Effectiveness of Non-Invasive Sensor-Based Tools for Blood Glucose Detection
Main Article Content
Abstract
Background: Monitoring blood glucose levels is one of the main pillars of diabetes management to prevent complications and reduce the risk of morbidity and mortality. Today's blood glucose monitoring is a non-invasive method that offers speed, accuracy, and painless convenience. Referring to this need, this study aims to demonstrate the effectiveness of non-invasive sensor-based detection devices in checking blood glucose levels in order to provide a more comfortable and efficient alternative for diabetes patients.
Methods: This study developed a non-invasive glucometer using the latest and smaller version of Arduino Uno and tested it on 20 samples, consisting of 10 diabetes mellitus patients and 10 with normal blood glucose. The test was carried out by comparing the measurement results from the non-invasive device and the standard GCU Easy Touch 3-in-1 device to determine the accuracy of the device. The tool-testing method uses sensitivity, specificity, and accuracy.
Results: This non-invasive measuring tool is more effective when used to measure patients with diabetes mellitus. This device shows an error rate of 9.21%, a sensitivity of 80%, and a specificity of 50%. Meanwhile, the overall measurement accuracy, calculated at 83.3%, reinforces the tool's effectiveness in providing reliable results.
Conclusion: This device has the potential to be a convenient and painless method of blood glucose monitoring for diabetic patients. However, further development is needed to improve the development of machine learning-based algorithms to process sensor data so that tools can identify unique patterns from each individual and provide more accurate results.
Article Details
References
Asada, H. H., Shaltis, P., Reisner, A., Sokwoo Rhee, & Hutchinson, R. C. (2003). Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine, 22(3), 28–40. https://doi.org/10.1109/MEMB.2003.1213624
Bergloff, A., Stratton, E., & Briggs Early, K. (2019). A Cross-Sectional Pilot Survey of Rural Clinic Attitudes and Proficiency with Insulin Pumps and Continuous Glucose Monitoring Devices. Diabetes Technology & Therapeutics, 21(11), 665–670. https://doi.org/10.1089/dia.2019.0161
Brown, D., & Lee, E. (2021). Optoelectronic Sensing Techniques for Glucose Monitoring: A Review. Journal of Sensors and Actuators B: Chemical.
Chandrasekaran, V., & Swamy, S. N. (2019). Design and implementation of non-invasive blood glucose monitoring system using infrared light. Proceedings of the International Conference on Communication and Electronics Systems.
Chung, J., So, H., Choi, & Wong, T. K. S. (2012). Recent advances in noninvasive glucose monitoring. Medical Devices: Evidence and Research, 45. https://doi.org/10.2147/MDER.S28134
Hadar, E., Chen, R., Toledano, Y., Tenenbaum-Gavish, K., Atzmon, Y., & Hod, M. (2019). Noninvasive, continuous, real-time glucose measurements compared to reference laboratory venous plasma glucose values. The Journal of Maternal-Fetal & Neonatal Medicine, 32(20), 3393–3400. https://doi.org/10.1080/14767058.2018.1463987
Houlden, R. L. (2018). Introduction. Canadian Journal of Diabetes, 42, S1–S5. https://doi.org/10.1016/j.jcjd.2017.10.001
Huang, Y., Chen, X., & Shao, S. (2018). Application of non-invasive blood glucose monitoring in healthcare: Methods and challenges. Journal of Sensors.
IDF. (2021). IDF Diabetes Atlas. International Diabetes Federation.
Kurniawan, Y., & Utomo, S. (2018). Design of non-invasive glucose meter using photodiode and Arduino Uno. Journal of Electrical Engineering and Computer Science, 10(2).
Lin, T., Mayzel, Y., & Bahartan, K. (2018). The accuracy of a non-invasive glucose monitoring device does not depend on clinical characteristics of people with type 2 diabetes mellitus. Journal of Drug Assessment, 7(1), 1–7. https://doi.org/10.1080/21556660.2018.1423987
MDPI. (2023). Non-Invasive Glucose Sensing Technologies and Products. Retrieved from https://www.mdpi.com/1424-8220/23/22/9130. . Https://Www.Mdpi.Com.
Melheim, L., Grandin, L., Persson, P.-O., Billström, K., Stos-Gale, Z., Ling, J., Williams, A., Angelini, I., Canovaro, C., Hjärthner-Holdar, E., & Kristiansen, K. (2018). Moving metals III: Possible origins for copper in Bronze Age Denmark based on lead isotopes and geochemistry. Journal of Archaeological Science, 96, 85–105. https://doi.org/10.1016/j.jas.2018.04.003
PERKENI. (2021). Pedoman Pemantauan gula darah mandiri. Endokrinologi Indonesia, 1–36. In Endokrinologi Indonesia.
Pratomo, F. D., & Sugiyama, M. (2019a). Development of a non-invasive glucose monitoring system using near-infrared spectroscopy and Arduino technology. Journal of Medical Engineering & Technology, 43(3).
Pratomo, F. D., & Sugiyama, M. (2019b). Development of a non-invasive glucose monitoring system using near-infrared spectroscopy and Arduino technology. Journal of Medical Engineering & Technology, 43(3).
Punthakee, Z., Goldenberg, R., & Katz, P. (2018). Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome. Canadian Journal of Diabetes, 42, S10–S15. https://doi.org/10.1016/j.jcjd.2017.10.003
Sanai, F., Sahid, A. S., Huvanandana, J., Spoa, S., Boyle, L. H., Hribar, J., Wang, D. T.-Y., Kwan, B., Colagiuri, S., Cox, S. J., & Telfer, T. J. (2023). Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology. Journal of Diabetes Science and Technology, 17(2), 336–344. https://doi.org/10.1177/19322968211054110
Sangeetha, V., & Mahesh, T. (2015). Blood glucose measurement using non-invasive method. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 04(05), 4047–4062. https://doi.org/10.15662/ijareeie.2015.0405036
Sawaryn, B., Klaassen, M., Zwart, H., & Veltink, P. (2021). Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System. Sensors Journal, 21(17).
Soelistijo, S. (2021). Pedoman Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2 Dewasa di Indonesia 2021. Global Initiative for Asthma. www.ginasthma.org.
Srichan, C., Srichan, W., Danvirutai, P., Ritsongmuang, C., Sharma, A., & Anutrakulchai, S. (2022). Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features. Scientific Reports, 12(1), 1769. https://doi.org/10.1038/s41598-022-05570-8
Stefanovski, D., Vellanki, P., Smiley-Byrd, D. D., Umpierrez, G. E., & Boston, R. C. (2020). Population insulin sensitivity from sparsely sampled oral glucose tolerance tests. Metabolism, 110, 154298. https://doi.org/10.1016/j.metabol.2020.154298
Sugiyono. (2017). Metode Penelitian Kuantitatif & Kualitatif. Alfabeta.
Suyono, & Hambali. (2020). Pengembangan alat ukur gula darah non-invasive menggunakan photodiode dan mikrokontroler Arduino Uno. Jurnal Teknologi Kesehatan, 8(1).
Wu, J., Liu, Y., Yin, H., & Guo, M. (2023). A new generation of sensors for non-invasive blood glucose monitoring. American Journal of Translational Research, 15(6).