Abstract:
Diabetes management is a very important eld of study that focuses on improving the
lives of peoples living with diabetes. Diabetes Mellitus (DM), a common human condition
characterised by hyperglycemia, includes a number of severe complications. In addition,
hypoglycemia which is the decrease in blood glucose levels is linked to catastrophic brain
failure and death. In this project, we look at a range of relevant research to get a better
understanding of some of the systems and concepts that can help in constructing an
autonomous glucose monitoring system, including deep learning approaches. Then we proceed
to introduce our system that combines non-intrusive Continuous Glucose Monitoring
(CGM) devices with blood glucose forecasting, classi cation of meal images, and human
activity recognition. We apply deep learning to predict the patient's future glucose levels
by combining several models that we have tested. Predictions are made by learning the
patterns of blood glucose changes throughout the day of the diabetic patient with the help
of meal detection and human activity recognition, allowing the system to estimate future
blood glucose levels and warn against hyperglycemia and hypoglycemia even when CGM
device becomes unwearable. We have conducted four experiments, one of which is a comparison
of deep learning models for food classi cation and human activity recognition, and
a couple more experimental results obtained by constructing a glucose forecasting system
that combines our deep learning models.