REVIEW ARTICLE
Evaluation of Non-Invasive Wearable Diabetes Sensors
1
Faculty of Physics, Mathematics and Information Technologies, Department of Applied Informatics, Chechen State Pedagogical University, Russia
Submission date: 2024-06-02
Final revision date: 2024-06-28
Acceptance date: 2024-06-28
Publication date: 2024-06-30
Corresponding author
Piya Muradova
Faculty of Physics, Mathematics and Information Technologies, Department of Applied Informatics, Chechen State Pedagogical University, Russia
Faculty of Physics, Mathematics and Information Technologies, Department of Applied Informatics, Chechen State Pedagogical University, Russia
Sensors and Machine Learning Applications 2024;3(2)
KEYWORDS
TOPICS
ABSTRACT
Diabetes management has increasingly emphasised the need for continuous glucose monitoring (CGM) systems, promoting advancements in non-invasive wearable diabetes sensors. This comprehensive review explores the latest developments in this field, focusing on the types, technological advancements, and challenges associated with these devices. The review is structured into distinct sections that examine the current state and future directions of optical, electromagnetic, and transdermal sensors, along with emerging technologies in non-invasive glucose monitoring. The review examines the technological enhancements that have improved sensor accuracy and precision, ergonomic designs for increased comfort, and advancements in data analytics that integrate machine learning for predictive analytics. Comparison of the major challenges such as maintaining sensor accuracy and reliability, ensuring user compliance, safeguarding data privacy, and overcoming cost-related barriers are explored. Furthermore, the paper discusses the promising future directions like the use of innovative materials, the integration of artificial intelligence, and the importance of regulatory and ethical considerations in the development of CGM technologies. This review not only underscores the significant progress made in the field but also highlights the critical need for ongoing research to overcome existing limitations. The implications of these technologies extend beyond individual patient management to broader applications in healthcare and lifestyle monitoring, promoting crucial shift towards more personalised and accessible diabetes management solutions.
REFERENCES (40)
1.
American Diabetes Association (2020) 'Classification and diagnosis of diabetes: Standards of medical care in diabetes—2020', Diabetes Care, 43(Supplement 1), pp. S14-S31.
2.
Bandodkar, A.J. and Wang, J. (2014) 'Non-invasive wearable electrochemical sensors: A review', Trends in Biotechnology, 32(7), pp. 363-371. doi: 10.1016/j.tibtech.2014.04.005.
3.
Chen, L., et al. (2021) 'Enhancing non-invasive glucose sensor performance with nanomaterials', Sensors and Actuators B: Chemical, 335, pp. 123-134. doi: 10.1016/j.snb.2021.129700.
4.
Chen, X., et al. (2021) 'Machine learning algorithms for NIR spectroscopy-based glucose sensor calibration', Biosensors and Bioelectronics, 187, pp. 113-123. doi: 10.1016/j.bios.2021.113290.
5.
Chen, X., Zhang, L., Zhao, Y., Wang, Z. and Shen, Y. (2021) 'Investigating the effect of skin properties on the accuracy of NIR spectroscopy-based glucose sensors', Journal of Biomedical Optics, 26(2), 025001. doi: 10.1117/1.JBO.26.2.025001.
6.
Chen, X., Zhao, Y. and Shen, Y. (2021) 'Risk of data breaches in wearable glucose sensors: Enhancing data security', Journal of Diabetes Science and Technology, 15(6), pp. 1452-1460. doi: 10.1177/19322968211003657.
7.
Chen, Y., et al. (2023) 'Flexible and stretchable electrochemical sensors for glucose monitoring', Advanced Functional Materials, 33(6), pp. 456-467. doi: 10.1002/adfm.202103771.
8.
Cheng, Y., et al. (2022) 'A touch-actuated glucose sensor fully integrated with microneedle array and reverse iontophoresis for diabetes monitoring', Biosensors and Bioelectronics, 203(November 2021), 114026. doi: 10.1016/j.bios.2022.114026.
9.
Deng, M., et al. (2022) 'Wearable fluorescent contact lenses for monitoring glucose via a smartphone', Sensors and Actuators B: Chemical, 352(P2), 131067. doi: 10.1016/j.snb.2021.131067.
10.
Diabetes Control and Complications Trial Research Group (1993) 'The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus', The New England Journal of Medicine, 329(14), pp. 977-986. doi: 10.1056/NEJM199309303291401.
11.
Gao, W., Emaminejad, S., Nyein, H.Y.Y., Challa, S., Chen, K., Peck, A., Fahad, H.M., Ota, H., Shiraki, H., Kiriya, D., Lien, D.H., Brooks, G.A., Davis, R.W. and Javey, A. (2016) 'Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis', Nature, 529(7587), pp. 509-514. doi: 10.1038/nature16521.
12.
Güntner, A.T., et al. (2022) 'Monitoring rapid metabolic changes in health and type-1 diabetes with breath acetone sensors', Sensors and Actuators B: Chemical, 367(June), 132182. doi: 10.1016/j.snb.2021.132182.
13.
Heinemann, L. (2018) 'Finger pricking and pain: A never ending story', Journal of Diabetes Science and Technology, 12(3), pp. 702-708. doi: 10.1177/1932296818768423.
14.
Heinemann, L. (2020) 'Continuous glucose monitoring and its implications for the treatment of patients with diabetes', The Lancet Diabetes & Endocrinology, 8(3), pp. 179-190. doi: 10.1016/S2213-8587(19)30311-0.
15.
Hirten, R.P., et al. (2024) 'Longitudinal assessment of sweat-based TNF-alpha in inflammatory bowel disease using a wearable device', Scientific Reports, 14(1), pp. 1-9. doi: 10.1038/s41598-023-36410-0.
