An artificial intelligence and machine vision based evaluation of physical education teaching

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

Exploration on the Operation Status and Optimization Strategy of Networked Teaching of Physical Education Curriculum Based on AI Algorithm

Artificial Intelligence (AI) and networked teaching have made great progress in recent years, especially the application of AI algorithms in teaching, which has been relatively mature. The online teaching of physical education (PE) courses is an important practice of networked teaching, but many colleges and universities ignore the importance of AI when carrying out such teaching work. Many colleges and universities have not played a good role in AI in teaching, which leads to inefficient PE teaching and low learning enthusiasm of students.

Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

In order to improve the effectiveness of the evaluation of student’s learning status in foreign language classrooms, this paper applies machine vision to classroom teaching. Through an in-depth analysis of the relative motion relationship between the end marker points of classroom feature recognition and the center point of the machine vision system window, this paper first proposes an autonomous tracking motion algorithm of the machine vision system window based on the preset field of view parameters. Moreover, this paper realizes the motion function of the window to track the marker points autonomously, completes the simulation analysis through two sets of planned trajectories and two sets of master hands to collect the actual trajectories, and verifies the correctness and feasibility of the algorithm. The research study shows that the algorithm based on machine vision proposed in this paper can effectively judge the real-time state of students in the foreign language classroom.

Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm

Physical education (PE) teaching resources occupy a very important position in the teaching of PE theory. Especially in the context of the Internet era, how to effectively extract PE teaching resources from the Internet is very important for PE teachers. However, the quality of PE teaching resources on the Internet is uneven, if not correctly identified, it will bring harm to students’ values. Therefore, it is very necessary to correctly identify the teaching resources of positive psychology. In the era of artificial intelligence, advanced intelligent algorithms provide a solution for the realization of this purpose. In this study, a text sentiment analysis model multi-layer-attention convolutional neural network (ACNN)-CNN based on hierarchical CNN is proposed, which combines the advantages of convolutional neural networks and the attention mechanism. In multi-layer-ACNN-CNN, position encoding information is added to the embedding layer to improve the accuracy of text sentiment classification. In order to verify the performance of the model, online PE teaching resources are extracted by a crawler system and the proposed model is used to classify the positive psychology of the teaching resources. By comparison, the proposed model obtained a better positive psychology classification effect in the experiment, which verifies that the model can extract text features more accurately, and is more suitable for emotion classification of long texts.

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Objective

The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data.

Methods

This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax.

Results

In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%.

Conclusion

In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.

Evaluation of the Physical Education Teaching and Training Efficiency by the Integration of Ideological and Political Courses with Lightweight Deep Learning

The purpose is to improve the training effect of physical education (PE) based on the teaching concept of ideological and political courses. The research is supported by the lightweight deep learning (DL) model of the Internet of things (IoT). Through intelligent recognition and classification of human action and images, it discusses the PE and training scheme based on the lightweight DL model. In addition, by the optimization of the accelerated compression algorithm and the evaluation of the PE and training effect of the Openpose algorithm, an optimization model of the PE and training effect has been successfully established. The research data results indicate that after 120 iterations of the model, the system recognition accuracy of the convolutional neural network (CNN) algorithm can only be improved to about 75%, while the recognition accuracy of the Openpose algorithm can reach about 85%. Compared with the CNN algorithm under the same number of iterations, the recognition accuracy can be improved by 9.8%. In addition, when the number of nodes in the network layer is 60, the system delay time of the proposed Openpose algorithm is smaller. At this time, the system delay of the algorithm is only 10.8s. Compared with the CNN algorithm under the same conditions, the proposed algorithm can save at least 1.2s in system delay time. The advantage of the algorithm is that it can improve the efficiency of physical training and teaching, and this research has important reference significance for the digital and intelligent development of the teaching mode of PE.

Study on the Reform and Development of Modern Physical Education Teaching Based on 5G Internet Communication Technology

Physical education (PE) is, in general, one of the most important skills developed for human healthiness. Many barriers exist in society to improve the performance in Chinese physical activities. Furthermore, the incorporation of 5G communication network technology is becoming a trend in the increase of physical activity in China on a daily basis. Physical exercise may assist Chinese people to enhance their mental abilities, self-concept, and goal orientation and avoid mental illnesses such as sadness and anxiety. Physical exercise without education is like having a body but no soul. There is no doubt about the value of physical education and other types of exercise in the educational system. In this paper, we propose refined physical education teaching based on 5G network technology to obtain everlasting data without termination. First, we preprocess the sports dataset using a stacked denoising autoencoder (SDAE), and a Gaussian Mixture Model (GMM) is utilized for the feature extraction process. A random forest approach (RFA) is then used in the selection of the features. Furthermore, we adopted a CNN-based upgraded classifier for classification and efficient data allocation (EDA) algorithm for storing data generated by the 5G network. Experimental results reveal that our proposed method outperforms the baseline methods by a huge margin.

Research on Optimal Control Method of Tennis Racket String Diameter Based on Kalman Filter Algorithm

In order to explore the optimal control method of tennis racket string diameter, this paper applies the Kalman filter algorithm to the collection and processing of tennis sports parameters. When the Kalman filter uses the optimal gain (the Kalman filter enters the steady state), the corresponding cost function is established based on the noncorrelated nature of its residual sequence, which is used as the judgment condition for the sampling strategy switch to improve the stability of the system. In addition, this paper improves the real-time performance and calculation accuracy of data transformation through adaptive sampling strategy and adaptive scale factor, improves the stability and estimation accuracy of the system as a whole, and builds an intelligent monitoring system. Finally, this paper systematically studied the optimization control method of tennis racket string diameter and verified that the Kalman filter algorithm can play a certain role in the optimization control of tennis racket string diameter.