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Research on athlete's wrong movement prediction method based on multimodal eye movement recognition

Research on athlete's wrong movement prediction method based on multimodal eye movement recognition In order to solve the problems of large prediction error, long time consumption and large amount of interference data in the prediction results of traditional methods, an athlete's wrong movement prediction method based on multimodal eye movement recognition is proposed. Firstly, the spectral clustering algorithm is used to mine the wrong movements. Secondly, the least square method is used to improve the support vector machine, and the improved support vector machine is used to classify athletes' wrong movements according to the statistical characteristics of athletes' wrong movements. Finally, based on the classification results and the historical data of athletes' wrong movements, the trend of athletes' wrong movements is predicted by the multimodal eye movement recognition method to complete the prediction of athletes' wrong movements. The experimental results show that the method causes small prediction error, consumes short prediction time and has a low proportion of interference data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Reasoning-based Intelligent Systems Inderscience Publishers

Research on athlete's wrong movement prediction method based on multimodal eye movement recognition

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-0556
eISSN
1755-0564
DOI
10.1504/ijris.2022.126658
Publisher site
See Article on Publisher Site

Abstract

In order to solve the problems of large prediction error, long time consumption and large amount of interference data in the prediction results of traditional methods, an athlete's wrong movement prediction method based on multimodal eye movement recognition is proposed. Firstly, the spectral clustering algorithm is used to mine the wrong movements. Secondly, the least square method is used to improve the support vector machine, and the improved support vector machine is used to classify athletes' wrong movements according to the statistical characteristics of athletes' wrong movements. Finally, based on the classification results and the historical data of athletes' wrong movements, the trend of athletes' wrong movements is predicted by the multimodal eye movement recognition method to complete the prediction of athletes' wrong movements. The experimental results show that the method causes small prediction error, consumes short prediction time and has a low proportion of interference data.

Journal

International Journal of Reasoning-based Intelligent Systems – Inderscience Publishers

Published: Jan 1, 2022

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