{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:29:50Z","timestamp":1769711390862,"version":"3.49.0"},"reference-count":23,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>In the past decade, people\u2019s life is getting better and better, and the attention to sports competition is also increasing. In the current information age, sports and athletes\u2019 data are very important, especially team football. In college, football coaches can use the data to analyze the situation of college football players and opposing players to better specify the corresponding tactics to win the game. However, at present, most of the data results need to be manually recorded and counted on the spot or after the game. In the process of statistics, Zhou Jing will inevitably have omissions and other problems. For this problem, a method based on space-time graph convolution. In the process, machine vision and motion recognition methods are combined, and the joint movements of different football players are extracted through the pose estimation method to obtain motion recognition results. To ented the methods on the KTH dataset. The results showed that the football motion recognition using the research method reached 98% on the dataset, which significantly improved the accuracy of nearly 5% over the existing state-of-the-art methods. At the same time, the accuracy rate of football movements was less than 5%. This means that the research method can effectively identify football sports, and can be widely used in other fields, and promote the development of human movement recognition in human-computer interaction and smart city and other fields.<\/jats:p>","DOI":"10.3233\/jifs-230890","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T13:21:35Z","timestamp":1685107295000},"page":"9095-9108","source":"Crossref","is-referenced-by-count":3,"title":["Motion recognition method of college football teaching based on convolution of spatio-temporal graph"],"prefix":"10.1177","volume":"45","author":[{"given":"Chun","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Physical Education, Tangshan Normal University, TangShan, China"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Physical Education, Tangshan Normal University, TangShan, China"}]},{"given":"Ningning","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Physical Education, Hengshui University, Hengshui, China"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-230890_ref1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.13195\/j.kzyjc.2020.1445","article-title":"Traffic flow prediction based on spatio-temporal graph convolution cyclic neural network","volume":"37","author":"Gu","year":"2022","journal-title":"Control and Decision"},{"issue":"S02","key":"10.3233\/JIFS-230890_ref2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.11896\/jsjkx.201200184","article-title":"Traffic forecasting model based on convolution network of two-way information spatio-temporal graph","volume":"48","author":"Kang","year":"2021","journal-title":"Computer Science"},{"issue":"3","key":"10.3233\/JIFS-230890_ref3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3969\/j.issn.1000-386x.2020.03.033","article-title":"3D human behavior recognition based on residual spatio-temporal graph convolution network","volume":"37","author":"Guan","year":"2020","journal-title":"Computer Application and Software"},{"issue":"19","key":"10.3233\/JIFS-230890_ref5","doi-asserted-by":"publisher","first-page":"68","DOI":"10.3969\/j.issn.1007-1423.2021.19.012","article-title":"Pedestrian trajectory prediction based on scene and pedestrian interaction force","author":"Peng","year":"2021","journal-title":"Modern Computer"},{"issue":"4","key":"10.3233\/JIFS-230890_ref6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.12677\/CSA.2021.114080","article-title":"Real-time fall detection method based on lightweight human posture estimation and graph convolution","volume":"11","author":"He","year":"2021","journal-title":"Computer Science and Application"},{"issue":"6","key":"10.3233\/JIFS-230890_ref7","doi-asserted-by":"publisher","first-page":"806","DOI":"10.14135\/j.cnki.1006-3080.20210625001","article-title":"Adaptive graph convolution and LSTM behavior recognition based on skeleton","volume":"48","author":"Mao","year":"2022","journal-title":"Journal of East China University of Science and Technology (Natural Science Edition)"},{"key":"10.3233\/JIFS-230890_ref8","doi-asserted-by":"publisher","first-page":"103723","DOI":"10.1016\/j.micpro.2020.103723","article-title":"Internet of things driven physical activity recognition system for physical education","volume":"81","author":"Wang","year":"2021","journal-title":"Microprocessors and Microsystems"},{"key":"10.3233\/JIFS-230890_ref9","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.comnet.2019.01.028","article-title":"Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM","volume":"151","author":"Fenil","year":"2019","journal-title":"Computer Networks"},{"key":"10.3233\/JIFS-230890_ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13369-021-05895-y","article-title":"Human\u2013computer interaction on IoT-based college physical