{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T16:19:09Z","timestamp":1794586749127,"version":"4.9.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sports Med - Open"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Human Pose Estimation (HPE) has gained increasing attention in sports research due to advancements in Deep Learning (DL) movement skills, which enable precise joint localization in 2D and 3D visual data. DL-based HPE facilitates non-invasive analysis of movement patterns in real-world settings, providing actionable insights for training, performance optimisation, and injury prevention. This systematic review examines the application of DL-based HPE in sports, focusing on the availability and accessibility of training datasets, reproducibility for practitioners, and the influence of human factors. The review also offers recommendations to guide future research and applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search following PRISMA guidelines was conducted across four databases\u2014Scopus, Web of Science, the Association for Computing Machinery, and SPORTDiscus, yielding 371 articles. Two independent reviewers applied inclusion and exclusion criteria to identify relevant studies, with a third reviewer resolving conflicts. Key aspects analysed included the scope of DL-based HPE applications, dataset characteristics, and algorithmic approaches. A supplementary search was conducted to include contemporary literature published since the initial search date. Data were synthesized descriptively, focusing on trends and limitations in the evidence base.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The identified applications of DL-based HPE in sports were categorized into four domains: movement skill analysis, action recognition, augmented coaching tools, and officiating support. Most studies relied on private datasets for algorithm training and validation, limiting reproducibility and generalizability. Bespoke multi-model algorithms were the most common approach, and single person pose estimation predominated. Despite its potential, the lack of open datasets and standardized practices poses challenges for broader adoption and practical implementation. These findings were echoed in the supplementary search which added no significant findings outside what previous studies had demonstrated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This review represents the first systematic evaluation of DL-based HPE from a sports science perspective, offering practical guidance for future research and applications. The findings highlight the need for open, standardized datasets and reproducible methodologies to advance the field. Future research should address these limitations while exploring innovative applications to maximize the impact of DL-based HPE in sports science.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40798-025-00953-3","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:50:54Z","timestamp":1765371054000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["The Application of Deep Learning Human Pose Estimation in Sport: A Systematic Review"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5766-4997","authenticated-orcid":false,"given":"Cavan","family":"Aulton","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6361-8143","authenticated-orcid":false,"given":"Lois","family":"Wakili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4506-9370","authenticated-orcid":false,"given":"Ben William","family":"Strafford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1398-6123","authenticated-orcid":false,"given":"Keith","family":"Davids","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6512-0084","authenticated-orcid":false,"given":"Chuang-Yuan","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"953_CR1","doi-asserted-by":"publisher","first-page":"829445","DOI":"10.3389\/fnbot.2022.829445","volume":"16","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Tang H, Zereg F, Xu D. Application of deep convolution network algorithm in sports video hot spot detection. Front Neurorobot. 