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<p>The continuous progress of science and technology promotes the continuous development of artificial intelligence, and digital intelligence empowerment has gradually become a focus topic of widespread social concern. Considering that traditional sports training is difficult to meet the higher requirements put forward by athletes on training methods, training load, and training equipment, this study combines artificial intelligence technology to explore the path of skill enhancement. In this paper, based on the skeletal key points for athlete training action recognition, the improved DHRNet network is optimized for the detection of skeletal key points and then for the complexity of the athlete’s training state, the occlusion of the skeletal points and the repair of motion information are carried out. On this basis, we construct an artificial intelligence-assisted training system composed of a video image input function system, action recognition function system, and behavior analysis function system. After completing the design, the core module of the system, the motion recognition system, was tested. The average recognition success rates of basic and sport movements were between 90.8~95.6% and 84.5~96.5%, respectively. The overall recognition correctness rate remained above 90% in the actual application test, which can meet the training needs of athletes. The average score for all abilities of the athletes using the AI-supported system is 85.242, which is 6.21 higher than that of the traditional training method. The AI-assisted system is of great help to athletes’ training, and it also enables the early realization of intelligent training.</p>
</abstract>

The continuous progress of science and technology promotes the continuous development of artificial intelligence, and digital intelligence empowerment has gradually become a focus topic of widespread social concern. Considering that traditional sports training is difficult to meet the higher requirements put forward by athletes on training methods, training load, and training equipment, this study combines artificial intelligence technology to explore the path of skill enhancement. In this paper, based on the skeletal key points for athlete training action recognition, the improved DHRNet network is optimized for the detection of skeletal key points and then for the complexity of the athlete’s training state, the occlusion of the skeletal points and the repair of motion information are carried out. On this basis, we construct an artificial intelligence-assisted training system composed of a video image input function system, action recognition function system, and behavior analysis function system. After completing the design, the core module of the system, the motion recognition system, was tested. The average recognition success rates of basic and sport movements were between 90.8~95.6% and 84.5~96.5%, respectively. The overall recognition correctness rate remained above 90% in the actual application test, which can meet the training needs of athletes. The average score for all abilities of the athletes using the AI-supported system is 85.242, which is 6.21 higher than that of the traditional training method. The AI-assisted system is of great help to athletes’ training, and it also enables the early realization of intelligent training.

Artificial Intelligence-Assisted Training and Skill Enhancement Strategies for Athletes

Applied Mathematics and Nonlinear Sciences

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

The continuous progress of science and technology promotes the continuous development of artificial intelligence, and digital intelligence empowerment has...

Artificial Intelligence-Assisted Training and Skill Enhancement Strategies for Athletes

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 02. Apr. 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2074

Schlüsselwörter
Action recognition, Improved DHRNet network, Skeletal keypoint detection, Artificial intelligence, Assisted training system

© 2024 Li Lin et al., published by Sciendo

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

Artificial Intelligence-Assisted Training and Skill Enhancement Strategies for Athletes

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 02. Apr. 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2074

SchlüsselwörterAction recognition, Improved DHRNet network, Skeletal keypoint detection, Artificial intelligence, Assisted training system

© 2024 Li Lin et al., published by Sciendo

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

Schlüsselwörter
Action recognition, Improved DHRNet network, Skeletal keypoint detection, Artificial intelligence, Assisted training system