A semantic rules & reasoning based approach for Diet and Exercise management for diabetics

Abstract

Diabetes is a serious chronic disease and balance diet as well as regular exercise are leading important factors for diabetes control. Management of healthy diet and proper exercise involve many decision variables from different domains such as gender, weight, height, age, needed calories and nutrition values, preferences about food and exercise, clinical guidelines and current vital signs etc. We have implemented a semantic rules and reasoning based approach that generates diet and exercise recommendations for diabetes patients. The developed prototype application is named Semantic Healthcare Assistant for Diet and Exercise (SHADE). Individual ontologies are defined for different domains (person, disease, food and exercise) along with SWRL rules and then imported all into an integrated ontology. The integrated ontology semantically generates the recommendations as inferences based on data and rules by using Pellet reasoner. Each generated meal menu is a list of food items along with portion size such that food items are user's preferred and menu is personalized, healthy and balanced diet. Finally, SHADE recommends user's preferred activities as exercises along with duration and intensity.