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

<p>The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. After completing the model construction, the structure of the external limb sports training auxiliary machinery is designed, and the system structure of the sports training auxiliary machinery is constructed by combining the needs of sports training. The optimization of external limb sports training auxiliary machinery was achieved by performing and solving mathematical modeling of a single limb. The performance of the action recognition model and the sports auxiliary machinery is examined through experiments, in which the action recognition model based on deep learning in this paper has the best effect, which is 95.75%, and the loss values of the model after convergence are all below 0.2. In the empirical testing of the performance of the sports assistive machine, the obtained target trajectory tracking error is within ±3mm and ±2mm, respectively, and the performance of the deep learning-based action recognition model and the external limb sports training assistive machine is excellent, which can effectively help the sports training. The purpose of this study is to provide design direction and practical significance for the design of a sports training assistance system.</p>
</abstract>

The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. After completing the model construction, the structure of the external limb sports training auxiliary machinery is designed, and the system structure of the sports training auxiliary machinery is constructed by combining the needs of sports training. The optimization of external limb sports training auxiliary machinery was achieved by performing and solving mathematical modeling of a single limb. The performance of the action recognition model and the sports auxiliary machinery is examined through experiments, in which the action recognition model based on deep learning in this paper has the best effect, which is 95.75%, and the loss values of the model after convergence are all below 0.2. In the empirical testing of the performance of the sports assistive machine, the obtained target trajectory tracking error is within ±3mm and ±2mm, respectively, and the performance of the deep learning-based action recognition model and the external limb sports training assistive machine is excellent, which can effectively help the sports training. The purpose of this study is to provide design direction and practical significance for the design of a sports training assistance system.

Designing an Athletic Training Assistance System under Deep Learning Guided by Kinesiology Theory

Applied Mathematics and Nonlinear Sciences

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

The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. The combined...

Designing an Athletic Training Assistance System under Deep Learning Guided by Kinesiology Theory

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 26. Dez. 2023

Akzeptiert: 03. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0299

Schlüsselwörter
Convolutional network, Long and short-term memory, Action recognition, Kinematic modeling, Training assistance system

© 2024 Chen Chen, published by Sciendo

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

Designing an Athletic Training Assistance System under Deep Learning Guided by Kinesiology Theory

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 26. Dez. 2023

Akzeptiert: 03. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0299

SchlüsselwörterConvolutional network, Long and short-term memory, Action recognition, Kinematic modeling, Training assistance system

© 2024 Chen Chen, published by Sciendo

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

Schlüsselwörter
Convolutional network, Long and short-term memory, Action recognition, Kinematic modeling, Training assistance system