2022;16:829445. https:\/\/doi.org\/10.3389\/fnbot.2022.829445.","journal-title":"Front Neurorobot"},{"issue":"18","key":"953_CR2","doi-asserted-by":"publisher","first-page":"5996","DOI":"10.3390\/s21185996","volume":"21","author":"A Badiola-Bengoa","year":"2021","unstructured":"Badiola-Bengoa A, Mendez-Zorrilla A. A systematic review of the application of camera-based human pose estimation in the field of sport and physical exercise. Sensors. 2021;21(18):5996.","journal-title":"Sensors"},{"key":"953_CR3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2012.13392","author":"C Zheng","year":"2022","unstructured":"Zheng C, Wu W, Chen C, Yang T, Zhu S, Shen J, et al. Deep learning-based human pose estimation: a survey. J ACM. 2022. https:\/\/doi.org\/10.48550\/arXiv.2012.13392.","journal-title":"J ACM"},{"issue":"7553","key":"953_CR4","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"key":"953_CR5","doi-asserted-by":"crossref","unstructured":"Chu X, Ouyang W, Li H, Wang X. Structured Feature Learning for Pose Estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE. 2016; 4715\u201323. http:\/\/ieeexplore.ieee.org\/document\/7780879\/. Accessed 22 Aug 2025.","DOI":"10.1109\/CVPR.2016.510"},{"issue":"2","key":"953_CR6","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/02701367.2013.784846","volume":"84","author":"RM Eime","year":"2013","unstructured":"Eime RM, Harvey JT, Sawyer NA, Craike MJ, Symons CM, Polman RCJ, et al. Understanding the contexts of adolescent female participation in sport and physical activity. Res Q Exerc Sport. 2013;84(2):157\u201366.","journal-title":"Res Q Exerc Sport"},{"issue":"1","key":"953_CR7","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s00530-022-00980-0","volume":"29","author":"S Dubey","year":"2023","unstructured":"Dubey S, Dixit M. A comprehensive survey on human pose estimation approaches. Multimedia Syst. 2023;29(1):167\u201395.","journal-title":"Multimedia Syst"},{"key":"953_CR8","doi-asserted-by":"publisher","unstructured":"Akan S, Varl\u0131 S. Reidentifying Soccer Players in Broadcast Videos Using Body FeatureAlignment Based on Pose. In: Proceedings of the Association for Computing Machinery. 2023;440\u20134. https:\/\/doi.org\/10.1145\/3603781.3603860.","DOI":"10.1145\/3603781.3603860"},{"key":"953_CR9","doi-asserted-by":"publisher","first-page":"105918","DOI":"10.1016\/j.ijsu.2021.105918","volume":"88","author":"C Sohrabi","year":"2021","unstructured":"Sohrabi C, Franchi T, Mathew G, Kerwan A, Nicola M, Griffin M, et al. PRISMA 2020 statement: what\u2019s new and the importance of reporting guidelines. Int J Surg. 2021;88:105918.","journal-title":"Int J Surg"},{"key":"953_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.09.002","volume":"152","author":"N Sarafianos","year":"2016","unstructured":"Sarafianos N, Boteanu B, Ionescu B, Kakadiaris IA. 3D human pose estimation: a review of the literature and analysis of covariates. Comput Vis Image Underst. 2016;152:1\u201320.","journal-title":"Comput Vis Image Underst"},{"key":"953_CR11","doi-asserted-by":"publisher","first-page":"103225","DOI":"10.1016\/j.cviu.2021.103225","volume":"210","author":"J Wang","year":"2021","unstructured":"Wang J, Tan S, Zhen X, Xu S, Zheng F, He Z, et al. Deep 3D human pose estimation: a review. Comput Vis Image Underst. 2021;210:103225.","journal-title":"Comput Vis Image Underst"},{"key":"953_CR12","doi-asserted-by":"crossref","unstructured":"Torralba A, Efros AA. Unbiased look at dataset bias. In: CVPR 2011. Colorado Springs, CO, USA: IEEE. 2011; 1521\u20138. http:\/\/ieeexplore.ieee.org\/document\/5995347\/. Accessed 19 Mar 2024.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"953_CR13","doi-asserted-by":"publisher","first-page":"103275","DOI":"10.1016\/j.cviu.2021.103275","volume":"212","author":"Y Desmarais","year":"2021","unstructured":"Desmarais Y, Mottet D, Slangen P, Montesinos P. A review of 3D human pose estimation algorithms for markerless motion capture. Comput Vis Image Underst. 