16.
Ibrahim, N.F.A., et al. (2022) 'A comprehensive review of the recent developments in wearable sweat-sensing devices', Sensors, 22(19), 7670. doi: 10.3390/s22197670.
17.
Kazemi, N., et al. (2023) 'In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities', Biosensors and Bioelectronics, 241(July), 115668. doi: 10.1016/j.bios.2021.115668.
18.
Kim, D., et al. (2021) 'Mobile apps for real-time glucose level monitoring using NIR spectroscopy-based sensors', mHealth, 7, pp. 78-89. doi: 10.21037/mhealth-20-120.
19.
Kim, J., Campbell, A.S. and Wang, J. (2021) 'Wearable non-invasive glucose sensors: A review of recent advancements', Biosensors and Bioelectronics, 180, 113044. doi: 10.1016/j.bios.2021.113044.
20.
Kim, J., Campbell, A.S. and Wang, J. (2022) 'Wearable biosensors for healthcare monitoring', Nature Biotechnology, 37(4), pp. 389-406. doi: 10.1038/s41587-019-0045-y.
21.
Kim, S., et al. (2022) 'Calibration techniques for dielectric spectroscopy-based glucose sensors', IEEE Transactions on Biomedical Engineering, 69(8), pp. 2345-2356. doi: 10.1109/TBME.2022.3150432.
22.
Kim, S., et al. (2022) 'Influence of environmental factors on dielectric spectroscopy-based glucose sensors', Sensors and Actuators B: Chemical, 345, 130333. doi: 10.1016/j.snb.2021.130333.
23.
Lee, H., et al. (2023) 'Flexible and stretchable electrochemical sensors for glucose monitoring: Enhancing comfort and usability', Biosensors and Bioelectronics, 188, 113333. doi: 10.1016/j.bios.2021.113333.
24.
Lee, H., et al. (2023) 'Statistical methods for calibrating reverse iontophoresis-based glucose sensors', Analytical Chemistry, 95(2), pp. 567-578. doi: 10.1021/acs.analchem.1c04301.
25.
Lee, H., Choi, T.K., et al. (2023) 'A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy', Nature Nanotechnology, 12(9), pp. 866-874. doi: 10.1038/nnano.2017.97.
26.
Lee, I., et al. (2021) 'Continuous glucose monitoring systems - Current status and future perspectives of the flagship technologies in biosensor research', Biosensors and Bioelectronics, 181(June 2020), 113054. doi: 10.1016/j.bios.2020.113054.
27.
Lee, M., et al. (2022) 'Integration of reverse iontophoresis-based sensors with cloud platforms', Digital Health, 8, 2055207621102345. doi: 10.1177/20552076211023455.
28.
Mahato, K. and Wang, J. (2021) 'Electrochemical sensors: From the bench to the skin', Sensors and Actuators B: Chemical, 344(April), 130178. doi: 10.1016/j.snb.2021.130178.
29.
Mahato, R. and Wang, C. (2021) 'Advanced materials for non-invasive glucose sensors', Advanced Healthcare Materials, 10(6), 2001187. doi: 10.1002/adhm.202001187.
30.
Mao, Y., et al. (2022) 'Surface-enhanced Raman scattering sensors for non-invasive glucose detection in saliva', Sensors and Actuators B: Chemical, 352(P2),.
31.
Noura, Z., et al. (2022) 'Wearable healthcare monitoring based on a microfluidic electrochemical integrated device for sensing glucose in natural sweat', Sensors, 22(22). doi: 10.3390/s22228971.
32.
Omer, A.E., et al. (2020) 'Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: novel design utilizing a four-cell CSRR hexagonal configuration', Scientific Reports, 10(1), pp. 1-20. doi: 10.1038/s41598-020-74348-0.
33.
Promphet, N., et al. (2020) 'Cotton thread-based wearable sensor for non-invasive simultaneous diagnosis of diabetes and kidney failure', Sensors and Actuators B: Chemical, 321(June), 128549. doi: 10.1016/j.snb.2020.128549.
34.
Sankhala, D., et al. (2022) 'A machine learning-based on-demand sweat glucose reporting platform', Scientific Reports, 12(1), pp. 1-11. doi:10.1038/s41598-022-06434-x.
35.
Shamili, C., et al. (2024) 'All-printed wearable biosensor based on MWCNT-iron oxide nanocomposite ink for physiological level detection of glucose in human sweat', Biosensors and Bioelectronics, 258(January), 116358. doi: 10.1016/j.bios.2023.116358.
36.
Shen, Y., et al. (2021) 'Physiological variations and their impact on the performance of reverse iontophoresis-based glucose sensors', Journal of Biomedical Optics, 26(4), 045003. doi: 10.1117/1.JBO.26.4.045003.
37.
Sohail, M., et al. (2023) 'Machine learning integration with wearable sensors for diabetes management', Artificial Intelligence in Medicine, 127, 102156. doi: 10.1016/j.artmed.2022.102156.
38.
Tang, L., et al. (2020) 'Non-invasive blood glucose monitoring technology: A review', Sensors, 20(23), 6884. doi: 10.3390/s20236884.
39.
Vlahoman, P., Cortes, M. and Emadi, A. (2020) 'Accuracy comparison of GlucoTrack to self-monitoring blood glucose devices', Journal of Diabetes Science and Technology, 14(2), pp. 330-335. doi: 10.1177/1932296820903107.
40.
Voss, A., et al. (2022) 'Detection of liver dysfunction using a wearable electronic nose system based on semiconductor metal oxide sensors', Biosensors, 12(6), 407. doi: 10.3390/bios12060407.
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