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 associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by regulating the insulin infusion rate. An activity recognition model based on convolutional neural networks is also proposed for predicting the patient's current physical activity. Predictions from this model are transformed into a six-level code and are fed as input to the neural network glucose prediction model. Experimental results of the proposed system show good performance in keeping blood glucose levels in the nondiabetic range.

Context Aware Control Systems: An Engineering Applications Perspective

“…The introduction of control system theory in resolving context aware changes of glucose-insulin levels in diabetic patients has enabled great progress for patient comfort, well being and quality of life [74]- [76]. For example, the CACS proposed by [77], showed good performance in maintenance of glucose levels based on human activity recognition. The algorithm uses a CNN for classifying six different human activities direct influencing the glucose dynamics (jogging, walking, moving upstairs, moving downstairs, sitting, and standing), using smartphone accelerometers.…”