2021;212:103275.","journal-title":"Comput Vis Image Underst"},{"issue":"1","key":"953_CR14","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/THMS.2022.3219242","volume":"53","author":"G Lan","year":"2023","unstructured":"Lan G, Wu Y, Hu F, Hao Q. Vision-based human pose estimation via deep learning: a survey. IEEE Trans Hum-Mach Syst. 2023;53(1):253\u201368.","journal-title":"IEEE Trans Hum-Mach Syst"},{"issue":"4","key":"953_CR15","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1519\/JSC.0b013e3181ad3448","volume":"24","author":"J Slawinski","year":"2010","unstructured":"Slawinski J, Bonnefoy A, Lev\u00eaque JM, Ontanon G, Riquet A, Dumas R, et al. Kinematic and kinetic comparisons of elite and well-trained sprinters during sprint start. J Strength Cond Res. 2010;24(4):896.","journal-title":"J Strength Cond Res"},{"issue":"10","key":"953_CR16","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1080\/026404102320675602","volume":"20","author":"MD Hughes","year":"2002","unstructured":"Hughes MD, Bartlett RM. The use of performance indicators in performance analysis. J Sports Sci. 2002;20(10):739\u201354.","journal-title":"J Sports Sci"},{"issue":"2","key":"953_CR17","first-page":"103","volume":"229","author":"JL McNitt-Gray","year":"2015","unstructured":"McNitt-Gray JL, Sand K, Ramos C, Peterson T, Held L, Brown K. Using technology and engineering to facilitate skill acquisition and improvements in performance. Proc Inst Mech Eng Part P J Sports Eng Technol. 2015;229(2):103\u201315.","journal-title":"Proc Inst Mech Eng Part P J Sports Eng Technol"},{"issue":"2","key":"953_CR18","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1080\/02640410310001641502","volume":"22","author":"W Helsen","year":"2004","unstructured":"Helsen W, Bultynck JB. Physical and perceptual-cognitive demands of top-class refereeing in association football. J Sports Sci. 2004;22(2):179\u201389.","journal-title":"J Sports Sci"},{"key":"953_CR19","doi-asserted-by":"publisher","unstructured":"Bachmann R, Sporri J, Fua P, Rhodin H. Motion Capture from Pan-Tilt Cameras with Unknown Orientation. In: 3DV 2019, 2019; 308\u201317. https:\/\/doi.org\/10.1109\/3DV.2019.00042.","DOI":"10.1109\/3DV.2019.00042"},{"issue":"24","key":"953_CR20","first-page":"1","volume":"10","author":"MM Baclig","year":"2020","unstructured":"Baclig MM, Ergezinger N, Mei Q, G\u00fcl M, Adeeb S, Westover L. A deep learning and computer vision based multi-player tracker for squash. Appl Sci (Switzerland). 2020;10(24):1\u201316.","journal-title":"Appl Sci (Switzerland)"},{"key":"953_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/app13137611","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. 2023. https:\/\/doi.org\/10.3390\/app13137611.","journal-title":"Appl Sci"},{"key":"953_CR22","doi-asserted-by":"publisher","DOI":"10.3390\/s23042364","author":"N Giulietti","year":"2023","unstructured":"Giulietti N, Caputo A, Chiariotti P, Castellini P. SwimmerNET: underwater 2D swimmer pose estimation exploiting fully convolutional neural networks. Sensors. 2023. https:\/\/doi.org\/10.3390\/s23042364.","journal-title":"Sensors"},{"issue":"4","key":"953_CR23","doi-asserted-by":"publisher","first-page":"2518","DOI":"10.1007\/s10489-020-01918-7","volume":"51","author":"D Groos","year":"2021","unstructured":"Groos D, Ramampiaro H, Ihlen EA. EfficientPose: scalable single-person pose estimation. Appl Intell. 2021;51(4):2518\u201333. https:\/\/doi.org\/10.1007\/s10489-020-01918-7.","journal-title":"Appl Intell"},{"key":"953_CR24","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4102294","author":"X Hu","year":"2021","unstructured":"Hu X. Football player posture detection method combining foreground detection and neural networks. Sci Program. 2021. https:\/\/doi.org\/10.1155\/2021\/4102294.","journal-title":"Sci Program"},{"key":"953_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/s21103368","author":"M Javadiha","year":"2021","unstructured":"Javadiha M, Andujar C, Lacasa E, Ric A, Susin A. Estimating player positions from padel high-angle videos: accuracy comparison of recent computer vision methods. Sensors (Basel). 