A Novel Deep Learning-Enabled Physical Education Mechanism
Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more...
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description | Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance. |
doi_str_mv | 10.1155/2022/8455164 |
format | Article |
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In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance.</description><identifier>ISSN: 1574-017X</identifier><identifier>EISSN: 1875-905X</identifier><identifier>DOI: 10.1155/2022/8455164</identifier><language>eng</language><publisher>Amsterdam: Hindawi</publisher><subject>Accuracy ; Algorithms ; Athletes ; Deep learning ; Error reduction ; Machine learning ; Neural networks ; Object recognition ; Olympic games ; Performance enhancement ; Sports ; Track & field ; Training ; Walking</subject><ispartof>Mobile information systems, 2022-03, Vol.2022, p.1-8</ispartof><rights>Copyright © 2022 Weiqi Wang and Jianan Jiang.</rights><rights>Copyright © 2022 Weiqi Wang and Jianan Jiang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-97494cfd790255cc4f80b87ef579c37fe0e6c8e3a031887f094de772a6da67c93</citedby><cites>FETCH-LOGICAL-c337t-97494cfd790255cc4f80b87ef579c37fe0e6c8e3a031887f094de772a6da67c93</cites><orcidid>0000-0001-9610-2391</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Lv, Jianhui</contributor><contributor>Jianhui Lv</contributor><creatorcontrib>Wang, Weiqi</creatorcontrib><creatorcontrib>Jiang, Jianan</creatorcontrib><title>A Novel Deep Learning-Enabled Physical Education Mechanism</title><title>Mobile information systems</title><description>Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Athletes</subject><subject>Deep learning</subject><subject>Error reduction</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Olympic games</subject><subject>Performance enhancement</subject><subject>Sports</subject><subject>Track & field</subject><subject>Training</subject><subject>Walking</subject><issn>1574-017X</issn><issn>1875-905X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp90EtPAjEQwPHGaCKiNz_AJh51Zbp9eyOIjwQfB024NaXbSsnSxRY0fHuXwNnTzOGXmeSP0CWGW4wZG1RQVQNJGcOcHqEeloKVCtj0uNuZoCVgMT1FZzkvADgQJnrobli8tj-uKe6dWxUTZ1IM8ascRzNrXF28z7c5WNMU43pjzTq0sXhxdm5iyMtzdOJNk93FYfbR58P4Y_RUTt4en0fDSWkJEetSCaqo9bVQUDFmLfUSZlI4z4SyRHgHjlvpiAGCpRQeFK2dEJXhteHCKtJHV_u7q9R-b1xe60W7SbF7qStOQTLGuezUzV7Z1OacnNerFJYmbTUGvaujd3X0oU7Hr_d8HmJtfsP_-g-EYWJD</recordid><startdate>20220308</startdate><enddate>20220308</enddate><creator>Wang, Weiqi</creator><creator>Jiang, Jianan</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-9610-2391</orcidid></search><sort><creationdate>20220308</creationdate><title>A Novel Deep Learning-Enabled Physical Education Mechanism</title><author>Wang, Weiqi ; Jiang, Jianan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-97494cfd790255cc4f80b87ef579c37fe0e6c8e3a031887f094de772a6da67c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Athletes</topic><topic>Deep learning</topic><topic>Error reduction</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Olympic games</topic><topic>Performance enhancement</topic><topic>Sports</topic><topic>Track & field</topic><topic>Training</topic><topic>Walking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Weiqi</creatorcontrib><creatorcontrib>Jiang, Jianan</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mobile information systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Weiqi</au><au>Jiang, Jianan</au><au>Lv, Jianhui</au><au>Jianhui Lv</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Deep Learning-Enabled Physical Education Mechanism</atitle><jtitle>Mobile information systems</jtitle><date>2022-03-08</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1574-017X</issn><eissn>1875-905X</eissn><abstract>Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance.</abstract><cop>Amsterdam</cop><pub>Hindawi</pub><doi>10.1155/2022/8455164</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-9610-2391</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Athletes Deep learning Error reduction Machine learning Neural networks Object recognition Olympic games Performance enhancement Sports Track & field Training Walking |
title | A Novel Deep Learning-Enabled Physical Education Mechanism |
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