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Abstract

At present, there is less software related to sport technical behavior recognition, and there are few studies on the classification and identification of detailed actions. By introducing computer technology to analyze the efficiency and regularity of sports, not only the characteristics of athletes can be excavated, but also the visibility and dynamic tracking of sport training can be provided. The process of sports education is a fast and complex systematic process. Through the interactive system of physical education, we can use different methods to collect sports data and make a comparative analysis of athletes’ movements. Through the data mining of the relationship between athletes’ physiological indexes and sports load, the unreasonable link in sports training can be avoided. Also, in sports training, we can use computer vision and modern biomechanics to construct a virtual sports education situation. With the classification accuracy as the fitness function, this paper collects the data through the network database, and returns the corresponding sport training parameters on this basis. The results showed that the accuracy of the model was nearly 98%, which met the actual demand. Therefore, the development of sports education assistant system can provide strong support for the process control of sports training and education.

Keywords

Support vector improved model computer interactive system recognition algorithm

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References

Jin

, Jin

S.W.

, Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms, Applied Soft Computing Journal 15(2) (2014), 113–120.

, You

, Yin

, et al., An improved fault-location method for distribution system using wavelets and support vector regression, International Journal of Electrical Power & Energy Systems 55(2) (2014), 467–472.

Ali

, Kamavuako

E.N.

, Scheme

E.J.

, et al., Support vector regression for improved real-time, simultaneous myoelectric control, IEEE Transactions on Neural Systems & Rehabilitation Engineering 22(6) (2014), 1198–1209.

Chan

W.H.

, Mohamad

M.S.

, Deris

, et al., Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme, Computers in Biology & Medicine 77(C) (2016), 102–115.

Guo

, Deng

, Jie

, et al., Sensor Multi-fault Diagnosis With Improved Support Vector Machines, IEEE Transactions on Automation Science & Engineering 14(2) (2017), 1053–1063.

Mishra

, Mishra

, SVM-BT-RFE: An improved gene selection framework using Bayesian T-test embedded in support vector machine (recursive feature elimination) algorithm, Karbala International Journal of Modern Science 1(2) (2015), 86–96.

, Zhou

, Li

, et al., Vibrant fault diagnosis for hydro-electric generating unit based on support vector data description improved with fuzzy K nearest neighbor, Proceedings of the Csee 34(32) (2014), 5788–5795.

Ansari

H.R.

, Gholami

, An improved support vector regression model for estimation of saturation pressure of crude oils, Fluid Phase Equilibria 402 (2015), 124–132.

, Chen

, Liu

, et al., Application of support vector machine based on improved grid search in quantitative analysis of gas, Chinese Journal of Sensors & Actuators 28(5) (2015), 774–778.

10.

Miao

, Liu

, Cao

, et al., Malware detection using bilayer behavior abstraction and improved one-class support vector machines, International Journal of Information Security 15(4) (2016), 361–379.

11.

Fattahi

, Application of improved support vector regression model for prediction of deformation modulus of a rock mass, Engineering with Computers 32(4) (2016), 567–580.

12.

Zhi-Jie

, Chun-Yan

Y.U.

, An Improved Support Vector Machines with Balanced Binary Decision Tree forMulti-class Classification, Journal of Chinese Computer Systems 35(5) (2014), 1124–1127.

13.

Dongxiao

, Tiannan

, Bingyi

, Load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm, Journal of Combinatorial Optimization 33(3) (2016), Power 1–22.

14.

, Hua

, Tang

, et al., Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer, Neural Computing and Applications 27(6) (2016), 1451–1461.

15.

A Support. Vector Machine-Based Particle Filter Method for Improved Flooding Classification, IEEE Geoscience and Remote Sensing Letters 12(9) (2015), 1943–1947.

16.

Dharamkar

, Singh

R.R.

, Dharamkar

, et al., Cyber-Attack Classification using Improved Ensemble Technique based on Support Vector Machine and Neural Network, International Journal of Computer Applications 103(11) (2014), 1–7.

17.

Zhang

S.M.

, Li

B.X.

, Wang

B.Y.

, The Application of an Improved Integration Algorithm of Support Vector Machine to the Prediction of Network Security Situation, Applied Mechanics and Materials 513-517 (2014), 2285–2288.

18.

Optimized support vector regression model by improved gravitational search algorithm for flatness pattern recognition, Neural Computing and Applications 26(5) (2015), 1167–1177.

19.

Gangquan

S.I.

, Shuiwang

L.I.

, Jianquan

, et al., Squares Support Vector Machine Parameters Optimization Based on Improved Fruit Fly Optimization Algorithm with Applications, Journal of Xian Jiaotong University 51(6) (2017), Least 14–19.

20.

Zhu

, Wei

, Localization Algorithm in Wireless Sensor Networks Based on Improved Support Vector Machine, Journal of Nanoelectronics & Optoelectronics 12(5) (2016), 452–459.

21.

Fang

, Wu

, Shang

, et al., Failure Prediction of Wind Turbines Using Improved Gray Relation Analysis Based Support Vector Machine Method, Journal of Computational & Theoretical Nanoscience 13(9) (2016), 5887–5895.

22.

, Disaster prediction model based on support vector machine for regression and improved differential evolution, Natural Hazards 85(2) (2016), 1–18.

23.

Yang

, Deng

, Li

, et al., Improved traffic detection with support vector machine based on restricted Boltzmann machine, Soft Computing 21(11) (2015), 1–12.

24.

Kanisha

, Lokesh

, Kumar

P.M.

, et al., Speech recognition with improved support vector machine using dual classifiers and cross fitness validation, Personal & Ubiquitous Computing (3) (2018), 1–9.

25.

Patel

, Sondhi

, Motvani

, et al., Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization, International Journal of Computer Applications 100(18) (2014), 34–37.

Design of computer interactive system for sports training based on artificial intelligence and improved support vector

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