2021. https:\/\/doi.org\/10.3390\/s21103368.","journal-title":"Sensors (Basel)"},{"key":"953_CR26","doi-asserted-by":"publisher","unstructured":"Jiang Z, Ji H, Menaker S, Hwang JN. GolfPose: Golf Swing Analyses with a Monocular Camera Based Human Pose Estimation. In: ICMEW 2022. 2022. https:\/\/doi.org\/10.1109\/ICMEW56448.2022.9859415.","DOI":"10.1109\/ICMEW56448.2022.9859415"},{"key":"953_CR27","doi-asserted-by":"publisher","unstructured":"Li YC, Chang CT, Cheng CC, Huang YL. Baseball Swing Pose Estimation Using OpenPose. In: RAAI 2021. 2021; 6\u20139. https:\/\/doi.org\/10.1109\/RAAI52226.2021.9507807.","DOI":"10.1109\/RAAI52226.2021.9507807"},{"key":"953_CR28","doi-asserted-by":"publisher","unstructured":"Ludwig K, Einfalt M, Lienhart R. Robust estimation of flight parameters for SKI jumpers. In: ICMEW 2020. 2020. https:\/\/doi.org\/10.1109\/ICMEW46912.2020.9105973.","DOI":"10.1109\/ICMEW46912.2020.9105973"},{"key":"953_CR29","doi-asserted-by":"publisher","unstructured":"Murakami T, Nakamura T. Athlete 3D pose estimation from a monocular TV sports video using pre-trained temporal convolutional networks. In: SMC 2020. 2020; 2615\u201320. https:\/\/doi.org\/10.1109\/SMC42975.2020.9283128.","DOI":"10.1109\/SMC42975.2020.9283128"},{"key":"953_CR30","doi-asserted-by":"publisher","first-page":"37749","DOI":"10.1109\/ACCESS.2023.3265595","volume":"11","author":"P Murthy","year":"2023","unstructured":"Murthy P, Taetz B, Lekhra A, Stricker D. DiveNet: dive action localization and physical pose parameter extraction for high performance training. IEEE Access. 2023;11:37749\u201367.","journal-title":"IEEE Access"},{"key":"953_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/s21082889","author":"L Needham","year":"2021","unstructured":"Needham L, Evans M, Cosker DP, Colyer SL. Can markerless pose estimation algorithms estimate 3D mass centre positions and velocities during linear sprinting activities? Sensors (Basel). 2021. https:\/\/doi.org\/10.3390\/s21082889.","journal-title":"Sensors (Basel)"},{"key":"953_CR32","doi-asserted-by":"publisher","unstructured":"Neher H, Vats K, Wong A, Clausi DA. HyperStackNet: A hyper stacked hourglass deep convolutional neural network architecture for joint player and stick pose estimation in hockey. In: CRV 2018. 2018; 313\u201320. https:\/\/doi.org\/10.1109\/CRV.2018.00051.","DOI":"10.1109\/CRV.2018.00051"},{"key":"953_CR33","doi-asserted-by":"publisher","unstructured":"Ooke N, Ikegami Y, Yamamoto K, Nakamura Y. Transfer Learning of Deep Neural Network Human Pose Estimator by Domain-Specific Data for Video Motion Capturing. In: ARSO 2022. https:\/\/doi.org\/10.1109\/ARSO54254.2022.9802965.","DOI":"10.1109\/ARSO54254.2022.9802965"},{"key":"953_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8110308","author":"R \u0160ajina","year":"2022","unstructured":"\u0160ajina R, Iva\u0161i\u0107-Kos M. 3D pose estimation and tracking in handball actions using a monocular camera. J Imaging. 2022. https:\/\/doi.org\/10.3390\/jimaging8110308.","journal-title":"J Imaging"},{"key":"953_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/s23156839","author":"HUR Siddiqui","year":"2023","unstructured":"Siddiqui HUR, Younas F, Rustam F, Flores ES, Ballester JB, Diez IDLT, et al. Enhancing cricket performance analysis with human pose estimation and machine learning. Sensors (Basel). 2023. https:\/\/doi.org\/10.3390\/s23156839.","journal-title":"Sensors (Basel)"},{"key":"953_CR36","doi-asserted-by":"publisher","unstructured":"Sun G. Golf Swing Correction Based on Deep Learning Body Posture Recognition. In: 2021 3rd International Conference on Pattern Recognition and Intelligent Systems. Bangkok, Thailand: ACM. 2021; 72\u20136. https:\/\/doi.org\/10.1145\/3480651.3480713. Accessed 12 Dec 2023.","DOI":"10.1145\/3480651.3480713"},{"key":"953_CR37","doi-asserted-by":"publisher","unstructured":"Zecha D, Eggert C, Lienhart R. Pose estimation for deriving kinematic parameters of competitive swimmers. In: Color and Imaging Conference 2017. 2017; 21\u20139. https:\/\/doi.org\/10.2352\/ISSN.2470-1173.2017.16.CVAS-345.","DOI":"10.2352\/ISSN.2470-1173.2017.16.CVAS-345"},{"key":"953_CR38","doi-asserted-by":"publisher","unstructured":"Zecha D, Einfalt M, Eggert C, Lienhart R. Kinematic pose rectification for performance analysis and retrieval in sports. In: CVPRW 2018. 