Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling

Traditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space...

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Autor principal:	Chen Chen
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/77239bce5b1d437399c4d72214bc8f1d
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spelling	oai:doaj.org-article:77239bce5b1d437399c4d72214bc8f1d2021-11-29T00:56:34ZResearch on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling1875-919X10.1155/2021/9545909https://doaj.org/article/77239bce5b1d437399c4d72214bc8f1d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9545909https://doaj.org/toc/1875-919XTraditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space-time preserving Fisher vector (FV) coding method and monocular motion video automatic scoring technology. Firstly, the gradient direction histogram and optical flow histogram are extracted to describe the motion posture and motion characteristics of the human body in motion video. After normalization and data dimensionality reduction based on the principal component analysis, the human motion feature vector with discrimination ability is obtained. Then, the spatiotemporal pyramid method is used to embed spatiotemporal features in FV coding to improve the ability to identify the correctness and coordination of human behavior. Finally, the linear model of different action classifications is established to determine the action score. In the key frame extraction experiment of the aerobics action video, the ST-FMP model improves the recognition accuracy of uncertain human parts in the flexible hybrid joint human model by about 15 percentage points, and the key frame extraction accuracy reaches 81%, which is better than the traditional algorithm. This algorithm is not only sensitive to human motion characteristics and human posture but also suitable for sports video annotation evaluation, which has a certain reference significance for improving the level of aerobics training.Chen ChenHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Chen Chen Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
description	Traditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space-time preserving Fisher vector (FV) coding method and monocular motion video automatic scoring technology. Firstly, the gradient direction histogram and optical flow histogram are extracted to describe the motion posture and motion characteristics of the human body in motion video. After normalization and data dimensionality reduction based on the principal component analysis, the human motion feature vector with discrimination ability is obtained. Then, the spatiotemporal pyramid method is used to embed spatiotemporal features in FV coding to improve the ability to identify the correctness and coordination of human behavior. Finally, the linear model of different action classifications is established to determine the action score. In the key frame extraction experiment of the aerobics action video, the ST-FMP model improves the recognition accuracy of uncertain human parts in the flexible hybrid joint human model by about 15 percentage points, and the key frame extraction accuracy reaches 81%, which is better than the traditional algorithm. This algorithm is not only sensitive to human motion characteristics and human posture but also suitable for sports video annotation evaluation, which has a certain reference significance for improving the level of aerobics training.
format	article
author	Chen Chen
author_facet	Chen Chen
author_sort	Chen Chen
title	Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
title_short	Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
title_full	Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
title_fullStr	Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
title_full_unstemmed	Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling
title_sort	research on aerobics training and evaluation method based on artificial intelligence-aided modeling
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/77239bce5b1d437399c4d72214bc8f1d
work_keys_str_mv	AT chenchen researchonaerobicstrainingandevaluationmethodbasedonartificialintelligenceaidedmodeling
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Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling

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