{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:21:07Z","timestamp":1782181267240,"version":"3.54.5"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"35","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Beijing Social Science Foundation","award":["19YTC043"],"award-info":[{"award-number":["19YTC043"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08206-8","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T06:03:04Z","timestamp":1674799384000},"page":"24755-24771","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automatic generation of Labanotation based on human pose estimation in folk dance videos"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-2728","authenticated-orcid":false,"given":"Xingquan","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sichen","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,27]]},"reference":[{"issue":"8","key":"8206_CR1","first-page":"1650","volume":"14","author":"P Jiang","year":"2018","unstructured":"Jiang P, Qin XL (2018) Adaptive video keyframe extraction based on visual attention model. J Image Gr 14(8):1650\u20131655","journal-title":"J Image Gr"},{"issue":"5","key":"8206_CR2","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.neucom.2019.07.103","volume":"390","author":"J He","year":"2020","unstructured":"He J, Zhang C, He XL et al (2020) Visual recognition of traffic police gestures with convolutional pose machine and handcrafted features. Neurocomputing 390(5):248\u2013259. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.103","journal-title":"Neurocomputing"},{"issue":"20","key":"8206_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3788\/LOP57.201509","volume":"679","author":"XK Zhang","year":"2020","unstructured":"Zhang XK, Zhang RF, Liu YH (2020) Human pose estimation based on quadratic generative antagonism. Laser Optoelectron Prog 679(20):335\u2013343. https:\/\/doi.org\/10.3788\/LOP57.201509","journal-title":"Laser Optoelectron Prog"},{"issue":"2","key":"8206_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s13362-020-0071-x","volume":"10","author":"S Glas","year":"2020","unstructured":"Glas S, Kiesel R, Kolkmann S et al (2020) Intraday renewable electricity trading: advanced modeling and numerical optimal control. J Math Ind 10(2):49\u201385. https:\/\/doi.org\/10.1186\/s13362-020-0071-x","journal-title":"J Math Ind"},{"issue":"6","key":"8206_CR5","first-page":"1668","volume":"42","author":"GM Feng","year":"2021","unstructured":"Feng GM, Liu YJ (2021) Visual algorithm for on-the-job behavior analysis. Comput Eng Des 42(6):1668\u20131676","journal-title":"Comput Eng Des"},{"key":"8206_CR6","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3969\/j.issn.1006-4052.2019.09.046","volume":"9","author":"RM Lian","year":"2019","unstructured":"Lian RM, Liu Y, Yu P et al (2019) Video based human pose detection methods and their applications. Comput Program Skills Maint 9:127\u2013129. https:\/\/doi.org\/10.3969\/j.issn.1006-4052.2019.09.046","journal-title":"Comput Program Skills Maint"},{"issue":"6","key":"8206_CR7","first-page":"37","volume":"34","author":"KY Zhou","year":"2021","unstructured":"Zhou KY (2021) Fitness action recognition system based on deep learning. Ind Control Comput 34(6):37\u201339","journal-title":"Ind Control Comput"},{"issue":"21","key":"8206_CR8","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TPWRS.2018.2862246","volume":"34","author":"S Baltaoglu","year":"2018","unstructured":"Baltaoglu S, Tong L, Zhao Q (2018) Algorithmic bidding for virtual trading in electricity markets. IEEE Trans Power Syst 34(21):535\u2013543. https:\/\/doi.org\/10.1109\/TPWRS.2018.2862246","journal-title":"IEEE Trans Power Syst"},{"issue":"16","key":"8206_CR9","doi-asserted-by":"publisher","first-page":"12679","DOI":"10.1109\/JIOT.2020.3026988","volume":"8","author":"Z