2018; 1872\u201380. https:\/\/doi.org\/10.1109\/CVPRW.2018.00232.","DOI":"10.1109\/CVPRW.2018.00232"},{"key":"953_CR39","doi-asserted-by":"publisher","unstructured":"Z\u00f6llner M, Krause M, Gemeinhardt J, D\u00f6llinger M, Kniesburges S. Evaluation of Machine Learning Based Pose Estimation of Surfers on RiverWaves. In: Proceedings of the Association for Computing Machinery. 2023; 443\u20137. https:\/\/doi.org\/10.1145\/3594806.3596570.","DOI":"10.1145\/3594806.3596570"},{"key":"953_CR40","doi-asserted-by":"publisher","unstructured":"Akan S, Varl\u0131 S. Reidentifying Soccer Players in Broadcast Videos Using Body FeatureAlignment Based on Pose. In: Proceedings of the Association for Computing Machinery. 2023; 440\u20134. https:\/\/doi.org\/10.1145\/3603781.3603860.","DOI":"10.1145\/3603781.3603860"},{"key":"953_CR41","doi-asserted-by":"publisher","unstructured":"Fani M, Neher H, Clausi DA, Wong A, Zelek J. Hockey Action Recognition via Integrated Stacked Hourglass Network. In: CVPRW 2017. 2017; 85\u201393. https:\/\/doi.org\/10.1109\/CVPRW.2017.17.","DOI":"10.1109\/CVPRW.2017.17"},{"key":"953_CR42","doi-asserted-by":"publisher","unstructured":"Janbi NF, Almuaythir N. BowlingDL: A Deep Learning-Based Bowling Players Pose Estimation and Classification. In: ICAISC 2023. https:\/\/doi.org\/10.1109\/ICAISC56366.2023.10085434.","DOI":"10.1109\/ICAISC56366.2023.10085434"},{"key":"953_CR43","doi-asserted-by":"publisher","unstructured":"Kulkarni KM, Shenoy S. Table tennis stroke recognition using two-dimensional human pose estimation. In: CVPRW 2021. 2021; 4571\u20139. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00515.","DOI":"10.1109\/CVPRW53098.2021.00515"},{"key":"953_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7835241","author":"H Li","year":"2022","unstructured":"Li H, Guo H, Huang H. Analytical model of action fusion in sports tennis teaching by convolutional neural networks. Comput Intell Neurosci. 2022. https:\/\/doi.org\/10.1155\/2022\/7835241.","journal-title":"Comput Intell Neurosci"},{"issue":"2","key":"953_CR45","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.32604\/csse.2023.028003","volume":"44","author":"S Nandagopal","year":"2023","unstructured":"Nandagopal S, Karthy G, Sheryl Oliver A, Subha M. Optimal deep convolutional neural network with pose estimation for human activity recognition. Comput Syst Sci Eng. 2023;44(2):1719\u201333.","journal-title":"Comput Syst Sci Eng"},{"key":"953_CR46","doi-asserted-by":"publisher","unstructured":"Vats K, Neher H, Clausi DA, Zelek J. Two-stream action recognition in ice hockey using player pose sequences and optical flows. In: CRV 2019. 2019; 181\u20138. https:\/\/doi.org\/10.1109\/CRV.2019.00032.","DOI":"10.1109\/CRV.2019.00032"},{"key":"953_CR47","doi-asserted-by":"publisher","unstructured":"Xu X, Jia J, Yang P, Chen Y, Chen J, Sun Y, et al. Pose evaluation method based on part-based hierarchical bidirectional recurrent neural network. In: ICISCE 2019. 2019; 399\u2013402. https:\/\/doi.org\/10.1109\/ICISCE48695.2019.00086.","DOI":"10.1109\/ICISCE48695.2019.00086"},{"key":"953_CR48","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6118798","author":"T Yang","year":"2022","unstructured":"Yang T, Jiang C, Li P. Video analysis and system construction of basketball game by lightweight deep learning under the internet of things. Comput Intell Neurosci. 2022. https:\/\/doi.org\/10.1155\/2022\/6118798.","journal-title":"Comput Intell Neurosci"},{"key":"953_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11223797","author":"K Zuo","year":"2022","unstructured":"Zuo K, Su X. Three-dimensional action recognition for basketball teaching coupled with deep neural network. Electronics. 