{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T14:39:14Z","timestamp":1782830354300,"version":"3.54.5"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006071"],"award-info":[{"award-number":["62006071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Project of Henan Province","award":["232103810086"],"award-info":[{"award-number":["232103810086"]}]},{"name":"Henan University of Technology Grain Information Processing Center Open Fund","award":["KFJJ2023010"],"award-info":[{"award-number":["KFJJ2023010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"DOI":"10.1007\/s44230-025-00120-7","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:16:24Z","timestamp":1763990184000},"page":"576-594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GaitMed: A Medical Gait Dataset and Benchmark for Musculoskeletal Disease Classification"],"prefix":"10.1007","volume":"5","author":[{"given":"Weiya","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saiyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"120_CR1","first-page":"646","volume":"81","author":"AD Woolf","year":"2003","unstructured":"Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World Health Organ. 2003;81:646\u201356.","journal-title":"Bull World Health Organ"},{"key":"120_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-3-4","volume":"3","author":"R Baker","year":"2006","unstructured":"Baker R. Gait analysis methods in rehabilitation. J Neuroeng Rehabil. 2006;3:1\u201310.","journal-title":"J Neuroeng Rehabil"},{"key":"120_CR3","doi-asserted-by":"publisher","first-page":"3362","DOI":"10.3390\/s140203362","volume":"14","author":"A Muro-De-La-Herran","year":"2014","unstructured":"Muro-De-La-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors. 2014;14:3362\u201394.","journal-title":"Sensors"},{"key":"120_CR4","doi-asserted-by":"crossref","unstructured":"Makihara Y, Nixon MS, Yagi Y. Gait recognition: Databases, representations, and applications. In: Computer Vision: A Reference Guide. Springer; 2021. p. 487\u201399.","DOI":"10.1007\/978-3-030-63416-2_883"},{"key":"120_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3230633","volume":"51","author":"C Wan","year":"2018","unstructured":"Wan C, Wang L, Phoha VV. A survey on gait recognition. ACM Computing Surveys (CSUR). 2018;51:1\u201335.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"120_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2018.01.007","volume":"167","author":"P Connor","year":"2018","unstructured":"Connor P, Ross A. Biometric recognition by gait: A survey of modalities and features. Comput Vis Image Underst. 2018;167:1\u201327.","journal-title":"Comput Vis Image Underst"},{"key":"120_CR7","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2005","unstructured":"Han J, Bhanu B. Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell. 2005;28:316\u201322.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107069","volume":"98","author":"R Liao","year":"2020","unstructured":"Liao R, Yu S, An W, Huang Y. A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 2020;98:107069.","journal-title":"Pattern Recogn"},{"key":"120_CR9","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1038\/s41598-019-38748-8","volume":"9","author":"F Horst","year":"2019","unstructured":"Horst F, Lapuschkin S, Samek W, M\u00fcller KR, Sch\u00f6llhorn WI. Explaining the unique nature of individual gait patterns with deep learning. Sci Rep. 2019;9:2391.","journal-title":"Sci Rep"},{"key":"120_CR10","first-page":"4165","volume":"2016","author":"T Wolf","year":"2016","unstructured":"Wolf T, Babaee M, Rigoll G. Multi-view gait recognition using 3D convolutional neural networks. In IEEE international conference on image processing (ICIP). 2016;2016:4165\u20139.","journal-title":"In IEEE international conference on image processing (ICIP)"},{"key":"120_CR11","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s10489-022-03543-y","volume":"53","author":"G Li","year":"2023","unstructured":"Li G, Guo L, Zhang R, Qian J, Gao S. Transgait: Multimodal-based gait recognition with set transformer. Appl Intell. 2023;53:1535\u201347.","journal-title":"Appl Intell"},{"key":"120_CR12","doi-asserted-by":"crossref","unstructured":"Shen C, Yu S, Wang J, Huang GQ, Wang L. A comprehensive survey on deep gait recognition: Algorithms, datasets, and challenges. IEEE Transactions on Biometrics, Behavior, and Identity Science ;2024.","DOI":"10.1109\/TBIOM.2024.3486345"},{"key":"120_CR13","unstructured":"Fan C, Hou S, Huang Y, Yu S. Exploring deep models for practical gait recognition;2023. arXiv:2303.03301."},{"key":"120_CR14","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/TPAMI.2003.1251144","volume":"25","author":"L Wang","year":"2003","unstructured":"Wang L, Tan T, Ning H, Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell. 2003;25:1505\u201318.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR15","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW. The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell. 2005;27:162\u201377.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR16","unstructured":"Yu S, Tan D, Tan T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th international conference on pattern recognition (ICPR\u201906). IEEE; 2006;4:441\u2013444."},{"key":"120_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41074-017-0037-0","volume":"10","author":"N Takemura","year":"2018","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans Comput Vision Appl. 2018;10:1\u201314.","journal-title":"IPSJ Trans Comput Vision Appl"},{"key":"120_CR18","unstructured":"Zhu Z, Guo X, Yang T, Huang J, Deng J, Huang G, et\u00a0al. Gait recognition in the wild: A benchmark. