Tennis Teaching Research Articles

EFEITOS DO EXERCÍCIO DE FLEXIBILIDADE DOS MEMBROS INFERIORES SOBRE A APTIDÃO FÍSICA DOS JOGADORES DE TÊNIS DE MESA

ABSTRACT Introduction: the main fitness requirements of table tennis players are highly automated motor skills and a strong body control ability; this combination is believed to benefit from flexibility exercises. Objective: Study the effects of lower limb flexibility training on physical fitness in table tennis players. Methods: The controlled experiment randomly divided 20 volunteers into two groups with no statistical differences between them. The control group continued using the existing table tennis teaching program, while the experimental group adopted the optimized lower limb flexibility training program. The course design was organized according to the physical education teacher's table tennis player talent training plan. Results: In the experimental group, the number of right body turns for throwing and blocking increased to 60.41 ± 4.67 times after the experiment; the number of right body turns increased to 64.045 ± 5.22; in the control group, the number of right push and blocks increased to 56.78 ± 3.67 times after the experiment. After the experiment, the number of fixed point swing speed of the whole station increased to 64.66 ± 3.95 (P<0.05). Conclusion: Adding lower limb flexibility exercises to table tennis flexibility training has been shown to improve athletes’ static and dynamic flexibility, positively optimizing players’ fitness. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

INVESTIGAÇÃO E PREVENÇÃO DE RISCO ESPORTIVO NO ENSINO DO TÊNIS DE MESA

ABSTRACT Introduction: Among college students who are not majoring in sports, table tennis is well accepted, highly popular, and has low requirements regarding equipment investment. Therefore, many students choose this sport, although there are also certain sports risks in the course of the sport. Objective: Investigate the sporting risks of teaching table tennis and its preventive measures. Methods: Before each class exercise, the physical education teacher led the experimental class to rigorously complete the warm-up. In contrast, the control class maintained the basic program configuration without the warm-up phase. After 24 hours of practice, students in the experimental class and the control class were tested with the FMS. Results: The experimental class stability score increased from 1.58 points to 1.84 points, and the trunk rotation stability score increased from 1.68 points to 2.05 points. Conclusion: Warm-up activities before sports can further reduce sports risks in the table tennis teaching process by providing a better and safer higher education environment for students. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Retracted: MEMS Sensors in Tennis Teaching and Training under the Background of Big Data Intelligent Communication

Retracted: MEMS Sensors in Tennis Teaching and Training under the Background of Big Data Intelligent Communication

Retracted: Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training.

[This retracts the article DOI: 10.1155/2022/7625626.].

Part 1—USTA and Tennis Canada Learning to Play Tennis Initiatives: Applying Ecological Dynamics, Enactivism, and Participatory Sense-Making

In this series of two articles, we connect complexity theoretical frameworks of ecological dynamics and enactivism to initiatives for learning to play tennis advocated by USTA and Tennis Canada. These initiatives were inspired by the International Tennis Federation commitment to reduce the complexity of learning tennis by rescaling the game for children and novice players. This article suggests that tennis teaching is shifting from a skill and drill approach to one embracing a play-practice-play idea in line with Ecological Dynamics. Considering players as dynamic and adaptive sense-making beings, this article outlines how these initiatives create the conditions to embrace insights from motor learning in relation to a constraints-led approach, and enactivism from embodied cognition. This first article concludes with applying an enactivist teaching strategy called modification by adaptation to these tennis initiatives, showing how players of diverse ability can challenge each other, promoting more game-based dynamic learning.

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Sensor technology has been deeply into the sports industry, with the help of sensors to monitoring and collection data for the physical training in real-time. In table tennis, wearable sensors can record the amount of training, movement essentials, and the number of strokes of both hands and assist in the testing and evaluation of table tennis. This paper analyzes the application of wearable sensors in table tennis training activities and the details of signal collection and feature extraction. At the same time, machine learning technology often used to recognize and test table tennis training data, and a support vector machine (SVM) is one of the representative classifiers. Applying the processed signal data to the classification and testing of SVM can effectively identify the movement and evaluate the training effect and athletes’ physical fitness. The integration of intelligent sensors and table tennis can effectively improve the evaluation efficiency and quality in the process of teaching and training.

Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine.

In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. Therefore, using machine learning technology for fuzzy research has a good research prospect. In the development of related research, the author of this study noticed that some researchers began to use tennis machine learning technology and achieved good results. However, most of the research is only for simple analysis and is related to the current work. It cannot be used to move a solid tennis ball, nor it can make small changes to the original tennis movement; thus, it cannot carry out a complete and brand-new movement. The defense of tennis first establishes visual teaching tools with the help of various courses and visual teaching techniques to improve the teaching effect. By optimizing the network data, this study constructs the corresponding data search model, which downloads a large amount of data from the network ram, so as to separate the impact of the network environment on the load. The simulation results show that the model is optimized for the high-quality 3G network environment, and the load time and energy consumption are greatly reduced. It is more efficient in WiFi and a a high-quality 4G network environment.

