In recent years, significant progress has been made in the field of non-invasive medical technologies, particularly in the estimation of glycated hemoglobin (HbA1c) and blood glucose using wearable devices. This article highlights key research findings and publications from 2020 to 2024 that have contributed to this rapidly evolving area. The studies explore various machine learning algorithms, optical sensor technologies, and advanced simulation models, all with the goal of improving diabetes management and health monitoring.
2024
"Design and Validation of a Monte Carlo Method for the Implementation of Noninvasive Wearable Devices for HbA1c Estimation Considering the Skin Effect", Micromachines 2024, 15(9), 1067 (Published: 24 August 2024)
"EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms", Appl. Sci. 2024, 14(4), 1406
2023
"A Comparative Analysis of Various Machine Learning Algorithms to Improve the Accuracy of HbA1c Estimation Using Wrist PPG Data", Sensors 2023, 23(16), 7231
"Noninvasive In Vivo Estimation of HbA1c Based on the Beer–Lambert Model from Photoplethysmogram Using Only Two Wavelengths", Appl. Sci. 2023, 13(6)
"Noninvasive in vivo estimation of HbA1c using Monte Carlo photon propagation simulation: Application of tissue-segmented 3D MRI stacks of fingertip and wrist for wearable systems", Sensors 2023, 23, 540 (Published: 03 January 2023) (IF: 3.847)
2022
"Optical Measurement of Molar Absorption Coefficient of HbA1c: Comparison of Theoretical and Experimental Results", Sensors 2022, 22(21), 8179; (Published: 25 October 2022) (IF: 3.847)
"Noninvasive Quantification of In-Vivo Glycated Hemoglobin Based on Photon Diffusion Theory and Genetic Symbolic Regression Models", IEEE Trans. on Biomedical Engineering, Volume: 69, Issue: 6, pp. 2053–2064, June 2022 (IF: 5.20)
"Machine-Learning-Based Noninvasive In-Vivo Estimation of HbA1c Using Photoplethysmography Signals", Sensors 2022, 22, 2963 (Published: 12 April 2022) (IF: 3.847)
"Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals", Sensors 2022, 22(3), 1175; (Published: 4 February 2022) (IF: 3.847)
2021
"Quantitative Analysis of Different Multi-Wavelength PPG Devices and Methods for Noninvasive In-Vivo Estimation of Glycated Hemoglobin", Appl. Sci. 2021, 11, 6867 (Published: 26 July 2021) (IF: 2.679)
"Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation", Sensors 2021, Volume 21, Issue 14, 4918 (Published: 19 July 2021) (IF: 3.847)
"Derivation and Validation of Gray-Box Models to Estimate Non-Invasive In-vivo Percentage Glycated Hemoglobin using Digital Volume Pulse Waveform", Scientific Reports, Jun. 2021, doi: 10.1038/s41598-021-91527-2 (IF: 4.379)
"Towards Non-invasive Blood Glucose Measurement using Machine Learning: An All-Purpose PPG System Design", Biomedical Signal Processing and Control, Volume 68, July 2021, 102706 (IF: 3.137)
2020
"Development of a Wearable Reflection-Type Pulse Oximeter System to Acquire Clean PPG Signal, and Measure Pulse Rate and SpO2 with and without Finger Motion", Electronics, vol. 9, issue 11, 13 November 2020 (IF: 2.412)
"An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell", Expert Systems with Applications, Volume 149, 1 July 2020 (IF: 6.954)
Paving the Way for Future Healthcare
The research conducted from 2020 to 2024 has significantly advanced the capabilities of wearable devices in healthcare, particularly for non-invasive blood glucose and HbA1c monitoring. With the integration of machine learning algorithms, advanced photon simulation models, and multi-wavelength PPG technology, these innovations are not only improving the accuracy and functionality of health monitoring but also paving the way for more accessible, non-invasive diabetes management solutions.
As these technologies continue to evolve, the potential for wearable devices to transform healthcare delivery, especially for chronic conditions like diabetes, becomes ever more promising.
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In recent years, significant progress has been made in the field of non-invasive medical technologies, particularly in the estimation of glycated hemoglobin (HbA1c) and blood glucose using wearable devices. This article highlights key research findings and publications from 2020 to 2024 that have contributed to this rapidly evolving area. The studies explore various machine learning algorithms, optical sensor technologies, and advanced simulation models, all with the goal of improving diabetes management and health monitoring.
2024
2023
2022
2021
2020
Paving the Way for Future Healthcare
The research conducted from 2020 to 2024 has significantly advanced the capabilities of wearable devices in healthcare, particularly for non-invasive blood glucose and HbA1c monitoring. With the integration of machine learning algorithms, advanced photon simulation models, and multi-wavelength PPG technology, these innovations are not only improving the accuracy and functionality of health monitoring but also paving the way for more accessible, non-invasive diabetes management solutions.
As these technologies continue to evolve, the potential for wearable devices to transform healthcare delivery, especially for chronic conditions like diabetes, becomes ever more promising.