education","volume":"48","author":"Che","year":"2021","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"7","key":"10.3233\/JIFS-230890_ref11","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3724\/SP.J.1089.2021.18640","article-title":"Human motion recognition method combining multi-attention mechanism and spatio-temporal graph convolution network","volume":"33","author":"Li","year":"2021","journal-title":"Journal of Computer Aided Design and Graphics"},{"issue":"10","key":"10.3233\/JIFS-230890_ref12","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3969\/j.issn.1673-629X.2021.10.012","article-title":"Attitude classification of characters in video based on spatio-temporal graph convolution network","volume":"31","author":"Zhang","year":"2021","journal-title":"Computer Technology and Development"},{"issue":"06n08","key":"10.3233\/JIFS-230890_ref13","doi-asserted-by":"publisher","first-page":"2140016","DOI":"10.1142\/S0218213021400169","article-title":"Internet of things framework in athletics physical teaching system and health monitoring","volume":"30","author":"Zhen","year":"2021","journal-title":"International Journal on Artificial Intelligence Tools"},{"issue":"2","key":"10.3233\/JIFS-230890_ref14","doi-asserted-by":"publisher","first-page":"241","DOI":"10.16798\/j.issn.1003-0530.2022.02.003","article-title":"Bone motion recognition based on multi-partition spatio-temporal graph convolution network","volume":"38","author":"Xin","year":"2022","journal-title":"Signal Processing"},{"issue":"2","key":"10.3233\/JIFS-230890_ref15","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s11227-021-03957-4","article-title":"A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes","volume":"78","author":"Bilal","year":"2022","journal-title":"The Journal of Supercomputing"},{"issue":"21","key":"10.3233\/JIFS-230890_ref16","doi-asserted-by":"publisher","first-page":"14441","DOI":"10.1007\/s00521-021-06084-6","article-title":"A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data","volume":"33","author":"Ma","year":"2021","journal-title":"Neural Computing and Applications"},{"issue":"07","key":"10.3233\/JIFS-230890_ref17","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.11834\/jig.200510","article-title":"Multi-person interactive behavior recognition based on spatio-temporal graph convolution","volume":"26","author":"Cheng","year":"2021","journal-title":"Chinese Journal of Image and Graphics"},{"issue":"3","key":"10.3233\/JIFS-230890_ref18","doi-asserted-by":"publisher","first-page":"780","DOI":"10.19734\/j.issn.1001-3695.2021.08.0361","article-title":"A traffic flow prediction method based on multi-spatio-temporal graph convolution network","volume":"39","author":"Shi","year":"2022","journal-title":"Computer Application Research"},{"issue":"2","key":"10.3233\/JIFS-230890_ref19","first-page":"128","article-title":"Research on traffic accident prediction based on spatio-temporal graph convolution network","volume":"50","author":"Liu","year":"2022","journal-title":"Journal of Zhejiang University of Technology"},{"issue":"5","key":"10.3233\/JIFS-230890_ref20","doi-asserted-by":"publisher","first-page":"3050","DOI":"10.1109\/TCSVT.2021.3098839","article-title":"Multi-stream interaction networks for human action recognition","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"7","key":"10.3233\/JIFS-230890_ref21","doi-asserted-by":"publisher","first-page":"112","DOI":"10.11896\/jsjkx.201000089","article-title":"Multi-task spatio-temporal graph convolution network for taxi no-load time prediction","volume":"48","author":"Song","year":"2021","journal-title":"Computer Science"},{"issue":"S02","key":"10.3233\/JIFS-230890_ref22","doi-asserted-by":"publisher","first-page":"130","DOI":"10.11896\/jsjkx.201200205","article-title":"Human motion recognition based on causal relationship and spatio-temporal graph convolution network","volume":"48","author":"Ye","year":"2021","journal-title":"Computer Science"},{"issue":"5","key":"10.3233\/JIFS-230890_ref23","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.13700\/j.bh.1001-5965.2020.0095","article-title":"Unmanned aerial vehicle network intrusion detection method based on spatio-temporal graph convolution network","volume":"47","author":"Chen","year":"2021","journal-title":"Journal of Beihang University"},{"issue":"05","key":"10.3233\/JIFS-230890_ref24","doi-asserted-by":"publisher","first-page":"214","DOI":"10.15888\/j.cnki.csa.007911","article-title":"Forecast of network car demand based on multi-graph spatio-temporal convolution neural network","volume":"30","author":"Zhou","year":"2021","journal-title":"Computer System Application"}],"updated-by":[{"DOI":"10.3233\/jifs-219434","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"record-id":"65270"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-230890","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:49:05Z","timestamp":1769672945000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-230890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":23,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-230890","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,4]]}}}