future of automated insulin delivery systems. Endocrine Practice. 2025. Vol. 31. No. 9. Pp. 1162–1170. DOI: 10.1016/j.eprac.2025.05.752.
Ivorra A., Genescà M., Sola A. et al. Bioimpedance dispersion width as a parameter to monitor living tissues. Physiological Measurement. 2005. Vol. 26. No. 2. Pp. S165–S173. DOI: 10.1088/0967-3334/26/2/016.
Klonoff D.C. The need for clinical accuracy guidelines for blood glucose monitors. Journal of Diabetes Science and Technology. 2012. Vol. 6. No. 1. Pp. 1–4. DOI: 10.1177/193229681200600101.
Kovatchev B.P., Cox D.J., Gonder-Frederick L.A., Clarke W. Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care. 1997. Vol. 20. No. 11. Pp. 1655–1658. DOI: 10.2337/diacare.20.11.1655.
Lin T., Gal A., Mayzel Y. et al. Non-invasive glucose monitoring: a review of challenges and recent advances. Current Trends in Biomedical Engineering & Biosciences. 2017. Vol. 6. No. 5. Pp. 1–8. Art. 555696. DOI: 10.19080/CTBEB.2017.06.555696.
Litinskaia E.L., Mikhailov M.O., Polyakova E.A., Pozhar K.V. Modeling of diffuse reflectance near-infrared spectroscopy based system for noninvasive tissue glucose level measuring. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. Pp. 2818–2822. DOI: 10.1109/ElConRus51938.2021.9396609. EDN: DXNWPI.
Lu S., Zhao H., Ju K. et al. Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? Journal of Clinical Monitoring and Computing. 2008. Vol. 22. No. 1. Pp. 23–29. DOI: 10.1007/s10877-007-9103-y.
Macdonald J.R., Schoonman J., Lehnen A.P. Applicability and power of complex nonlinear least squares for the analysis of impedance and admittance data. Journal of Electroanalytical Chemistry and Interfacial Electrochemistry. 1982. Vol. 131. Pp. 77–95. DOI: 10.1016/0022-0728(82)87062-9.
Ollmar S., Schrunder A.F., Birgersson U. et al. A battery-less implantable glucose sensor based on electrical impedance spectroscopy. Scientific Reports. 2023. Vol. 13. No. 1. Art. 18122. DOI: 10.1038/s41598-023-45154-8.
Patterson J.A., Yang G.-Z. Ratiometric artifact reduction in low power reflective photoplethysmography. IEEE Transactions on Biomedical Circuits and Systems. 2011. Vol. 5. No. 4. Pp. 330–338. DOI: 10.1109/TBCAS.2011.2161304.
Pozhar K.V., Mikhailov M.O., Polyakova E.A. et al. Noninvasive measurement of the glucose level in biological media based on diffuse reflectance spectroscopy. Biomedical Engineering. 2021. Vol. 55. No. 2. Pp. 84–88. DOI: 10.1007/s10527-021-10076-0. EDN: MZOLTT.
Pozhar K.V., Mikhailov M.O., Polyakova E.A., Litinskaia E.L. Diffuse reflectance photometric system for noninvasive blood glucose control. Journal of Physics: Conference Series. 2021. Vol. 2091. No. 1. P. 012014. DOI: 10.1088/1742-6596/2091/1/012014. EDN: BGSCAL.
Randles J.E.B. Kinetics of rapid electrode reactions. Discussions of the Faraday Society. 1947. Vol. 1. Pp. 11–19. DOI: 10.1039/DF9470100011.
Sanai F., Sahid A.S., Huvanandana J. et al. Evaluation of a continuous blood glucose monitor: a novel and non-invasive wearable using bioimpedance technology. Journal of Diabetes Science and Technology. 2023. Vol. 17. No. 2. Pp. 336–344. DOI: 10.1177/19322968211054110.
Stephanie Y.H.K., Norazan M.K. Non-invasive blood glucose measurement using temperature based approach. Jurnal Teknologi (Sciences & Engineering). 2013. Vol. 64. No. 3. Pp. 105–110. DOI: 10.11113/JT.V64.2087.
Todaro B., Begarani F., Sartori F., Luin S. Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management? Frontiers in Chemistry. 2022. Vol. 10 (2022). Art. 994272. DOI: 10.3389/fchem.2022.994272.
Keywords:
multisensory, data processing, continuous glucose monitoring, insulin therapy control, noninvasive monitoring.