The Application of Virtual Reality Technology in Sports Psychology: Theory, Practice, and Prospect.

Once virtual reality technology (Virtual Reality, VR) came out, it has received a lot of attention; in recent years, it has been widely used in the study of psychology. Because it can improve the ecological validity of experimental research, the level of conditional control, reproducibility, and avoid the dangers of field operations, it has been introduced into the field of psychology by many researchers. Compared with traditional sports psychology research methods, virtual reality technology has the characteristics of multiperception, immersion, interaction, and imagination to get a better, more realistic feel and increase people's interest in sports. Taking the application of virtual reality technology in table tennis teaching in colleges and universities as an example, this study aims to review the application of virtual reality technology in sports psychology; summarize the theory, practice, and prospect of the application of virtual reality technology in sports psychology; and add new content to the research of sports psychology. It aims to review the principles, characteristics, and application of virtual reality technology in sports psychology; summarize the advantages and disadvantages of the application of virtual reality technology in sports and point out the problems that need to be paid attention to when using this method; explore the application of virtual reality technology in sports psychology; and add new content to the research of sports psychology.

Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks.

In order to improve the effectiveness of tennis teaching and enhance students' understanding and mastery of tennis standard movements, based on the three-dimensional (3D) convolutional neural network architecture, the problem of action recognition is deeply studied. Firstly, through OpenPose, the recognition process of human poses in tennis sports videos is discussed. Athlete tracking algorithms are designed to target players. According to the target tracking data, combined with the movement characteristics of tennis, real-time semantic analysis is used to discriminate the movement types of human key point displacement in tennis. Secondly, through 2D pose estimation of tennis players, the analysis of tennis movement types is achieved. Finally, in the tennis player action recognition, a lightweight multiscale convolutional model is proposed for tennis player action recognition. Meanwhile, a key frame segment network (KFSN) for local information fusion based on keyframes is proposed. The network improves the efficiency of the whole action video learning. Through simulation experiments on the public dataset UCF101, the proposed 3DCNN-based KFSN achieves a recognition rate of 94.8%. The average time per iteration is only 1/3 of the C3D network, and the convergence speed of the model is significantly faster. The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students' learning and understanding of actions in the teaching process.

Study on the Quality Analysis and Improvement of Tennis Teaching under the Internet System

Improving the quality of tennis teaching and learning is an important part of the modernization of education in China. The limitations of traditional tennis teaching quality evaluation methods have made them controversial, and it is crucial to improve the scientific, rational, and timely evaluation of tenn is teaching quality. Therefore, it is necessary to establish a scientific and rational quality evaluation model for tennis education to evaluate the quality of tennis education. Based on the principle of constructing a perfect tennis teaching quality evaluation system, this paper analyzes the advantages and disadvantages of the previous tennis teaching quality evaluation methods and summarizes the problems existing in the current tennis teaching quality evaluation system in a university. On this basis, to break the limitations of the existing tennis teaching quality evaluation system in a university, a tennis teaching quality evaluation model based on genetic algorithm (GA) and back-propagation (BP) neural network is proposed and a more scientific reasonable tennis teaching quality evaluation system. The evaluation results of BP neural network optimization method based on adaptive mutation genetic algorithm are very satisfactory.

Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations

Abstract Tennis is a sport, which is mainly composed of many parts, such as mobile devices, software and hardware. With the rapid development of today's time, people's living standards are getting higher and higher, the traditional teaching mode can no longer meet the user's needs well, so teachers need to help students improve their overall quality with the help of more advanced modern online teaching methods, therefore, the design of a practical and flexible mobile assistance system is necessary for universities nowadays. This paper mainly introduces the tennis auxiliary system based on ordinary differential equations, which is based on the database, and it has the functions of data collection, processing and display. This software is designed and implemented by using javaSwing technology and solidworks development tools. After that, SSH framework is used to create the application and backend management interface for later maintenance and update, so that the management can understand the students' situation at any time and make corresponding decision analysis to better serve the users and improve the quality and level of tennis teaching.

Research on the Application of Artificial Neural Network-Based Virtual Image Technology in College Tennis Teaching.

