Biomechanical Characteristics of Taekwondo Athletes' Horizontal Kick Technique Based on Machine Learning Algorithm

Taekwondo competitors frequently use the horizontal kick, because it is quick and simple to demonstrate, and it helps them place higher in the competition. This paper investigates artificial neural networks in machine learning algorithms to extract reliable information from the complex competition training process. This essay examines the biomechanical aspects of taekwondo practitioners' horizontal kicks and offers insightful conclusions. A general introduction to machine learning algorithms is given in this article. It combines machine learning algorithms with the biomechanical characteristics analysis of taekwondo athletes' horizontal kick technical movements to analyze the biomechanical aspects of the sport's athletes' horizontal kick technical movements. According to this paper's experimental methodology, the accuracy recognition rates of using neural networks to distinguish between the three stages of taekwondo horizontal kick technical movements are all above 75%, which is within the acceptable range. In contrast to the experimental procedure and measurement data using traditional biomechanical characteristics analysis, the experimental results in this paper demonstrate that the biomechanical characteristics of taekwondo athletes' horizontal kick technical movements based on machine learning algorithms are more abundant.