AI-Powered Motion Analysis in Physical Education: GANs and Bayesian Approaches

Background: Bayesian models and Generative Adversarial Networks (GANs) are some AI tools that are transforming the analysis of motion in physical education. They address some of the problems in motion capture, namely data quality and diversity and uncertainty quantification, thereby making motion capture systems reliable and personalised for individual use.Objective: Project aims include the development of an artificial-intelligence machine analysis framework that incorporates Bayesian inferences, hence GANs, thereby adding quality assurance to motion prediction, advancement to motion capture, provision for real-time feedback, as well as exercise tailoring for inclusive physical education to individuals.Methods: Bayesian inference was used to analyze uncertainty, and GANs for generating synthetic data and fill in data gaps. In the suggested system, features are extracted, preprocessed, data is collected, and adaptive training is used to come up with a comprehensive motion analysis methodology.Results: The combined GAN-Bayesian method has error rate 7% lesser and accuracy, adaptability, and efficiency at 93%, 92%, and 91%, respectively. In case of students' participation and energy ratios, 96.8% and 93.7%, it could be seen that in response to real-time feedback the system is resilient under all circumstances related to physical education.Conclusion: GANs and Bayesian models are combined to optimize motion capture and analysis, ensuring that data-driven targeted training improves performance, inclusivity, and adaptability. With this innovative framework, the approach to physical education is put to a higher standard.