At the same time that my country has shifted from high-speed development to high-quality development, my country has also put forward new requirements for education development. Due to the limited study time during college, each student's study habits and learning process are also different, and the degree of connection between tennis lessons is high, so there will be polarization when learning tennis. With the development of science and technology, more and more technological innovations are integrated into the classroom, and traditional teaching methods can no longer keep up with the pace of the times. Tennis teaching is a subject of equal proportion between theory and practice. The traditional teaching method simplifies the theory, which makes students to have some bad phenomena when they practice. Aiming at this series of problems, this paper uses algorithms such as softmax function and threshold function to construct an application model of virtual image technology based on the artificial neural network in tennis teaching. The research results of the article show that: (1) the average accuracy rate of the method in this paper is 97.22%, and the highest accuracy rate is 99.17%. The average accuracy rate also tends to increase with the increase of sample size; the recall rate is the highest, and the highest recall rate is 99.36%. The average recall rate is 96.77%; the highest correct rate is close to 100% and is significantly higher than the other three methods; the average correct rate reaches 98.8%; the response time is the shortest; the average response time is 33 ms; and the response time increases with the increase of the sample size. (2) After using this model, tennis skills have been improved, with an average of 12 in situ flips, an average of 7 in situ rackets, an average of 5 in situ forehand draws, and an average of 3 in situ backhand draws. (3) The average forehand and backhand scores of the class after the experiment were 90 and 86; the average forehand and backhand stability were 8 and 7; and the average forehand and backhand accuracy were 31 and 29, respectively. The average depth of forehand and backhand is 36 and 32. (4) Most of the students are satisfied with this model, and they all choose to strongly agree and relatively agree, and the percentage of very agree that helps stimulate learning has reached 60.52%, and no students choose to disagree very much.

Development of Basic Teaching Materials for Table Tennis in the Form of Interactive Multimedia for Education Students Sport Development Faculty of Sport Science Padang State University

Based on observations and the results of needs analysis in the field, the learning process for table tennis subjects so far still has many shortcomings and needs to be addressed, the presentation of the material content is very complex and difficult to understand, the learning method used is relatively monotonous and does not utilize available learning media, lecturers are still used as the only source of information, so students become less active and creative. So that the process of achieving learning objectives is a little hampered. The purpose of this research is to develop learning resources and media for students in particular and the wider community in the form of table tennis teaching materials in the form of interactive multimedia. This type of research is research and development using the Lee & Owen development model. The data analysis technique used descriptive qualitative analysis in the form of initial observation data and needs analysis and input. The results of the analysis in the form of validation and testing of the developed product can be reported as follows: (1) media expert validation showed 89.65% results, (2) material expert validation showed 97.22% results, (3) evaluation expert validation showed 68 results, 7%, (4) small group trial data 74.17%, (5) large group trial data 75.19%.

Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

In order to solve the problem of traditional tennis serving technique teaching, subjective, and experience-based teaching methods, the author proposes a method to extract tennis training movements based on artificial intelligence sensor video analysis. This method requires the use of ordinary cameras and computer technology; video analysis technology is used to guide tennis teaching and training. The result obtained is as follows: in the experimental group and the control group, at the end of the teaching stage, the movement characteristics of each link of the body are quite different, which can reach 15%-20%; when the experimental group and the control group did the “scratching back” movement during the teaching period, at the end of teaching, the students in the experimental group were close to high-level tennis players in their movement skills, and the movement gap was only about 5%. It is proved that the method proposed by the author provides an objective and scientific basis for measuring the referee’s penalty level. In the grass-roots tennis teaching and training, it has important value and significance and has a great promotion prospect.

MEMS Sensors in Tennis Teaching and Training under the Background of Big Data Intelligent Communication

Racket sports (tennis, pawns, tennis, etc.) are currently very popular sports, and they are a very intense aerobic exercise. The climatic conditions of the competition venue, time difference factors, the size of the competition venue, background, lighting, wind direction, etc. may all have an impact on the performance of the game. In order to fully control and eliminate these risk factors, it is recommended to go to the competition site for adaptive training one month in advance. If you can participate in the prematch training camp organized by the general administration, it is of course the best choice. This research mainly discusses the development of MEMS sensors in tennis teaching and training under the background of big data intelligent communication. Using a single inertial sensor fixed on the bottom of the racket for data collection, a real-time data stream window segmentation method based on the combination of sliding window and action window is proposed. Finally, based on the above recognition method, an intelligent tennis training system software is developed, introduces the processing flow of MEMS sensor data, and gives a specific implementation plan for data preprocessing through in-depth study of the characteristics of inertial navigation data. The tennis training system collects the raw data of tennis players during the training process through the MEMS sensor hardware module placed at the bottom of the tennis racket handle, and the sensor module can upload these raw data to the smart phone terminal through wireless communication. The smart phone terminal can analyze and process the collected raw data and finally feedback the number and proportion of each technical action of the tennis player in the training process and the maximum racquet speed through the mobile phone software in real time. After the practice training, the average CTN score of the control group was 141.73, and the standard deviation of the data was 4.185. After the practice training, the average CTN score of the experimental group was 161.67, and the standard deviation was 6.042. This research will improve the efficiency of tennis teaching and training.