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2021:14789\u201314799."},{"key":"120_CR19","first-page":"2801","volume":"45","author":"C Song","year":"2022","unstructured":"Song C, Huang Y, Wang W, Wang L. CASIA-E: a large comprehensive dataset for gait recognition. IEEE Trans Pattern Anal Mach Intell. 2022;45:2801\u201315.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR20","doi-asserted-by":"crossref","unstructured":"Zheng J, Liu X, Liu W, He L, Yan C, Mei T. Gait recognition in the wild with dense 3d representations and a benchmark. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2022:20228\u201320237.","DOI":"10.1109\/CVPR52688.2022.01959"},{"key":"120_CR21","doi-asserted-by":"crossref","unstructured":"Li W, Hou S, Zhang C, Cao C, Liu X, Huang Y, et\u00a0al. An in-depth exploration of person re-identification and gait recognition in cloth-changing conditions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2023:13824\u201313833.","DOI":"10.1109\/CVPR52729.2023.01328"},{"key":"120_CR22","doi-asserted-by":"crossref","unstructured":"Shen C, Fan C, Wu W, Wang R, Huang GQ, Yu S. Lidargait: Benchmarking 3d gait recognition with point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2023:1054\u20131063.","DOI":"10.1109\/CVPR52729.2023.00108"},{"issue":"12","key":"120_CR23","doi-asserted-by":"publisher","first-page":"14920","DOI":"10.1109\/TPAMI.2023.3312419","volume":"45","author":"C Fan","year":"2023","unstructured":"Fan C, Hou S, Wang J, Huang Y, Yu S. Learning gait representation from massive unlabelled walking videos: A benchmark. IEEE Trans Pattern Anal Mach Intell. 2023;45(12):14920\u201337.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR24","first-page":"7855","volume":"38","author":"S Zou","year":"2024","unstructured":"Zou S, Fan C, Xiong J, Shen C, Yu S, Tang J. Cross-covariate gait recognition: A benchmark. Proc AAAI Conf Artif Intell. 2024;38:7855\u201363.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"120_CR25","doi-asserted-by":"crossref","unstructured":"Zhou Z, Liang J, Peng Z, Fan C, An F, Yu S. Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2024:284\u2013294.","DOI":"10.1007\/978-3-031-72086-4_27"},{"key":"120_CR26","doi-asserted-by":"crossref","unstructured":"Ye D, Fan C, Ma J, Liu X, Yu S. Biggait: Learning gait representation you want by large vision models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2024:200\u2013210.","DOI":"10.1109\/CVPR52733.2024.00027"},{"key":"120_CR27","doi-asserted-by":"crossref","unstructured":"Shen C, Wang R, Duan L, Yu S. LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition. In: Proceedings of the Computer Vision and Pattern Recognition Conference; 2025:6627\u20136636.","DOI":"10.1109\/CVPR52734.2025.00621"},{"key":"120_CR28","doi-asserted-by":"crossref","unstructured":"Jiao T, Guo C, Feng X, Chen Y, Song J. A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods, Technologies and Applications. Computers, Materials & Continua. 2024;80(1).","DOI":"10.32604\/cmc.2024.053204"},{"key":"120_CR29","doi-asserted-by":"crossref","unstructured":"Jin D, Fan C, Ma J, Zhou J, Chen W, Yu S. On Denoising Walking Videos for Gait Recognition. In: Proceedings of the Computer Vision and Pattern Recognition Conference; 2025:12347\u201312357.","DOI":"10.1109\/CVPR52734.2025.01152"},{"key":"120_CR30","first-page":"4120","volume":"39","author":"D Jin","year":"2025","unstructured":"Jin D, Fan C, Chen W, Yu S. Exploring more from multiple gait modalities for human identification. Proc AAAI Conf Artif Intell. 2025;39:4120\u20138.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"120_CR31","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.joca.2013.12.015","volume":"22","author":"A Elbaz","year":"2014","unstructured":"Elbaz A, Mor A, Segal G, Debi R, Shazar N, Herman A. Novel classification of knee osteoarthritis severity based on spatiotemporal gait analysis. Osteoarthritis Cartilage. 2014;22:457\u201363.","journal-title":"Osteoarthritis Cartilage"},{"key":"120_CR32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1038\/s41597-020-0481-z","volume":"7","author":"B Horsak","year":"2020","unstructured":"Horsak B, Slijepcevic D, Raberger AM, Schwab C, Worisch M, Zeppelzauer M. GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Sci Data. 2020;7:143.","journal-title":"Sci Data"},{"key":"120_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0178615","volume":"12","author":"LH Kikkert","year":"2017","unstructured":"Kikkert LH, De Groot MH, van Campen JP, Beijnen JH, Hortob\u00e1gyi T, Vuillerme N, et al. Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic. PLoS One. 2017;12:e0178615.","journal-title":"PLoS One"},{"key":"120_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016:770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"120_CR35","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018:7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"120_CR36","doi-asserted-by":"crossref","unstructured":"Qiu Z, Yao T, Mei T. Learning spatio-temporal representation with pseudo-3d residual networks. In: proceedings of the IEEE International Conference on Computer Vision; 2017:5533\u20135541.","DOI":"10.1109\/ICCV.2017.590"},{"key":"120_CR37","first-page":"3467","volume":"44","author":"H Chao","year":"2021","unstructured":"Chao H, Wang K, He Y, Zhang J, Feng J. GaitSet: Cross-view gait recognition through utilizing gait as a deep set. IEEE Trans Pattern Anal Mach Intell. 