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<p>To solve the problems of low completeness of data collection and poor accuracy of evaluation of traditional sports training movement completion, this paper proposes an artificial intelligence-based DTW pose matching algorithm. Firstly, the sports postures are matched and evaluated for movements. Then define the limb angle deviation factor and calculate the deviation degree of each limb angle, output the limbs with a deviation degrees greater than the set standard, identify the deviated limbs that affect the overall movement standard, and realize the evaluation and result in the feedback of basic movements in sports training. Under the three feature extraction methods, the accuracy of the statistical value feature extraction algorithm, LR feature extraction algorithm and integrated feature extraction algorithm of the DTW poses matching algorithm were 91.12%, 96.84% and 96.54%, respectively. The accuracy rates of LDA, KNN, NB, CART and DTW algorithms were 25%, 43%, 32%, 33%, and 72%, respectively, when the other four models were used to compare the accuracy rates with the DTW pose matching algorithm. Among them, the accuracy of the DTW algorithm is significantly higher than the other four algorithms. Therefore, the artificial intelligence-based sports training management model proposed in this paper can significantly improve the current problems, such as difficulty in assessing the accuracy of sports movement training, inefficient supervision of teachers’ guidance to students, and difficulty for students to improve their movements directly, which has strong practical significance and application value.</p>
</abstract>

To solve the problems of low completeness of data collection and poor accuracy of evaluation of traditional sports training movement completion, this paper proposes an artificial intelligence-based DTW pose matching algorithm. Firstly, the sports postures are matched and evaluated for movements. Then define the limb angle deviation factor and calculate the deviation degree of each limb angle, output the limbs with a deviation degrees greater than the set standard, identify the deviated limbs that affect the overall movement standard, and realize the evaluation and result in the feedback of basic movements in sports training. Under the three feature extraction methods, the accuracy of the statistical value feature extraction algorithm, LR feature extraction algorithm and integrated feature extraction algorithm of the DTW poses matching algorithm were 91.12%, 96.84% and 96.54%, respectively. The accuracy rates of LDA, KNN, NB, CART and DTW algorithms were 25%, 43%, 32%, 33%, and 72%, respectively, when the other four models were used to compare the accuracy rates with the DTW pose matching algorithm. Among them, the accuracy of the DTW algorithm is significantly higher than the other four algorithms. Therefore, the artificial intelligence-based sports training management model proposed in this paper can significantly improve the current problems, such as difficulty in assessing the accuracy of sports movement training, inefficient supervision of teachers’ guidance to students, and difficulty for students to improve their movements directly, which has strong practical significance and application value.

Analysis of the current situation and development countermeasures of college sports training and management based on the background of artificial intelligence

Applied Mathematics and Nonlinear Sciences

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

To solve the problems of low completeness of data collection and poor accuracy of evaluation of traditional sports training movement completion, this paper...

Analysis of the current situation and development countermeasures of college sports training and management based on the background of artificial intelligence

Pubblicato online: 02 giu 2023

Pagine: -

Ricevuto: 14 giu 2022

Accettato: 17 ott 2022

DOI: https://doi.org/10.2478/amns.2023.1.00161

Parole chiave
College sports, Sports training and management, Artificial intelligence, Gesture matching

© 2023 Mingqian Li et al., published by Sciendo

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

Analysis of the current situation and development countermeasures of college sports training and management based on the background of artificial intelligence

Pubblicato online: 02 giu 2023

Pagine: -

Ricevuto: 14 giu 2022

Accettato: 17 ott 2022

DOI: https://doi.org/10.2478/amns.2023.1.00161

Parole chiaveCollege sports, Sports training and management, Artificial intelligence, Gesture matching

© 2023 Mingqian Li et al., published by Sciendo

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

Parole chiave
College sports, Sports training and management, Artificial intelligence, Gesture matching