Teaching of Backspin Serve in Table Tennis

Backspin serve is a common serve in table tennis matches, and it is also one of the difficult contents for students to master in college table tennis teaching. In order to make students master backspin serve more quickly, the author tries to practice the interest and practicability of serve in the teaching process. This paper expounds and analyzes the feedback information in the concrete implementation and practice in order to further improve the teaching content of physical education in colleges and universities and provide teaching reference for school teaching and sports training.

Intelligent Teaching of Tennis Based on Human Skeleton Node Model

In order to improve the effect of tennis intelligent teaching, this paper combines the human skeleton node model to construct a tennis intelligent teaching model. Aiming at the fact that there are errors in model identification and positional disturbances such as friction which cannot be avoided in actual tennis teaching, an effective sliding mode control strategy based on disturbance observer is presented. Moreover, this paper builds a simulation model through MATLAB/Simulink to verify the influence of the three parameters of inertia, damping, and stiffness in the admittance model on the control performance. Then, this paper combines the admittance control theory to analyze the difference between the active mode and the passive mode and the main functions of the two control modes and constructs an intelligent simulation system. The experimental study shows that the tennis intelligent teaching model based on the human skeleton node model has good effects in tennis action correction and tennis-teaching effect improvement.

Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training.

The Internet of Things is an important link in the future development of the Internet and will have a significant impact on development over the next five years or so. The purpose of this article is to learn how to use the Internet of Things to learn and practice table tennis and find new ways to integrate table tennis and the internet. This article proposes to build a detection system for IoT sensors and collect information about the ball's movement state and the athlete's movement state during table tennis. Through data analysis, we better guide athletes to develop table tennis training. At the same time, it also uses knowledge innovation to integrate the Internet of Things with the reality of life and better derive the development of the Internet of Things artificial intelligence. Based on this, this paper designs the construction of IoT sensors to collect the sports status of table tennis enthusiasts and verify the sports experience of table tennis enthusiasts by comparing factors such as physical training, basic skills and mobile training, interest, and confidence. The experimental results of this article show that the Internet of Things artificial intelligence has great potential, and it can innovatively promote industrial change. It better realizes the teaching and training of table tennis players and improves the teaching and training ability of table tennis by 20%.

Physical Education Teaching Strategy under Internet of Things Data Computing Intelligence Analysis.

Racket sports such as tennis are amongst the most popular recreational sports activities. Optimizing tennis teaching methods and improving teaching modes can effectively improve the teaching quality of tennis. In this study, a video and image action recognition system based on image processing techniques and Internet of things is developed to overcome the shortcomings of the traditional tennis teaching methods. To validate its performance, the students of tennis courses are divided into experimental group and control group, respectively. The control group is taught by using the traditional tennis teaching method whereas the experimental group is taught by using the IoT video and image recognition teaching system. Three factors of students including service throwing height, arm elbow angle, and knee bending angles of both groups are measured and compared with those of world elite tennis players. The results show that the students' serving abilities in the experimental group are significantly improved using the video and image recognition system based on IoT, and they are better than those of the students in the control group. The proposed video and image processing technique can be applied in students' physical education and can be employed to provide the basis for the innovation of tennis teaching strategies in physical education.

Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System

With the rapid development of information technology in today’s era, the application of the Internet, big data, and smart bracelet information technology in the field of sports has enhanced the intelligence of sports and plays an important role in promoting sports performance. This paper focuses on the application of wireless sensors in the field of tennis, using research methods such as literature research, video analysis, comparative research, and mathematical statistics, to explore and analyze the application of wireless sensors in the field of tennis big data, tennis robotics, and the implementation of tennis teaching and training, to provide a theoretical basis for promoting the application of wireless sensors in the field of tennis and also for the broader application of wireless sensors in sports to provide a theoretical reference. For the problem of multiple scales of motion targets in action videos, two video action recognition methods based on high- and low-level feature fusion are proposed, which are the video action recognition methods based on top-down feature fusion and the video action recognition methods based on bottom-up feature fusion. The multipowered mobile anchor nodes are allowed to move along a prescribed route and broadcast multiple power signals, and then, the location of the unknown node is estimated using a four-ball intersection weight center-of-mass algorithm. Simulations show experimentally that the algorithm reduces the average localization error and requires fewer anchor nodes.