{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T17:04:43Z","timestamp":1762794283522,"version":"build-2065373602"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Research on Sports and Medical Public Service and Health Promotion for Community Aged Groups in Liaoning Province under the Background of Physical and Medical Integration","award":["L23CTY004"],"award-info":[{"award-number":["L23CTY004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00138-025-01733-5","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T02:43:37Z","timestamp":1757558617000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the potential of deep learning techniques for analyzing athlete movements in competitive athletics sports"],"prefix":"10.1007","volume":"36","author":[{"given":"Yilun","family":"Gao","sequence":"first","affiliation":[]},{"given":"Jie","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Yuexin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"issue":"15","key":"1733_CR1","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.3390\/math10152794","volume":"10","author":"N Xu","year":"2022","unstructured":"Xu, N., Cui, X., Wang, X., Zhang, W., Zhao, T.: An intelligent athlete signal processing methodology for balance control ability assessment with multi-headed self-attention mechanism. Mathematics 10(15), 2794 (2022)","journal-title":"Mathematics"},{"issue":"13","key":"1733_CR2","doi-asserted-by":"publisher","first-page":"7611","DOI":"10.3390\/app13137611","volume":"13","author":"C Duan","year":"2023","unstructured":"Duan, C., Hu, B., Liu, W., Song, J.: Motion capture for sporting events based on graph convolutional neural networks and single target pose estimation algorithms. Appl. Sci. 13(13), 7611 (2023)","journal-title":"Appl. Sci."},{"key":"1733_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02060-2","author":"P Ong","year":"2022","unstructured":"Ong, P., Chong, T.K., Ong, K.M., Low, E.S.: Tracking of moving athlete from video sequences using flower pollination algorithm. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-021-02060-2","journal-title":"Vis. Comput."},{"issue":"5","key":"1733_CR4","doi-asserted-by":"publisher","first-page":"2422","DOI":"10.3390\/s23052422","volume":"23","author":"M Skublewska-Paszkowska","year":"2023","unstructured":"Skublewska-Paszkowska, M., Powroznik, P.: Temporal pattern attention for multivariate time series of tennis strokes classification. Sensors 23(5), 2422 (2023)","journal-title":"Sensors"},{"key":"1733_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ypmed.2023.107644","volume":"174","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Zhang, G.: CNN sensor based motion capture system application in basketball training and injury prevention. Prev. Med. 174, 107644 (2023)","journal-title":"Prev. Med."},{"issue":"3","key":"1733_CR6","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1007\/s00500-023-09565-z","volume":"28","author":"G Chen","year":"2024","unstructured":"Chen, G.: An interpretable composite CNN and GRU for fine-grained martial arts motion modeling using big data analytics and machine learning. Soft. Comput. 28(3), 2223\u20132243 (2024)","journal-title":"Soft. Comput."},{"issue":"23","key":"1733_CR7","doi-asserted-by":"publisher","first-page":"18093","DOI":"10.1007\/s00500-023-09215-4","volume":"27","author":"X Sun","year":"2023","unstructured":"Sun, X., Wang, Y., Khan, J.: Hybrid LSTM and GAN model for action recognition and prediction of lawn tennis sport activities. Soft. Comput. 27(23), 18093\u201318112 (2023)","journal-title":"Soft. Comput."},{"key":"1733_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2024.1452019","volume":"18","author":"H Chen","year":"2024","unstructured":"Chen, H., Yue, X.: Swimtrans Net: a multimodal robotic system for swimming action recognition driven via Swin-Transformer. Front. Neurorobot. 18, 1452019 (2024)","journal-title":"Front. Neurorobot."},{"key":"1733_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103954","volume":"97","author":"A AlShami","year":"2023","unstructured":"AlShami, A., Boult, T., Kalita, J.: Pose2Trajectory: using transformers on body pose to predict tennis player\u2019s trajectory. J. Vis. Commun. Image Represent. 97, 103954 (2023)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"2","key":"1733_CR10","doi-asserted-by":"publisher","first-page":"023021","DOI":"10.1117\/1.JEI.34.2.023021","volume":"34","author":"J Huang","year":"2025","unstructured":"Huang, J., Chen, L., Yao, Y., Xu, J., Wang, C.: Swin transfomer with structure from motion for group activity recognition. J. Electron. Imaging 34(2), 023021\u2013023021 (2025)","journal-title":"J. Electron. Imaging"},{"issue":"24","key":"1733_CR11","doi-asserted-by":"publisher","first-page":"19317","DOI":"10.1007\/s00500-023-09321-3","volume":"27","author":"X Li","year":"2023","unstructured":"Li, X., Ullah, R.: An image classification algorithm for football players\u2019 activities using deep neural network. Soft. Comput. 27(24), 19317\u201319337 (2023)","journal-title":"Soft. Comput."