2022. https:\/\/doi.org\/10.3390\/electronics11223797.","journal-title":"Electronics"},{"issue":"12","key":"953_CR50","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3991\/ijoe.v18i12.30893","volume":"18","author":"FE Ait-Bennacer","year":"2022","unstructured":"Ait-Bennacer FE, Aaroud A, Akodadi K, Cherradi B. Applying deep learning and computer vision movement skills for an e-Sport and smart coaching system using a multiview dataset: case of Shotokan Karate. Int J Online Biomed Eng. 2022;18(12):35\u201353.","journal-title":"Int J Online Biomed Eng"},{"key":"953_CR51","doi-asserted-by":"publisher","unstructured":"Akiyama S, Umezu N. Similarity-based Form Visualization for Supporting Sports Instructions. In: 2022 IEEE LifeTech. 2022; 480\u20134. https:\/\/doi.org\/10.1109\/LifeTech53646.2022.9754764.","DOI":"10.1109\/LifeTech53646.2022.9754764"},{"key":"953_CR52","doi-asserted-by":"publisher","unstructured":"Chao B, Zhang Y. Research and realization of crouch start correction system based on human pose estimation. In: ITCA 2020. 2020; 764\u20137. https:\/\/doi.org\/10.1109\/ITCA52113.2020.00164.","DOI":"10.1109\/ITCA52113.2020.00164"},{"key":"953_CR53","doi-asserted-by":"publisher","unstructured":"Jian CZ, Abdullah J, Lenando H. DL-Shuttle: Badminton Coaching Training Assistance System Using Deep Learning Approach. In: ICDI 2022. 2022; 300\u20136. https:\/\/doi.org\/10.1109\/ICDI57181.2022.10007137.","DOI":"10.1109\/ICDI57181.2022.10007137"},{"key":"953_CR54","doi-asserted-by":"publisher","unstructured":"Nurahmadan IF, Pradnyana IWW. Utilization of Pose Estimation and Multilayer Perceptron Methods in the Development of Taekwondo Martial Arts Independent Learning. In: ICIMCIS 2021. 2021; 267\u201372. https:\/\/doi.org\/10.1109\/ICIMCIS53775.2021.9699268.","DOI":"10.1109\/ICIMCIS53775.2021.9699268"},{"key":"953_CR55","doi-asserted-by":"publisher","unstructured":"Phang JTS, Lim KH, Lease BA, Chiam DH. Deep Learning Pose Estimation for Kinematics Measurement in Archery. In: GECOST 2022. 2022; 298\u2013302. https:\/\/doi.org\/10.1109\/GECOST55694.2022.10010619.","DOI":"10.1109\/GECOST55694.2022.10010619"},{"key":"953_CR56","doi-asserted-by":"publisher","unstructured":"Suda S, Makino Y, Shinoda H. Prediction of Volleyball Trajectory Using Skeletal Motions of SetterPlayer. In: Proceedings of the Association for Computing Machinery. 2019. https:\/\/doi.org\/10.1145\/3311823.3311844.","DOI":"10.1145\/3311823.3311844"},{"key":"953_CR57","doi-asserted-by":"publisher","unstructured":"Takeichi K, Ichikawa M, Shinayama R, Tagawa T. A mobile application for running form analysis based on pose estimation movement skill. In: ICMEW 2018. https:\/\/doi.org\/10.1109\/ICMEW.2018.8551559.","DOI":"10.1109\/ICMEW.2018.8551559"},{"key":"953_CR58","doi-asserted-by":"publisher","unstructured":"Wang J, Qiu K, Peng H, Fu J, Zhu J. AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance. In: Proceedings of the Association for Computing Machinery. 2019; 374\u201382. https:\/\/doi.org\/10.1145\/3343031.3350910.","DOI":"10.1145\/3343031.3350910"},{"key":"953_CR59","doi-asserted-by":"publisher","unstructured":"Wei C, Wen J, Bi R, Yang H, Tao Y, Fan Y, et al. Online 8-Form Tai Chi Chuan Training and Evaluation System Based on Pose Estimation. In: HPCC-DSS-SmartCity-DependSys 2022. 2022; 366\u201371. https:\/\/doi.org\/10.1109\/HPCC-DSS-SmartCity-DependSys57074.2022.00078.","DOI":"10.1109\/HPCC-DSS-SmartCity-DependSys57074.2022.00078"},{"key":"953_CR60","doi-asserted-by":"publisher","unstructured":"Wessa E, Ashraf A, Atia A. Can pose classification be used to teach Kickboxing? In: ICECET 2021. 2021. https:\/\/doi.org\/10.1109\/ICECET52533.2021.9698656.","DOI":"10.1109\/ICECET52533.2021.9698656"},{"key":"953_CR61","doi-asserted-by":"publisher","unstructured":"Wu E, Perteneder F, Koike H. Real-Time Table Tennis Forecasting System Based on Long Short-Term PosePrediction Network. In: Proceedings of the Association for Computing Machinery. 2019. https:\/\/doi.org\/10.1145\/3355056.3364555.","DOI":"10.1145\/3355056.3364555"},{"issue":"2","key":"953_CR62","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s12652-021-03359-7","volume":"14","author":"Y Shi","year":"2023","unstructured":"Shi Y, Hu Z. Recognizing the take-off action pattern of basketball players based on fuzzy neural network system. J Ambient Intell Humaniz Comput. 