<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>There are many different types of batting motions in table tennis, and the accurate identification of these motion patterns is of great importance for the analysis of batting motions. In this paper, firstly, we explore the efficient representation of the batting motion in the time domain by reasonably placing the sensors and processing the collected data with respect to the characteristics of the batting motion. Secondly, based on the SVM algorithm, in real-world applications, the issue of creating complicated hypersurfaces is resolved using the kernel function technique., so that the simple hypersurfaces in the original space can achieve satisfactory classification results. Finally, a table tennis batting action recognition algorithm based on the SVM algorithm is calculated. The algorithm can efficiently extract the features of batting actions in temporal order and finally accomplishes an average recognition correctness of 93.58%, which is 4.155% more accurate than decision trees, 5.255% more accurate than random forests, and 3.305% more accurate than integrated classifiers. The best performance among the four classifiers indicates that the model has the best performance. This study demonstrates that the SVM algorithm model can quickly and effectively classify and identify action signals for the batting action during table tennis training.</p>
</abstract>

There are many different types of batting motions in table tennis, and the accurate identification of these motion patterns is of great importance for the analysis of batting motions. In this paper, firstly, we explore the efficient representation of the batting motion in the time domain by reasonably placing the sensors and processing the collected data with respect to the characteristics of the batting motion. Secondly, based on the SVM algorithm, in real-world applications, the issue of creating complicated hypersurfaces is resolved using the kernel function technique., so that the simple hypersurfaces in the original space can achieve satisfactory classification results. Finally, a table tennis batting action recognition algorithm based on the SVM algorithm is calculated. The algorithm can efficiently extract the features of batting actions in temporal order and finally accomplishes an average recognition correctness of 93.58%, which is 4.155% more accurate than decision trees, 5.255% more accurate than random forests, and 3.305% more accurate than integrated classifiers. The best performance among the four classifiers indicates that the model has the best performance. This study demonstrates that the SVM algorithm model can quickly and effectively classify and identify action signals for the batting action during table tennis training.

Research on multimedia intelligent algorithm based on recognition and detection of table tennis ball striking action

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

There are many different types of batting motions in table tennis, and the accurate identification of these motion patterns is of great importance for the...

Research on multimedia intelligent algorithm based on recognition and detection of table tennis ball striking action

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 15 dic 2022

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00468

Parole chiave
Table tennis action recognition, Support vector machine, Acceleration Sensor, Kernel function, Hyperplane

© 2023 Xinjing Dong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on multimedia intelligent algorithm based on recognition and detection of table tennis ball striking action

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 15 dic 2022

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00468

Parole chiaveTable tennis action recognition, Support vector machine, Acceleration Sensor, Kernel function, Hyperplane

© 2023 Xinjing Dong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Table tennis action recognition, Support vector machine, Acceleration Sensor, Kernel function, Hyperplane