Context-aware system for glycemic control in diabetic patients using neural networks

Abstract

Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associatedwith diabetes management. Over the last few decades, there have been advancements in the computational power ofembedded systems and glucose sensing technologies. These advancements have attracted the attention of researchersaround the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetesbased on neural networks is proposed. These neural networks are used for making predictions based on the clinical data ofa patient. A neural network feedback controller is also designed to provide a glycemic response by regulating the insulininfusion rate. An activity recognition model based on convolutional neural networks is also proposed for predicting thepatient’s current physical activity. Predictions from this model are transformed into a six-level code and are fed as inputto the neural network glucose prediction model. Experimental results of the proposed system show good performance inkeeping blood glucose levels in the nondiabetic range.