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Construction of the Athletic Ability Assessment System in Sports Teaching Integrating Artificial Intelligence and Convolutional Neural Network Models

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TL;DR

This study develops a semi-supervised multi-granularity spatial-temporal graph convolutional network (MG-STGC) for objective athletic ability assessment in college sports, achieving high accuracy (up to 95.6%) on benchmark datasets and enabling continuous, multi-dimensional scoring to support personalized training and intelligent physical education.

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In college physical education teaching, college students’ athletic ability assessment still faces problems of strong subjectivity in scoring and difficulty in quantifying complex movement characteristics. Traditional methods struggle to capture multi-granularity information of skeletons in continuous movements, and the cost of acquiring annotated data is high. This study aims to construct a set of quantitative assessment models for athletic ability oriented to college physical education classrooms. It realizes the joint optimization of action recognition and athletic ability scoring through the multi-granularity spatial-temporal graph convolutional network (MGSTGC). The MG-STGC model uses an encoder to extract joint-level, limb-level, and body-level features. It combines labeled and unlabeled data via semi-supervised learning strategies to achieve the joint optimization of action recognition and quantitative assessment of athletic ability. The athletic ability assessment module can generate continuous scores across four dimensions: strength, stability, standardization, and coordination. These scores are obtained through spatiotemporal statistical mapping of historical action segments and skeleton features, providing data support for individualized training. On the NTU RGB+D dataset, MG-STGC achieves a Top-1 accuracy of 95.6% and 89.7% on the X-view and X-sub benchmarks. On the FineGym dataset, it reaches 80.5% Top-1 accuracy on the Gym99 subset and 75.4% on the Gym288 subset, with category average accuracies of 69.8% and 62.6%, respectively, outperforming baseline models. Ablation experiments show that the granularity information fusion module and parameters have an impact on model performance. Research shows that MGSTGC can efficiently capture multi-granularity information of action skeletons and provide an objective and quantitative method for athletic ability assessment in college physical education classrooms. MG-STGC also lays a feasible theoretical and practical foundation for intelligent physical education teaching and personalized training.

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Physical education is an important part of the university education system, and it is necessary to carry out a detailed analysis of the relevant contents. This paper takes the physical education teaching in colleges and universities as the research object, analyzes the application value of Internet technology, sorts out the basic principles of physical education teaching in colleges and universities, and sorts out the specific application of Internet technology in physical education teaching in colleges and universities from the dimensions of optimizing curriculum resources, building network matrix, improving the evaluation system, and carrying out independent exercise, aiming to provide thinking directions for more college physical education teachers, scientifically apply Internet technology, upgrade and optimize the level of physical education teaching in colleges and universities, and cultivate the good habits of lifelong exercise in students.

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Mobile network technology is an inevitable product of the development of the times, and has been applied and achieved good results in the field of higher education. It has the characteristics of portability of mobile terminals, convenience of mobile networks, and universality of mobile network application services. In college physical education teaching, the application of this technology will inevitably have a certain impact on college physical education teaching. This paper adopts research methods of literature and Delphi. The study found that the application of mobile network technology in physical education teaching can promote teaching and communication between teachers and students, as well as between students and students, provide support for students' mobile learning, and optimize the management of physical education teaching. In addition, it may also have some negative impacts on the classroom teaching of physical education in universities, students' psychology, and life patterns. Based on the characteristics of mobile internet in teaching application, this paper proposes countermeasures and suggestions for the application of mobile internet technology in physical education teaching in universities.

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