ABSTRACT
Over the years and with the help of technology, the daily care of type 1 diabetes has been improved significantly. The increased adoption of continuous glucose monitoring, the continuous subcutaneous insulin injection and the accurate behavioral monitoring mHealth solutions have contributed to this phenomenon. In this study we present a mobile application for automated dietary assessment of Mediterranean food images as part of the GlucoseML system. Based on short-term predictive analysis of the glucose trajectory, GlucoseML is a type-1 diabetes self-management system. A computer vision approach is used as main part of the GlucoseML dietary assessment system calculating food carbohydrates, fats and proteins, relying on: (i) a deep learning subsystem for food image classification, and (ii) a 3D food image reconstruction subsystem for the volume estimation of food. The deep learning subsystem achieves 82.4% and 97.5% top-1 and top-5 accuracy, respectively, for food image classification while the subsystem for volume estimation of food achieves a mean absolute percentage error 10.7% for the four main categories of MedGRFood dataset.
