[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Zhang, Jin

doi:10.1155/2021/3155357

Cited by 15 publications

(16 citation statements)

References 33 publications

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“…is study introduces the application of multiple regression analysis in table tennis mixed doubles. In subsequent studies, deep analysis and discussion of singles or doubles [20][21][22][23][24]. Besides, in practice, in commercial, construction, or other sports games, it can solve many practical problems.…”

Section: Study Conclusionmentioning

confidence: 99%

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

Wang

et al. 2022

Journal of Environmental and Public Health

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Since the table tennis mixed doubles competition was officially listed as the Olympic Games, the players around the world paid more attention to the project. In this background, the 20 mixed doubles finals were used in the literature, video observation, and analysis of multiple regression. From the receiving point of view, the score difference between men and women is not very great, but female players may be more consistent. The contribution of male and female scores to the game is more effective than model 1 and model 2 for different rounds. Therefore, model 2 is more efficient in the analysis of high-level table tennis competitions. Multiple regression model can be used to analyze and predict table tennis singles, doubles, and mixed doubles games, which we will see more and more in future research results.

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Section: Study Conclusionmentioning

confidence: 99%

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

Wang

et al. 2022

Journal of Environmental and Public Health

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“…e innovation of this paper is that (1) the application of machine learning to the research on the evaluation model of the technical and tactical e ectiveness of tennis matches, which is innovative and practical. (2) First, this study utilizes AI strategies to just and obviously break down the cooperation and dynamic course of members in tennis matches. It can improve the strategic pro ciency of mentors and competitors participated in tennis and go with their choicemaking ways of behaving more judicious, logical, and prescient.…”

Section: Introductionmentioning

confidence: 99%

Model for Evaluating the Technical and Tactical Effectiveness of Tennis Matches Based on Machine Learning

Liu

2022

Mobile Information Systems

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As the level of tennis improves, the ability and strategy to play in the game determine the responsibility for the outcome of the game. It is committed to improving the professional and strategic level of Asian tennis players and narrowing the gap with high-level European and American tennis players. The purpose of this paper is to study the application of machine learning in the study of the evaluation model of the technical and tactical effectiveness of tennis matches, and proposes the decision tree algorithm, artificial neural network, reinforcement learning algorithm, and related concepts of tennis matches. Therefore, this paper selects Federer’s technical and tactical games from 2013 to 2017 as the research object. And by paying attention to the application characteristics of Federer’s methods and strategies in each stage, a detailed statistical analysis of the data is carried out point by point. The exploratory outcomes show that through the AI calculation, it is found that the incredible skill and vital sufficiency of Federer’s hard court game change around 0.600, and the typical worth is 0.594. Particular and vital efficiency showed a sluggish recuperation in 2017.

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“…First, the vision system of the TTR is an upgraded version of general machine learning vision systems and the eye to detect and track the real-time position and dynamic states of TTB. The vision system has a solid ability to track high-speed moving objects (Li, 2021 ; Zhang, 2021 ). Second, the decision-making system is the instruction-distributor of the three systems.…”

Section: Relevant Theories and Methodsmentioning

confidence: 99%

Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

Liu

Ding

2022

Front. Neurorobot.

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The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.

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[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Cited by 15 publications

References 33 publications

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

[Retracted] Application of Multiple Regression Analysis Model in Table Tennis Competition

Model for Evaluating the Technical and Tactical Effectiveness of Tennis Matches Based on Machine Learning

Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

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