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Children's Motor Intelligence Evaluation System Based on Multi-data Fusion

Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's spo...

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Detalles Bibliográficos
Autor principal: Zhao, Sihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467716/
https://www.ncbi.nlm.nih.gov/pubmed/36118946
http://dx.doi.org/10.1155/2022/3775800
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author Zhao, Sihong
author_facet Zhao, Sihong
author_sort Zhao, Sihong
collection PubMed
description Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children's physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children's exercise evaluation. This paper studied the children's motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children's autonomous sports games and determine the first-level indicators in the children's sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children's exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children's movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children's comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children's sports evaluation results are more average, which can enhance children's sports confidence and promote children's effective exercise.
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spelling pubmed-94677162022-09-16 Children's Motor Intelligence Evaluation System Based on Multi-data Fusion Zhao, Sihong Contrast Media Mol Imaging Research Article Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children's physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children's exercise evaluation. This paper studied the children's motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children's autonomous sports games and determine the first-level indicators in the children's sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children's exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children's movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children's comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children's sports evaluation results are more average, which can enhance children's sports confidence and promote children's effective exercise. Hindawi 2022-09-05 /pmc/articles/PMC9467716/ /pubmed/36118946 http://dx.doi.org/10.1155/2022/3775800 Text en Copyright © 2022 Sihong Zhao. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhao, Sihong
Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title_full Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title_fullStr Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title_full_unstemmed Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title_short Children's Motor Intelligence Evaluation System Based on Multi-data Fusion
title_sort children's motor intelligence evaluation system based on multi-data fusion
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467716/
https://www.ncbi.nlm.nih.gov/pubmed/36118946
http://dx.doi.org/10.1155/2022/3775800
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