Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Abstract

As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm.