Assessing and Improving Intelligent Physical Education Approaches Using Modified Cat Swarm Optimization Algorithm

In general, technological innovations have transformed sports training or physical education classrooms constructively. Physical education instructors can develop personalized and realistic expectations for their students using software, streaming videos, cameras, and trackers. However, the intelligent physical education models seek efficient assessment to improve its real-time acceptance. This paper proposes Enhanced Cat Swarm Optimization (ECSO) algorithm to create a physical education system assessment model and increase the evaluation impact of sports training methods. This research identifies and explains various factors to design a multi-functional physical education monitoring device based on visual sensing that meets the real needs of users. To find out how sensitive the parameters are, this study uses the local sensitivity analysis theory. It incorporates real-world needs to suggest an optimized CSO algorithm with modified searching and tracking modes. Subsequently, the proposed methodology implements a randomized experiment to assess the model’s success. The proposed ECSO algorithm employing modified searching and tracing modes gives the best assessment of 98.8%.