Supportive Glucose Sensing Mobile Application to Improve the Accuracy of Continuous Glucose Monitors

Abstract

An insulin pump can be programmed to continuously deliver accurate amounts of insulin to diabetic patients. A continuous glucose monitor (CGM) which provides continuous patient glucose levels, needs to be calibrated at least every 6 hours. This paper provides an overview of the software and hardware requirements to increase the calibration duration with a high level of accuracy in an open source Artificial Pancreas platform. On the software level, it uses a smartphone camera to capture the food intake, and a smartphone sensor and positioning system to capture the patient movements.  The system maps three months’ worth of data points to the actual glucose level generated by the CGM. It then generates the probability of the estimated insulin needed based on the recorded movements and food intake activities for the patient. The logged data is used as the training data set. Using Bayes’ analysis, the generated probability that is based on the patient activities is used as posterior probabilities to the CGM results, which generates a more accurate estimation of the glucose level. On the hardware level, the paper presents a Universal Remote Control and its associated protocol to connect the smartphone with the CGM for information retrieval and with the insulin pump for information dissemination. The information is sent to the insulin pump using a Field Programmable Gate Array (FPGA). For communication, there are two kinds of message frames: Dosage Delivery Frame (DDF) and Acknowledgement frame (ACKF) with a secure layer of encryption.

This work is being sponsored in part by Murray Award for Research and Development for 2013-2014 and TecBridge/Northeastern Pennsylvania Technology Institute (NPTI).