The Effectiveness of Adaptive Algorithms Based on Deep Learning in Acquiring the Bar Flight Skill on the Parallel Bars Apparatus in Men’s Artistic Gymnastics

Abstract

The researcher, through this study, aims to highlight the importance of transforming self-assessment into precise quantitative analysis (with angular/temporal accuracy) to detect hidden errors and person allies the model for each athlete through adaptive algorithms that integrate individual characteristics (such as physical measurements and performance patterns). This, in turn, enhances the effectiveness of correction and reduces the risk of injury. Furthermore, the study seeks to enable real-time feedback during training by means of intelligent diagnostics that translate data into immediately applicable guidance This significance can be summarized as: “Transforming raw data into intelligent training decisions that enhance performance and promote safety”, drawing upon the findings of the Deep Mind study published in Nature, which demonstrated the superiority of adaptive models in complex motor tasks The study aimed to investigate the effectiveness of adaptive algorithms based on deep learning in mastering the skill of bar flight on the parallel bars apparatus in men’s artistic gymnastics The researcher employed the experimental method using a two-group design (control/experimental) on a sample of elite artistic gymnasts, with careful control of environmental and individual variables. Through in-depth data analysis, the study demonstrated the superiority of the intelligent diagnostic system, as artificial intelligence algorithms proved highly efficient in detecting subtle biomechanical errors that traditional methods fail to identify. Moreover, they provided real-time, three-dimensional motion analysis within fractions of a second Finally, the study recommended the adoption of the intelligent system in training programmers by integrating the proposed AI model as a core tool in elite gymnastics coaching. This would transform motor correction from a delayed, observation-based process to an accurate, real-time system. The study also advised incorporating this model into coach education programmers, curricula of physical education faculties, and youth training academies.