Applying Deep Learning to Track Food Consumption and Human Activity for Non-intrusive Blood Glucose Monitoring

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.