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Artificial Intelligence Technology in Basketball Training Action Recognition
The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272734/ https://www.ncbi.nlm.nih.gov/pubmed/35832349 http://dx.doi.org/10.3389/fnbot.2022.819784 |
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author | Cheng, Yao Liang, Xiaojun Xu, Yi Kuang, Xin |
author_facet | Cheng, Yao Liang, Xiaojun Xu, Yi Kuang, Xin |
author_sort | Cheng, Yao |
collection | PubMed |
description | The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level. |
format | Online Article Text |
id | pubmed-9272734 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92727342022-07-12 Artificial Intelligence Technology in Basketball Training Action Recognition Cheng, Yao Liang, Xiaojun Xu, Yi Kuang, Xin Front Neurorobot Neuroscience The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level. Frontiers Media S.A. 2022-06-27 /pmc/articles/PMC9272734/ /pubmed/35832349 http://dx.doi.org/10.3389/fnbot.2022.819784 Text en Copyright © 2022 Cheng, Liang, Xu and Kuang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Cheng, Yao Liang, Xiaojun Xu, Yi Kuang, Xin Artificial Intelligence Technology in Basketball Training Action Recognition |
title | Artificial Intelligence Technology in Basketball Training Action Recognition |
title_full | Artificial Intelligence Technology in Basketball Training Action Recognition |
title_fullStr | Artificial Intelligence Technology in Basketball Training Action Recognition |
title_full_unstemmed | Artificial Intelligence Technology in Basketball Training Action Recognition |
title_short | Artificial Intelligence Technology in Basketball Training Action Recognition |
title_sort | artificial intelligence technology in basketball training action recognition |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272734/ https://www.ncbi.nlm.nih.gov/pubmed/35832349 http://dx.doi.org/10.3389/fnbot.2022.819784 |
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