Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis
In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.663359/full |
| _version_ | 1818848397339131904 |
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| author | Jiatian Liu |
| author_facet | Jiatian Liu |
| author_sort | Jiatian Liu |
| collection | DOAJ |
| description | In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports. |
| first_indexed | 2024-12-19T06:16:41Z |
| format | Article |
| id | doaj.art-3b9906d101a9400cae5865463b856dca |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-19T06:16:41Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-3b9906d101a9400cae5865463b856dca2022-12-21T20:32:50ZengFrontiers Media S.A.Frontiers in Psychology1664-10782021-06-011210.3389/fpsyg.2021.663359663359Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports AnalysisJiatian LiuIn order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.https://www.frontiersin.org/articles/10.3389/fpsyg.2021.663359/fullhuman action recognitionconvolutional neural networkimage recognitionsports analysissports psychology |
| spellingShingle | Jiatian Liu Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis Frontiers in Psychology human action recognition convolutional neural network image recognition sports analysis sports psychology |
| title | Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis |
| title_full | Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis |
| title_fullStr | Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis |
| title_full_unstemmed | Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis |
| title_short | Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis |
| title_sort | convolutional neural network based human movement recognition algorithm in sports analysis |
| topic | human action recognition convolutional neural network image recognition sports analysis sports psychology |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.663359/full |
| work_keys_str_mv | AT jiatianliu convolutionalneuralnetworkbasedhumanmovementrecognitionalgorithminsportsanalysis |