2021;44:3467\u201378.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR38","doi-asserted-by":"crossref","unstructured":"Fu Y, Wei Y, Zhou Y, Shi H, Huang G, Wang X, et\u00a0al. Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence. 2019;33:8295\u20138302.","DOI":"10.1609\/aaai.v33i01.33018295"},{"key":"120_CR39","doi-asserted-by":"crossref","unstructured":"Luo H, Gu Y, Liao X, Lai S, Jiang W. Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops. 2019:0\u20130.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"120_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404","volume":"106","author":"X Qin","year":"2020","unstructured":"Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M. U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recogn. 2020;106:107404.","journal-title":"Pattern Recogn"},{"key":"120_CR41","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2019","unstructured":"Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y. Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell. 2019;43:172\u201386.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR42","doi-asserted-by":"crossref","unstructured":"Fan C, Ma J, Jin D, Shen C, Yu S. Skeletongait: Gait recognition using skeleton maps. In: Proceedings of the AAAI conference on artificial intelligence. 2024;38:1662\u20131669.","DOI":"10.1609\/aaai.v38i2.27933"},{"key":"120_CR43","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018;106:249\u201359.","journal-title":"Neural Netw"},{"key":"120_CR44","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q. Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision 2015:1116\u20131124.","DOI":"10.1109\/ICCV.2015.133"},{"key":"120_CR45","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2021","unstructured":"Ye M, Shen J, Lin G, Xiang T, Shao L, Hoi SC. Deep learning for person re-identification: A survey and outlook. IEEE Trans Pattern Anal Mach Intell. 2021;44:2872\u201393.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"120_CR46","doi-asserted-by":"crossref","unstructured":"Fan C, Liang J, Shen C, Hou S, Huang Y, Yu S. Opengait: Revisiting gait recognition towards better practicality. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2023:9707\u20139716.","DOI":"10.1109\/CVPR52729.2023.00936"},{"key":"120_CR47","doi-asserted-by":"crossref","unstructured":"Fan C, Peng Y, Cao C, Liu X, Hou S, Chi J, et\u00a0al. Gaitpart: Temporal part-based model for gait recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020: 14225\u201314233.","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"120_CR48","doi-asserted-by":"crossref","unstructured":"Chao H, He Y, Zhang J, Feng J. Gaitset: Regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI conference on artificial intelligence. 2019;33:8126\u20138133.","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"120_CR49","unstructured":"Lin B, Zhang S, Wang M, Li L, Yu X. Gaitgl: Learning discriminative global-local feature representations for gait recognition;2022. arXiv:2208.01380."},{"key":"120_CR50","doi-asserted-by":"crossref","unstructured":"Fan C, Hou S, Liang J, Shen C, Ma J, Jin D, et\u00a0al. OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality. IEEE Transactions on Pattern Analysis and Machine Intelligence; 2025.","DOI":"10.1109\/TPAMI.2025.3576283"},{"key":"120_CR51","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization;2014. arXiv:1412.6980."},{"key":"120_CR52","doi-asserted-by":"crossref","unstructured":"Salman H, Parks C, Swan M, Gauch J. Orthonets: Orthogonal channel attention networks. In: 2023 IEEE International Conference on Big Data (BigData). IEEE; 2023:829\u2013837.","DOI":"10.1109\/BigData59044.2023.10386646"},{"key":"120_CR53","unstructured":"Borovykh A, Bohte S, Oosterlee CW. Conditional time series forecasting with convolutional neural networks;2017. arXiv:1703.04691."},{"issue":"3","key":"120_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT, Ni LM. Generalizing from a few examples: A survey on few-shot learning. ACM Comput Surv. 2020;53(3):1\u201334.","journal-title":"ACM Comput Surv"},{"key":"120_CR55","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision 2017:2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"120_CR56","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998\u20136008.","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00120-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-025-00120-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00120-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T09:09:09Z","timestamp":1765962549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-025-00120-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["120"],"URL":"https:\/\/doi.org\/10.1007\/s44230-025-00120-7","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"21 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study complies with ethical standards for anonymized human data research. All participants provided written informed consent authorizing the conversion of raw videos or images into irreversibly de-identified silhouettes and skeleton heatmaps, use for non-profit medical research, and academic publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"All authors have provided their consent for the publication of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Participation and Publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Registration (if applicable)"}}]}}