Welcome to Scholar Publishing Group

International Journal of Sports Technology, 2020, 1(3); doi: 10.38007/IJST.2020.010303.

Intelligent Data Analysis Model in Tennis Intelligent Training Simulation Design

Author(s)

Xiazhong Chen

Corresponding Author:
Xiazhong Chen
Affiliation(s)

Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

Abstract

With the increasing attention and love of tennis, much tennis-related auxiliary equipment emerges as the times require. A large number of companies specializing in tennis-assisted teaching have emerged in China and internationally, and have achieved some achievements. The main purpose of this paper is to discuss how to use the method of intelligent data analysis to carry out the simulation design of tennis intelligence training. This paper introduced an auxiliary tennis intelligent training simulation system designed by using image processing technology. The auxiliary sports training system used the method of human posture estimation to quantitatively analyze and compare the postures of athletes and coaches, so as to provide coaches with more intuitive exercise analysis and guidance. The experimental results of this paper showed that the highest recognition rate was 100%, and the lowest was 98.0%; the highest accuracy rate was 98.0%, and the lowest was 96.4%. It can be seen that the simulation system had a high recognition rate of actions in tennis training. It is very meaningful to apply the intelligent data analysis model to the simulation design of tennis intelligent training. The system can not only correctly identify the movements of athletes, but also can compare their movements with those of the coaches, so as to correct wrong movements and improve the efficiency of training. 

Keywords

Tennis Smart Training, Human Pose Estimation, Intelligent Data Analysis Model, Image Processing

Cite This Paper

Xiazhong Chen. Intelligent Data Analysis Model in Tennis Intelligent Training Simulation Design. International Journal of Sports Technology (2020), Vol. 1, Issue 3: 17-31. https://doi.org/10.38007/IJST.2020.010303.

References

[1] Baiget E, Iglesias X, Fuentes J P. New Approaches for On-court Endurance Testing and Conditioning in Competitive Tennis Players. Strength and Conditioning Journal. (2019) 41(5): 9-16. https://doi.org/10.1519/SSC.0000000000000470

[2] Anbarci N, Arin K P, Kuhlenkasper T, Zenker C. Revisiting loss aversion: Evidence from professional tennis. Journal of Economic Behavior & Organization. (2018) 153(SEP.): 1-18. https://doi.org/10.1016/j.jebo.2017.10.014

[3] Cohen-Zada D, Krumer A, Shapir O M. Testing the effect of serve order in tennis tiebreak. Journal of Economic Behavior & Organization. (2018) 146(feb.): 106-115. https://doi.org/10.1016/j.jebo.2017.12.012

[4] Kovalchik S A, Ingram M. Estimating the duration of professional tennis matches for varying formats. Journal of Quantitative Analysis in Sports. (2018) 14(1): 13-23. https://doi.org/10.1515/jqas-2017-0077

[5] Subijana C, Conde E, Garcia M P, Chamorro J L. Explorando la carrera dual en tenistas: diferencias segun genero y nivel competitivo Exploring dual career in tennis players: gender and competitive level differences. Cultura, Ciencia y Deporte. (2021) 16(47): 95-106. https://doi.org/10.12800/ccd.v16i47.1697

[6] Haridas A V, Marimuthu R, Sivakumar V G. A critical review and analysis on techniques of speech recognition: The road ahead. International Journal of Knowledge-Based in Intelligent Engineering Systems. (2018) 22(1): 39-57. https://doi.org/10.3233/KES-180374

[7] Bo Shen, Shuoyu Wang, Peng Shi. A Visual Information Based Walking Intention Recognition Method for Intelligent Walking Training Robot. International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association. (2018) 23(1): 9-18.

[8] Song T, Zhao H, Liu Z, Liu H, Sun D. Intelligent human hand gesture recognition by local-global fusing quality-aware features. Future Generation Computer Systems. (2021) 115(7043): 298-303. https://doi.org/10.1016/j.future.2020.09.013

[9] Gieczyk A, Chora M. Intelligent human-centred mobile authentication system based on palmprints. Journal of Intelligent and Fuzzy Systems. (2020) 39(6): 8217-8224. https://doi.org/10.3233/JIFS-189142

[10] Saqib S, Ditta A, Khan M A. Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance. Cmc -Tech Science Press-. (2020) 66(2): 2061-2076. https://doi.org/10.32604/cmc.2020.013905

[11] Kim Y, Ghorpade A, Fang Z. Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System. IEEE intelligent systems. (2018) 33(4): 5-23. https://doi.org/10.1109/MIS.2018.043741317

[12] Wei Quan, Jinseok Woo, Yuichiro Toda, Naoyuki Kubota. Human Posture Recognition for Estimation of Human Body Condition. Journal of Advanced Computational Intelligence and Intelligent Informatics. (2019) 23(3): 519-527. https://doi.org/10.20965/jaciii.2019.p0519

[13] Qu J, Qiao N, Shi H. Convolutional neural network for human behavior recognition based on smart bracelet. Journal of Intelligent and Fuzzy Systems. (2020) 38(8): 1-12. https://doi.org/10.3233/JIFS-179651

[14] Gadebe M L. Smartphone Nave Bayes Human Activity Recognition Using Personalized Datasets. Journal of Advanced Computational Intelligence and Intelligent Informatics. (2020) 24(5): 685-702. https://doi.org/10.20965/jaciii.2020.p0685

[15] Jing Y. Research on fuzzy English automatic recognition and human-computer interaction based on machine learning. Journal of Intelligent and Fuzzy Systems. (2020) 39(4): 5809-5819. https://doi.org/10.3233/JIFS-189057

[16] Chowdary M K, Hemanth D J. Human emotion recognition using intelligent approaches: A review. Intelligent Decision Technologies. (2020) 13(4): 417-433. https://doi.org/10.3233/IDT-190101

[17] Srivastava S, Tripathi B K. Deep Intelligent System for Human Recognition in Complex Domain. Journal of Engineering and Applied Sciences. (2019) 14(2): 373-385. https://doi.org/10.36478/jeasci.2019.373.385

[18] Manske R C, Stovak M, Loo P. Full effort compared to partial effort performance of the tennis serve in collegiate tennis players. International Journal of Sports Science & Coaching. (2021) 16(3): 763-767. https://doi.org/10.1177/1747954120978645

[19] Cooper C, Kennedy R E. Expected length and probability of winning a tennis game. The Mathematical Gazette. (2021) 105(564): 490-500. https://doi.org/10.1017/mag.2021.117

[20] Silva S D, Mioranza D, Matsushita R. FIFA Is Right: The Penalty Shootout Should Adopt the Tennis Tiebreak Format. Open Access Library Journal. (2018) 05(3): 1-23. https://doi.org/10.4236/oalib.1104427