},{"issue":"3","key":"1733_CR12","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1504\/IJWET.2024.142213","volume":"19","author":"X Jiang","year":"2024","unstructured":"Jiang, X.: Identification of badminton players\u2019 swinging movements based on improved dense trajectory algorithm. Int. J. Web Eng. Technol. 19(3), 310\u2013329 (2024)","journal-title":"Int. J. Web Eng. Technol."},{"key":"1733_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2022.860981","volume":"16","author":"L Zhang","year":"2022","unstructured":"Zhang, L.: Applying deep learning-based human motion recognition system in sports competition. Front. neurorobot 16, 860981 (2022)","journal-title":"Front. neurorobot"},{"issue":"3","key":"1733_CR14","doi-asserted-by":"publisher","first-page":"033017","DOI":"10.1117\/1.JEI.30.3.033017","volume":"30","author":"J Liu","year":"2021","unstructured":"Liu, J., Che, Y.: Action recognition for sports video analysis using part-attention spatio-temporal graph convolutional network. J. Electron. Imaging 30(3), 033017\u2013033017 (2021)","journal-title":"J. Electron. Imaging"},{"issue":"3","key":"1733_CR15","doi-asserted-by":"publisher","first-page":"3528","DOI":"10.1007\/s11227-023-05611-7","volume":"80","author":"SB Khobdeh","year":"2024","unstructured":"Khobdeh, S.B., Yamaghani, M.R., Sareshkeh, S.K.: Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network. J. Supercomput. 80(3), 3528\u20133553 (2024)","journal-title":"J. Supercomput."},{"issue":"3","key":"1733_CR16","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.3390\/app15031543","volume":"15","author":"S Jung","year":"2025","unstructured":"Jung, S., Kim, H., Park, H., Choi, A.: Integrated AI system for real-time sports broadcasting: player behavior, game event recognition, and generative AI commentary in basketball games. Appl. Sci. 15(3), 1543 (2025)","journal-title":"Appl. Sci."},{"issue":"1","key":"1733_CR17","doi-asserted-by":"publisher","first-page":"20240050","DOI":"10.1515\/nleng-2024-0050","volume":"14","author":"P Chen","year":"2025","unstructured":"Chen, P., Peng, J.: Analysis of the sports action recognition model based on the LSTM recurrent neural network. Nonlinear Eng. 14(1), 20240050 (2025)","journal-title":"Nonlinear Eng."},{"issue":"1","key":"1733_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/s13677-023-00552-1","volume":"12","author":"L Xiao","year":"2023","unstructured":"Xiao, L., Cao, Y., Gai, Y., Khezri, E., Liu, J., Yang, M.: Recognizing sports activities from video frames using deformable convolution and adaptive multiscale features. J. Cloud Comput 12(1), 167 (2023)","journal-title":"J. Cloud Comput"},{"key":"1733_CR19","doi-asserted-by":"crossref","unstructured":"Ma, B.: Analysis of volleyball tactics and movements based on 3D spatio-temporal residual network.\u00a0Int. J. Image Graphics, p. 2750007 (2024).","DOI":"10.1142\/S0219467827500070"},{"key":"1733_CR20","doi-asserted-by":"publisher","first-page":"111281","DOI":"10.1109\/ACCESS.2023.3322455","volume":"11","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y.: Automatic shuttlecock motion recognition using deep learning. IEEE Access 11, 111281\u2013111291 (2023)","journal-title":"IEEE Access"},{"issue":"2","key":"1733_CR21","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1109\/TCSVT.2023.3288565","volume":"34","author":"R Li","year":"2023","unstructured":"Li, R., Bhanu, B.: Energy-motion features aggregation network for players\u2019 fine-grained action analysis in soccer videos. IEEE Trans. Circuits Syst. Video Technol. 34(2), 955\u2013972 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1733_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2024.1443432","volume":"18","author":"Q Lu","year":"2024","unstructured":"Lu, Q.: Sports-ACtrans Net: research on multimodal robotic sports action recognition driven via ST-GCN. Front. Neurorobot. 18, 1443432 (2024)","journal-title":"Front. Neurorobot."},{"key":"1733_CR23","unstructured":"The Olympic sports dataset used in this study is available at http:\/\/vision.stanford.edu\/Datasets\/OlympicSports\/."},{"key":"1733_CR24","unstructured":"The UCF Sports dataset is available at https:\/\/www.crcv.ucf.edu\/data\/UCF_Sports_Action.php."},{"issue":"8","key":"1733_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/s24082519","volume":"24","author":"A Enkhbat","year":"2024","unstructured":"Enkhbat, A., Shih, T.K., Cheewaprakobkit, P.: Human action recognition and note recognition: a deep learning approach using STA-GCN. Sensors 24(8), 2519 (2024)","journal-title":"Sensors"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01733-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-025-01733-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01733-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T17:00:47Z","timestamp":1762794047000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-025-01733-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":25,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1733"],"URL":"https:\/\/doi.org\/10.1007\/s00138-025-01733-5","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"24 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"118"}}