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

<p>The accurate analysis and prediction of human motion posture can provide effective data support for sports training. With the continuous progress of computer science and technology and the gradual maturity of artificial intelligence technology, there is still some controversy about the recognition effect of motion features under different algorithms. Therefore, this paper compares the recognition effects of different learning methods, including two machine learning methods by using k-means classification, such as SVM and a deep learning method CNN. The motion data of three basic movements, such as running, jumping and turning, are collected and their feature information is extracted by sensor equipment. The recognition effect is compared with the overall accuracy rate as the evaluation index. Among them, when k-means classification is used, through feature extraction and selection, the final combination of mean and variance features is obtained and applied to SVM. When CNN is adopted, its performance can be optimised by adjusting its network structure and some super parameters in the convolution layer and pool layer.</p>
</abstract>

The accurate analysis and prediction of human motion posture can provide effective data support for sports training. With the continuous progress of computer science and technology and the gradual maturity of artificial intelligence technology, there is still some controversy about the recognition effect of motion features under different algorithms. Therefore, this paper compares the recognition effects of different learning methods, including two machine learning methods by using k-means classification, such as SVM and a deep learning method CNN. The motion data of three basic movements, such as running, jumping and turning, are collected and their feature information is extracted by sensor equipment. The recognition effect is compared with the overall accuracy rate as the evaluation index. Among them, when k-means classification is used, through feature extraction and selection, the final combination of mean and variance features is obtained and applied to SVM. When CNN is adopted, its performance can be optimised by adjusting its network structure and some super parameters in the convolution layer and pool layer.

A Comparative Study of Motion Feature Recognition under Different Learning Methods

Applied Mathematics and Nonlinear Sciences

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

The accurate analysis and prediction of human motion posture can provide effective data support for sports training. With the continuous progress of computer...

A Comparative Study of Motion Feature Recognition under Different Learning Methods

Pubblicato online: 31 lug 2023

Pagine: 3567 - 3576

Ricevuto: 12 apr 2022

Accettato: 09 set 2022

DOI: https://doi.org/10.2478/amns.2021.2.00222

Parole chiave
machine learning, deep learning, motion characteristics, information extraction

© 2023 Shuang Zhou, published by Sciendo

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

A Comparative Study of Motion Feature Recognition under Different Learning Methods

Pubblicato online: 31 lug 2023

Pagine: 3567 - 3576

Ricevuto: 12 apr 2022

Accettato: 09 set 2022

DOI: https://doi.org/10.2478/amns.2021.2.00222

Parole chiavemachine learning, deep learning, motion characteristics, information extraction

© 2023 Shuang Zhou, published by Sciendo

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

Parole chiave
machine learning, deep learning, motion characteristics, information extraction