2023;14(2):1071\u201381.","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"17","key":"953_CR63","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1080\/02640414.2022.2117474","volume":"40","author":"R Blythman","year":"2022","unstructured":"Blythman R, Saxena M, Tierney GJ, Richter C, Smolic A, Simms C. Assessment of deep learning pose estimates for sports collision tracking. J Sports Sci. 2022;40(17):1885\u2013900.","journal-title":"J Sports Sci"},{"key":"953_CR64","doi-asserted-by":"publisher","unstructured":"Hudovernik V, Skocaj D. Video-Based Detection of Combat Positions and Automatic Scoring in Jiu-jitsu. In: Proceedings of the Association for Computing Machinery. 2022; 55\u201363. https:\/\/doi.org\/10.1145\/3552437.3555707.","DOI":"10.1145\/3552437.3555707"},{"key":"953_CR65","doi-asserted-by":"publisher","unstructured":"Nishio M, Nonaka N, Fujihira R, Murakami H, Tajima T, Yamada M, et al. Objective Detection of High-Risk Tackle in Rugby by Combination of Pose Estimation and Machine Learning. In: Lecture Notes in Computer Science (LNCS). 2023; 215\u201328. https:\/\/doi.org\/10.1007\/978-3-031-29168-5_15.","DOI":"10.1007\/978-3-031-29168-5_15"},{"key":"953_CR66","doi-asserted-by":"publisher","unstructured":"Nonaka N, Fujihira R, Nishio M, Murakami H, Tajima T, Yamada M, et al. End-to-End High-Risk Tackle Detection System for Rugby. In: CVPRW 2022. 2022; 3549\u201358. https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00399.","DOI":"10.1109\/CVPRW56347.2022.00399"},{"issue":"1\u20132","key":"953_CR67","first-page":"13","volume":"32","author":"K Cardenas","year":"2024","unstructured":"Cardenas K, Semwal S, Maher J. End-to-end move prediction system for indoor rock climbing: beta caller. J WSCG. 2024;32(1\u20132):13\u201320.","journal-title":"J WSCG"},{"key":"953_CR68","doi-asserted-by":"publisher","DOI":"10.1007\/s12283-025-00515-6","author":"J Fang","year":"2025","unstructured":"Fang J, Yeung C, Fujii K. Foul prediction with estimated poses from soccer broadcast video. Sports Eng. 2025. https:\/\/doi.org\/10.1007\/s12283-025-00515-6.","journal-title":"Sports Eng"},{"key":"953_CR69","doi-asserted-by":"publisher","first-page":"108356","DOI":"10.1016\/j.bspc.2025.108356","volume":"111","author":"M Shoaib","year":"2026","unstructured":"Shoaib M, Husnain G. Deep learning-based spatiotemporal action recognition in football using I3D and TSN with pose estimation. Biomed Signal Process Control. 2026;111:108356.","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"953_CR70","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1007\/s00500-023-09587-7","volume":"28","author":"W Ren","year":"2024","unstructured":"Ren W. A novel approach for automatic detection and identification of inappropriate postures and movements of table tennis players. Soft Comput. 2024;28(3):2245\u201369.","journal-title":"Soft Comput"},{"key":"953_CR71","doi-asserted-by":"publisher","DOI":"10.1051\/smdo\/2024004","author":"M Ye","year":"2024","unstructured":"Ye M. Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimation. Int J Simul Multidiscip Des Optim. 2024. https:\/\/doi.org\/10.1051\/smdo\/2024004.","journal-title":"Int J Simul Multidiscip Des Optim"},{"issue":"4","key":"953_CR72","doi-asserted-by":"publisher","first-page":"605","DOI":"10.62617\/mcb605","volume":"21","author":"Y Yu","year":"2024","unstructured":"Yu Y. Construction of tennis pose estimation and action recognition model based on improved ST-GCN. Mol Cell Biomech. 2024;21(4):605. https:\/\/doi.org\/10.62617\/mcb605.","journal-title":"Mol Cell Biomech"},{"issue":"13","key":"953_CR73","doi-asserted-by":"publisher","first-page":"4003","DOI":"10.3390\/s25134003","volume":"25","author":"W Zheng","year":"2025","unstructured":"Zheng W, Zhang M, Dong R, Qiu M, Wang W. Feasibility and accuracy of an RTMPose-Based markerless motion capture system for single-player tasks in 3x3 basketball. Sensors. 2025;25(13):4003.","journal-title":"Sensors"},{"issue":"2","key":"953_CR74","first-page":"20","volume":"7","author":"C Aulton","year":"2024","unstructured":"Aulton C, Strafford BW, Davids K, Chiu CY. Optimizing the use of machine learning and computer vision in sport: an ecological dynamics perspective. J Expert. 