Biomechanical research on the construction and optimization of youth basketball training system based on the integration of sports and education
Abstract
The development and improvement of a youth basketball training program founded on the fusion of education and sports is investigated in this study. Athlete performance and academic advancement must be balanced in light of the growing need for comprehensive youth development. Biomechanical factors play a significant role in both sports performance and injury prevention, making it essential to integrate them into the training program design. To increase the effectiveness and design of training programs, the suggested model makes use of the Tabu Search Optimized Intelligent Random Forest (TSO-IRF) algorithm. The TSO-IRF identifies important physical, technical, and cognitive elements affecting basketball play by combining search-based optimization with machine learning (ML) approaches from a biomechanical perspective. It focuses on elements such as joint forces, muscle activation patterns, and movement kinematics, which are fundamental in determining an athlete's performance and injury risk. The research gathers information on youth basketball training programs, with a specific emphasis on biomechanical aspects. This includes information on players' body mechanics during different basketball movements, like jumps, shots, and passes. By integrating this data, the study ensures that the goals of educational development and sports training are aligned, while also considering the biomechanical requirements of the athletes. TSO-IRF is used to evaluate these multidimensional features and provides individualized training suggestions in line with the performance and educational objectives of both sports. Experimental results indicate that the TSO-ERF model can perform better than traditional methods, providing higher prediction recall (94.26%), accuracy (97.81%) and precision (97.21%) in development metrics for players. Additionally, the model shows improved adaptability across various skill levels as it can adjust training recommendations based on an athlete's unique biomechanical characteristics. The proposed youth basketball training system optimizes loads in training, reduces risks of injury, and develops young athletes over the long term. It facilitates athletic success but fosters cognitive and emotional development so that the fields of sport and education may converge. Future work involves the application of this model in other sports disciplines and algorithm refinement to take care of larger datasets that would help deliver real-time performance feedback.
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