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

<p>This paper aims to investigate the effectiveness and influencing factors of different machine learning algorithms on soccer footwork recognition. In this paper, we use inertial sensors to obtain the basic data of soccer players’ movements, then convert them into initial data of footwork using pose representation and pose-solving filtering. The value of K mainly influences the classification accuracy of KNN, and the highest accuracy of 67.23% is achieved when K is 5. The classification accuracy of SVM is related to the choice of the distance function. The accuracy of CNN is mainly affected by the size of the convolutional kernel and the convolutional step size, and the highest accuracy is 73.82%. The machine learning-based soccer step recognition can improve the recognition accuracy of traditional physical methods and provide scientific sports guidance for soccer players’ training.</p>
</abstract>

This paper aims to investigate the effectiveness and influencing factors of different machine learning algorithms on soccer footwork recognition. In this paper, we use inertial sensors to obtain the basic data of soccer players’ movements, then convert them into initial data of footwork using pose representation and pose-solving filtering. The value of K mainly influences the classification accuracy of KNN, and the highest accuracy of 67.23% is achieved when K is 5. The classification accuracy of SVM is related to the choice of the distance function. The accuracy of CNN is mainly affected by the size of the convolutional kernel and the convolutional step size, and the highest accuracy is 73.82%. The machine learning-based soccer step recognition can improve the recognition accuracy of traditional physical methods and provide scientific sports guidance for soccer players’ training.

A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

Applied Mathematics and Nonlinear Sciences

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

This paper aims to investigate the effectiveness and influencing factors of different machine learning algorithms on soccer footwork recognition. In this...

A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

Online veröffentlicht: 26. Juli 2023

Seitenbereich: -

Eingereicht: 28. Sept. 2022

Akzeptiert: 03. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00092

Schlüsselwörter
K-nearest neighbor algorithm, Support vector machine, Convolutional neural network, Soccer step recognition, Inertial sensor

© 2023 Chengjun Li et al., published by Sciendo

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

A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

Online veröffentlicht: 26. Juli 2023

Seitenbereich: -

Eingereicht: 28. Sept. 2022

Akzeptiert: 03. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00092

SchlüsselwörterK-nearest neighbor algorithm, Support vector machine, Convolutional neural network, Soccer step recognition, Inertial sensor

© 2023 Chengjun Li et al., published by Sciendo

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

Schlüsselwörter
K-nearest neighbor algorithm, Support vector machine, Convolutional neural network, Soccer step recognition, Inertial sensor