2024;7(2):20.","journal-title":"J Expert"},{"issue":"9","key":"953_CR75","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1080\/02640414.2015.1072279","volume":"34","author":"J Stoszkowski","year":"2016","unstructured":"Stoszkowski J, Collins D. Sources, topics and use of knowledge by coaches. J Sports Sci. 2016;34(9):794\u2013802. https:\/\/doi.org\/10.1080\/02640414.2015.1072279.","journal-title":"J Sports Sci"},{"issue":"3","key":"953_CR76","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s40279-012-0011-z","volume":"43","author":"L Seifert","year":"2013","unstructured":"Seifert L, Button C, Davids K. Key properties of expert movement systems in sport. Sports Med. 2013;43(3):167\u201378.","journal-title":"Sports Med"},{"issue":"11","key":"953_CR77","doi-asserted-by":"publisher","first-page":"3686","DOI":"10.3390\/s24113686","volume":"24","author":"S Scataglini","year":"2024","unstructured":"Scataglini S, Abts E, Van Bocxlaer C, Van den Bussche M, Meletani S, Truijen S. Accuracy, validity, and reliability of markerless camera-based 3D motion capture systems versus marker-based 3D motion capture systems in gait analysis: a systematic review and meta-analysis. Sensors. 2024;24(11):3686.","journal-title":"Sensors"},{"key":"953_CR78","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-981-97-8193-5_17","volume-title":"Intelligent Solutions for Smart Adaptation in Digital Era","author":"Sahil","year":"2025","unstructured":"Sahil, Pratham, Neha, Sadotra P, Chouksey P, Chopra M, et al. Generative AI for the creation of images. In: Hasteer N, Blum C, Mehrotra D, Pandey HM, editors. Intelligent Solutions for Smart Adaptation in Digital Era. Singapore: Springer Nature; 2025. pp. 193\u2013207."},{"key":"953_CR79","doi-asserted-by":"publisher","first-page":"127125","DOI":"10.1016\/j.neucom.2023.127125","volume":"570","author":"M Xu","year":"2024","unstructured":"Xu M, Wang Y, Xu B, Zhang J, Ren J, Huang Z, et al. A critical analysis of image-based camera pose estimation movement skills. Neurocomputing. 2024;570:127125. https:\/\/doi.org\/10.1016\/j.neucom.2023.127125.","journal-title":"Neurocomputing"},{"key":"953_CR80","doi-asserted-by":"crossref","unstructured":"Peng J, Zhou Y, Mok PY. KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation. In 2024; 1123\u201332. https:\/\/openaccess.thecvf.com\/content\/CVPR2024\/html\/Peng_KTPFormer_Kinematics_and_Trajectory_Prior_Knowledge-Enhanced_Transformer_for_3D_Human_CVPR_2024_paper.html. Accessed 03 Nov 2025.","DOI":"10.1109\/CVPR52733.2024.00113"},{"key":"953_CR81","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.imavis.2017.02.002","volume":"61","author":"W Zhang","year":"2017","unstructured":"Zhang W, Liu Z, Zhou L, Leung H, Chan AB. Martial arts, dancing and sports dataset: a challenging stereo and multi-view dataset for 3D human pose estimation. Image Vis Comput. 2017;61:22\u201339.","journal-title":"Image Vis Comput"}],"container-title":["Sports Medicine - Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40798-025-00953-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40798-025-00953-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40798-025-00953-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:50:56Z","timestamp":1765371056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40798-025-00953-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["953"],"URL":"https:\/\/doi.org\/10.1186\/s40798-025-00953-3","relation":{},"ISSN":["2198-9761"],"issn-type":[{"value":"2198-9761","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"27 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Sheffield Hallam University ethics review board under the approval number ER57203991.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Keith Davids is an Editorial Board member of Sports Medicine\u2014Open but was not involved in the selection of peer reviewers for this manuscript or any of the subsequent editorial decisions. The authors have no conflicts of interest with the content of this article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"155"}}