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<p>Based on the fact that sports culture, as an important part of social culture, is also one of the key contents of the construction of social-ecological civilization, this paper proposes a smart sports training ecosystem based on the integration and development of artificial intelligence and sports culture sports training. Investigating the Evolution of Sports Culture and Athletic Training through the Lens of Intelligent Sports The foreground and background separation is used to obtain the user’s skeletal features, and the initial skeletal feature data is filtered for the abnormal data that will cause the skeletal data-position deviation, followed by optimizing the monitoring and guidance functions of the smart sports training ecosystem through the Hidden Markov-based motion recognition, and an example analysis is conducted to optimize the operation of the smart sports training ecosystem. The results show that: in the angle detection results during running, the ankle joint angle change interval of the participants in the test is [85.44°, 123.699°], and the average value is 404.5695°. The initial change range of ankle joint angle of the athletes at the moment of landing is 105.73°~108.98°, which confirms that the intelligent sports training ecosystem can provide users with high-quality interactive experience and real-time feedback on the changes of their own sports status. This study can improve the athletic ability and sports culture literacy of college students, and provide a theoretical basis and practical reference for the development path of sports culture and sports training under the perspective of smart sports.</p>
</abstract>

Based on the fact that sports culture, as an important part of social culture, is also one of the key contents of the construction of social-ecological civilization, this paper proposes a smart sports training ecosystem based on the integration and development of artificial intelligence and sports culture sports training. Investigating the Evolution of Sports Culture and Athletic Training through the Lens of Intelligent Sports The foreground and background separation is used to obtain the user’s skeletal features, and the initial skeletal feature data is filtered for the abnormal data that will cause the skeletal data-position deviation, followed by optimizing the monitoring and guidance functions of the smart sports training ecosystem through the Hidden Markov-based motion recognition, and an example analysis is conducted to optimize the operation of the smart sports training ecosystem. The results show that: in the angle detection results during running, the ankle joint angle change interval of the participants in the test is [85.44°, 123.699°], and the average value is 404.5695°. The initial change range of ankle joint angle of the athletes at the moment of landing is 105.73°~108.98°, which confirms that the intelligent sports training ecosystem can provide users with high-quality interactive experience and real-time feedback on the changes of their own sports status. This study can improve the athletic ability and sports culture literacy of college students, and provide a theoretical basis and practical reference for the development path of sports culture and sports training under the perspective of smart sports.

Research on the Development Path of Sports Culture and Athletic Training in the Perspective of Intelligent Sports

Applied Mathematics and Nonlinear Sciences

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

Based on the fact that sports culture, as an important part of social culture, is also one of the key contents of the construction of social-ecological...

Research on the Development Path of Sports Culture and Athletic Training in the Perspective of Intelligent Sports

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0223

Schlüsselwörter
Hidden Markov, Skeletal features, Filter processing, Sports recognition, Training system

© 2024 Cheng Guo et al., published by Sciendo

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

Research on the Development Path of Sports Culture and Athletic Training in the Perspective of Intelligent Sports

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0223

SchlüsselwörterHidden Markov, Skeletal features, Filter processing, Sports recognition, Training system

© 2024 Cheng Guo et al., published by Sciendo

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

Schlüsselwörter
Hidden Markov, Skeletal features, Filter processing, Sports recognition, Training system