Digital Twin in Sport: From an Idea to Realization

Abstract

A digital twin is a virtual model to reflect a physical object and helps it by making proper decisions. The decision-making process is based on the same input data that the simulated physical object has access to. Due to exploiting artificial intelligence, the decision-making process of the digital twin is more sophisticated than that of the physical object. In this study, the digital twin is applied to the sports training domain, where it addresses those questions that have arisen during the implementation of interval cycling training sessions. Thus, the digital twin runs on a mobile device (i.e., the Raspberry Pi platform), with which a cycle is equipped and demonstrates user-friendliness, robustness, reliability, and accuracy. The interval training sessions are transferred to the mobile device in the form of the domain-specific language EasyTrain, ensuring higher expressive power and ease of use. During the implementation, the digital twin advises the athlete with predicted information obtained by a sophisticated prediction model via a screen. The results of a huge experimental work showed that the difference in the average efficiency of the interval training implementation between the two cyclists that performed the experiments is prominent, as the efficiency of the professional training surpassed 90%, while the amateur training efficiency barely achieved 70%.