Towards Automatic Food Prediction During Endurance Sport Competitions

Abstract

Endurance sport events have increasingly been gaining the popularity. Every year, more and more amateur athletes decide to participate in such events. During the race, proper eating is one of the most important components for achieving the good finish time and in this respect also the good place. In this paper, we examine possibility to predict what to eat at the moment. In line with this, machine learning method, i.e., decision tree was used that bases on the current athlete performance, his/her feeling, needs, and even weather. First simulations showed that this method may be suitable for future use in endurance events.