Smart Bolus Estimation Taking into Account the Amount of Insulin on Board

Abstract

Self-management blood glucose (SMBG) and bolus calculations are pivotal components of evidence-based standard of care for young diabetics receiving multiple daily insulin injections. This paper aims at developing a smart bolus estimator that takes into account the amount of insulin on board (IoB), i.e. Insulin remaining in the patient's body, to reduce fear of hypoglycemia and achieve goals of glycemic control. Design of the proposed bolus estimator follows the feed-forward multi-perceptron artificial neural network (ANN) with an input represents the time shift from last injection of quick acting (QA) insulin. The network output represents the amount of IoB. Calculation of the bolus required for a meal/snack is then carried out normally deducing the estimated IoB and thus the possibility of excess insulin administration and the risk of hypoglycemia can be averted. A functional prototype of the proposed system is developed and tested successfully on various mobile devices (i.e smartphones and tablet computers).