Smartphone-based laser glucometer for non-invasive measurement of glucose level of diabetic patients

• Main Authors: Tamrin, K. F., Adilah, A.N., Hamdi, M. M., Rudiyanto, P. J.
• Format: Article
• Language: English
• Published: Majmuah Enterprise 2018
• Subjects: QC Physics; R Medicine (General)
• Online Access: http://ir.unimas.my/id/eprint/21952/3/Smartphone-based%20laser%20glucometer%20for%20non-invasive%20measurement%20%28abstract%29.pdf; http://ir.unimas.my/id/eprint/21952/; https://www.borneojournal.com/

Self-management of blood glucose level is crucial to the health of the body. Typically, a glucose meter (or glucometer) is used to measure the approximate concentration of glucose in the blood. Initially, a blood sample is acquired by finger pricking using either a manual- or automatic- lancet. This is followed by applying the blood drop to the glucostrip which is then fed to a glucometer. However, due to slow wound healing process in diabetic patient as compared to healthy individual, this conventional sampling process would expose patients’ fingers into unhealed wound and bacterial infection. More importantly, two clear limitations of invasive blood sampling are its painful process and cost of acquiring the instrument, lancet and glucostrip. It becomes even more expensive as diabetic in the state of a chronic condition, which unavoidably requires constant blood glucose monitoring. Thus, the main objective of this research is to design and develop a non-invasive optical measuring technique to automatically and painlessly measure the glucose level in the body using a custom-made device consisting of a laser pointer and a smartphone. Based on Snell’s law, glucose level in the urine as a function of refractive index is correlated actual reading measured using the conventional glucometer. Images captured by the smartphone is analysed using a Matlab software to determine the refractive index of the urine sample. Calibration of the system is performed by correlating the results with that of the glucometer. Both results compare well with R-squared value of 92.9%. The system can be potentially used for remote health and patient monitoring.