Abstract:
In the modern world, professionals of diverse industrial sectors have severely become victims of
overweight and obese conditions which can be minimized by having proper dietary plans, physical
activities, and minimizing alcohol-based relaxation. However, most of the exercise plans provided by
fitness applications currently available for usage are not personalized and general exercises are given
for every individual. In this research context, individuals are guided by recommending suitable
exercises with exercise frequency, exercise environment, and unique time period to perform
according to body parameters. According to domain experts, fitness plans highly depend on
individual characteristics. Therefore height, weight, age, sex, diet details, medical history and user
preferences for exercises taken from the front end which is a Tkinter Graphical User Interface. In this
system, food ontology uses these details to calculate the daily calorie intake and extra calorie intake
of the particular individual. Disease extraction using natural language processing techniques,
computed with Python and integrated with the output of Food Ontology which is to be mapped with
the exercise ontological knowledge base along with the predefined rules to match respective exercises
suitable for the particular individual that is compatible with his preferences. Two ontologies for foods
and exercises developed using Protégé 4.3 and data retrieved by running Simple Protocol and
Resource Description Framework Query Language (SPARQL) queries inside the Python code using
the RDFLib module and output is taken and directed to the front end. The entire system developed
with Python 3, where two ontological files of Foods and Exercises are loaded and tested for
consistency using the HermiT reasoner with the aid of Owlready2. The task-based ontology
evaluation approach is performed by addressing the competency questions through the execution of
SPARQL queries. In conclusion, this study provides an approach to integrate two ontologies and a
disease extraction model using Python programming language. Correctness and qualitative
evaluations of the system are verified by the domain experts, and recommendations from the
ontological system are beneficial for physical trainers to improve and validate their manual exercise
recommendations.
Keywords: Exercises; Ontology; Food; Tkinter; Python
