Narrow Results

In order to realize the rapid mining of sports data and intelligently formulate the sports training scheme that matches the athletes’ sports state, this paper studies the sports data mining and sports training decision support system based on big data technology. Leveraging MapReduce and OLAP, the system efficiently mines sports data and applies an improved Apriori algorithm to extract frequent itemsets tailored to athletes’ current states. These itemsets directly inform personalized training schemes, enhancing decision support for athletes. Experiments confirm the system’s scalability and precision in large-scale sports databases, effectively matching athletes’ training needs.

The aim of this paper is to improve the accurate analysis of the sports training project, research its video data profoundly, and discover deficiencies and summarize experience from the existing videos, thereby promoting China’s sports industry. According to previous works, the existing sports training target detection system has the problem of nonlinear video and low target detection accuracy, which can easily cause target loss or tracking failure. Therefore, the Kalman Filter (KF) is studied in-depth and applied to detect the targets in sports videos. Combined with the Multi-Innovation (MI) theory, an MI-Extended KF (EKF) algorithm model based on nonlinear dynamic technology is proposed. This model can effectively solve the filtering accuracy problem under the strong nonlinear system. Finally, the performance analysis through different datasets has verified the effectiveness of the model proposed further. Results demonstrate that the model proposed can effectively improve the filtering accuracy of nonlinear systems. As the angle module changes, the accuracy of sports recognition also changes. The final accuracy of the model proposed can reach above 96%. The simulation results and convergence are better than other algorithms, which also proves the effectiveness of the model proposed. The results can provide a theoretical basis for the research related to the detection of training sports targets in sports videos.

The purpose is to optimize the training methods of athletes and improve their competitive ability. First, the corresponding research model is established by analyzing the influencing factors of the athlete training system and combining with the dynamic nonlinear theory. Next, based on the deterministic learning theory, the dynamic nonlinear state identification model of athletes is established to obtain the state information of athletes in the process of competitive sports training and competition. Then, based on the established nonlinear state model, the nonlinear staged athlete training mode is designed. The experimental results show that the designed gait recognition algorithm based on deterministic learning can effectively collect the nonlinear signals generated by the movement process; the accuracy of gait recognition is about 90%, and it is less affected by the training and test datasets. The nonlinear staged training mode can effectively improve the overall index of maximum strength and strength endurance of tennis players, and effectively improve the training effect of players. This exploration provides a reference for the study of nonlinear competitive ability training mode of sports athletes.

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%.

With the rapid development of artificial intelligence, the related technologies and applications that accompany it emerge as the times require. The industry based on artificial intelligence is booming. Image recognition and target tracking technology are widely used in various fields, especially in the fields of security monitoring and augmented reality. Combined with the characteristics of athletes’ sports, an auxiliary information system is developed to supervise and guide the training in real time. It can track and analyze the characteristics of individual athletes’ sports function, the arrangement of coaches’ training plan, the state of brain function, the index of routine physiology and biochemistry, nutrition regulation, and the condition of injuries and injuries in the middle of the day, so as to reveal the athletes’ training in the middle of the day the changing rule of various indexes in the training state. Based on the mobile artificial intelligence terminal technology, this paper develops and designs a monitoring system for athletes’ training process in C/S mode. GPS is used to obtain athletes’ position information in real time and provide real-time guidance for athletes.

This paper proposes a system that helps improve children’s pitching form in baseball to improve throwing speed. The system uses Microsoft Kinect for motion sensing, onomatopoeia to teach users the timing of a pitch, and visual and aural effects to motivate them. The effectiveness of the system was tested through an experiment involving children as subjects. We also investigated how the impressions of visual and aural effects that effectively improve users’ throwing speed by conducting an experiment using subjects.