Non-Invasive Glucose Monitoring Using NIRS

Abstract

Blood glucose is the primary energy source of the human body. With the rise in diabetes, a condition that causes atypical blood glucose levels, the need to monitor and maintain a healthy glucose level has become paramount, especially for diabetics, as prolonged high glucose levels can lead to organ degradation and cardiac arrest. The most popular and reliable way to monitor one’s glucose level is the finger-prick method; however, it is both intrusive and costly. Patients might need to prick their fingers multiple times per day, which causes discomfort and without proper hygienic measures, can cause other diseases. Furthermore, the cost of replacing the lancets and strips could quickly add up, becoming an extra financial burden. Our project introduces a novel glucose monitoring solution using NIRs and Machine Learning techniques to overcome these shortcomings. By utilizing the property of glucose to absorb light in the Near Infrared (NIR) region, in conjunction with other parameters that have a high correlation to glucose levels, we have successfully implemented an alternative to the classic finger-pricking method. This thesis delves into the exploration, implementation and evaluation of our approach. This encompasses the hardware implementation, dataset collection, machine learning and the development and deployment of a mobile application. The results of this study demonstrate the potential of NIRs and the strategic application of machine learning methodologies in providing an adaptable solution to blood glucose monitoring without the need for invasive methods. It is anticipated that the findings and methodologies presented will help in advancing the nascent field of NIRs applications as well as promoting more non-invasive alternatives to popular intrusive biomedical methodologies that are reliable and inexpensive.