Extending an ontology-based personalized dietary recommendation for weightlifting with biomechanical knowledge

Abstract

On olympic weightlifting, just like on any high level competition or sport athletes are very competitive. World records are being beaten over and over and the winners are decided by details, that until recently were unnoted by the athletes themselves and their managers. The focus of this dissertation is the development of a system to help olympic weightlifting athlete’s to improve their performance and prevent injuries from the biomechanical analysis during the athlete’s training sessions. This dissertation presents a biomechanical ontology using a knowledgebased framework. Over this ontology will be applied a rule-based knowledge specific from olympic weightlifting that relates athlete’s body positions and angles that are collected using an external system during the athlete’s training sessions. From the inferred rules the system can provide information to the athletes and their managers if the athlete is doing the exercise correctly and at same time give some help to prevent injuries. The system includes a database that saves the athlete’s information and all data that is collected during the training sessions. It can be useful to verify the athlete progress comparing the actual and previous data. This work is focused on olympic weightlifting, however it can be used for any kind of sport just by doing some adaptations. This dissertation is a complement of another work focused on nutrition and menus recommendation for weightlifting.