Cai","year":"2021","unstructured":"Cai Z, Shi T (2021) Distributed query processing in the edge-assisted IoT data monitoring system. IEEE Internet Things J 8(16):12679\u201312693. https:\/\/doi.org\/10.1109\/JIOT.2020.3026988","journal-title":"IEEE Internet Things J"},{"key":"8206_CR10","doi-asserted-by":"publisher","unstructured":"Toshev A, Szegedy C (2014) DeepPose: human pose estimation via deep neural networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1653\u20131660. https:\/\/doi.org\/10.1109\/CVPR.2014.214","DOI":"10.1109\/CVPR.2014.214"},{"key":"8206_CR11","doi-asserted-by":"publisher","unstructured":"Wei S, Ramakrishna V, Kanade T et al (2016) Convolutional pose machines. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 4724\u20134732. https:\/\/doi.org\/10.1109\/CVPR.2016.511","DOI":"10.1109\/CVPR.2016.511"},{"key":"8206_CR12","doi-asserted-by":"publisher","unstructured":"Cao Z, Simon T, Wei SE et al (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1302\u20131310. https:\/\/doi.org\/10.48550\/arXiv.1611.08050","DOI":"10.48550\/arXiv.1611.08050"},{"key":"8206_CR13","doi-asserted-by":"publisher","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision (ECCV), pp 483\u2013499. https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"8206_CR14","doi-asserted-by":"publisher","unstructured":"Fang HS, Xie S, Tai YW et al (2017) RMPE: regional multi-person pose estimation. In: IEEE International conference on computer vision (ICCV), pp 2353\u20132362. https:\/\/doi.org\/10.1109\/ICCV.2017.256","DOI":"10.1109\/ICCV.2017.256"},{"key":"8206_CR15","doi-asserted-by":"publisher","unstructured":"Sun K, Xiao B, Liu D et al (2019) Deep high-resolution representation learning for human pose estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 5686\u20135696. https:\/\/doi.org\/10.1109\/CVPR.2019.00584","DOI":"10.1109\/CVPR.2019.00584"},{"issue":"8","key":"8206_CR16","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.3969\/j.issn.0372-2112.2020.08.010","volume":"48","author":"L Shen","year":"2020","unstructured":"Shen L, Chen Y (2020) End-to-end unlabeled human pose estimation network based on high-dimensional information encoding and decoding with feature monitoring. Acta Electron Sin 48(8):1528\u20131537. https:\/\/doi.org\/10.3969\/j.issn.0372-2112.2020.08.010","journal-title":"Acta Electron Sin"},{"issue":"2","key":"8206_CR17","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1109\/TMTT.2018.2880914","volume":"67","author":"J Xu","year":"2019","unstructured":"Xu J, Wan H, Chen ZY (2019) Sharp skirt bandpass filter-integrated single-pole double-throw switch with absorptive OFF-state. IEEE Trans Microw Theory Tech 67(2):704\u2013711. https:\/\/doi.org\/10.1109\/TMTT.2018.2880914","journal-title":"IEEE Trans Microw Theory Tech"},{"key":"8206_CR18","doi-asserted-by":"publisher","unstructured":"Feng T (2019) Three-dimensional human pose estimation based on monocular vision. Harbin Institute of Technology, Harbin. https:\/\/doi.org\/10.27061\/d.cnki.ghgdu.2019.000896","DOI":"10.27061\/d.cnki.ghgdu.2019.000896"},{"issue":"03","key":"8206_CR19","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3969\/j.issn.0255-8297.2019.03.013","volume":"37","author":"SR Fan","year":"2019","unstructured":"Fan SR, Jia YT, Liu JH (2019) Feature selection for human pose recognition based on three-axis acceleration sensor. Chin J Appl Sci 37(03):427\u2013436. https:\/\/doi.org\/10.3969\/j.issn.0255-8297.2019.03.013","journal-title":"Chin J Appl Sci"},{"key":"8206_CR20","doi-asserted-by":"publisher","unstructured":"Kanazawa A, Black MJ, Jacobs DW et al (2018) End-to-end recovery of human shape and pose. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 7122\u20137131. https:\/\/doi.org\/10.1109\/CVPR.2018.00744","DOI":"10.1109\/CVPR.2018.00744"},{"key":"8206_CR21","doi-asserted-by":"publisher","first-page":"44.1","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta D, Sridhar S, Sotnychenko O et al (2017) Vnect: real-time 3d human pose estimation with a single RGB camera. ACM Trans Gr 36:44.1-44.14","journal-title":"ACM Trans Gr"},{"key":"8206_CR22","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neucom.2022.05.029","volume":"501","author":"Z Cai","year":"2022","unstructured":"Cai Z, Esposito C, Dargahi T et al (2022) Graph-powered learning for social networks. Neurocomputing 501:244\u2013245. https:\/\/doi.org\/10.1016\/j.neucom.2022.05.029","journal-title":"Neurocomputing"},{"issue":"6","key":"8206_CR23","doi-asserted-by":"publisher","first-page":"2256011","DOI":"10.1142\/S0218001422560110","volume":"36","author":"XQ Cai","year":"2022","unstructured":"Cai XQ, Wang T, Bai X et al (2022) Pogt: a peking opera gesture training system using infrared sensors. Int J Pattern Recognit Artif Intell 36(6):2256011. https:\/\/doi.org\/10.1142\/S0218001422560110","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"8206_CR24","doi-asserted-by":"publisher","unstructured":"Martinez J, Hossain R, Romero J et al (2017) A simple yet effective baseline for 3d human pose estimation. In: IEEE international conference on computer vision (ICCV), pp 2659\u20132668. https:\/\/doi.org\/10.1109\/ICCV.2017.288","DOI":"10.1109\/ICCV.2017.288"},{"key":"8206_CR25","doi-asserted-by":"publisher","unstructured":"Hossain M, Little J (2018) Exploiting temporal information for 3d human pose estimation. In: European conference on computer vision (ECCV), pp 69\u201386. https:\/\/doi.org\/10.1007\/978-3-030-01249-6_5","DOI":"10.1007\/978-3-030-01249-6_5"},{"key":"8206_CR26","doi-asserted-by":"publisher","unstructured":"Pavllo D, Feichtenhofer C, Grangier D et al (2019) 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 7753\u20137762.https:\/\/doi.org\/10.1109\/CVPR.2019.00794","DOI":"10.1109\/CVPR.2019.00794"},{"key":"8206_CR27","doi-asserted-by":"publisher","unstructured":"Hachimura K, Nakamura M (2001) Method of generating coded description of human body motion from motion-captured data. In: IEEE International workshop on robot and human interactive communication (ROMAN), pp 122\u2013127. https:\/\/doi.org\/10.1109\/ROMAN.2001.981889","DOI":"10.1109\/ROMAN.2001.981889"},{"key":"8206_CR28","doi-asserted-by":"publisher","unstructured":"Chen H, Qian G, James J (2005) An autonomous dance scoring system using marker-based motion capture. In: Workshop on multimedia signal processing (MMSP), pp 1\u20134. https:\/\/doi.org\/10.1109\/MMSP.2005.248666","DOI":"10.1109\/MMSP.2005.248666"},{"key":"8206_CR29","doi-asserted-by":"publisher","first-page":"10823","DOI":"10.1007\/s11042-014-2209-6","volume":"74","author":"W Choensawat","year":"2015","unstructured":"Choensawat W, Nakamura M, Hachimura K (2015) GenLaban: a tool for generating Labanotation from motion capture data. Multimed Tools Appl 74:10823\u201310846. https:\/\/doi.org\/10.1007\/s11042-014-2209-6","journal-title":"Multimed Tools Appl"},{"key":"8206_CR30","doi-asserted-by":"publisher","DOI":"10.7666\/d.Y2916406","volume-title":"Research on automatic generation of Labanotation based on human motion capture data","author":"H Guo","year":"2015","unstructured":"Guo H (2015) Research on automatic generation of Labanotation based on human motion capture data. Beijing Jiaotong Univ, Beijing. https:\/\/doi.org\/10.7666\/d.Y2916406"},{"key":"8206_CR31","doi-asserted-by":"publisher","unstructured":"Guo H, Miao ZJ, Zhu FY et al (2014) Automatic labanotation generation based on human motion capture data. In: Chinese conference on pattern recognition (CCPR), pp 426\u2013435. https:\/\/doi.org\/10.1007\/978-3-662-45646-0_44","DOI":"10.1007\/978-3-662-45646-0_44"},{"key":"8206_CR32","doi-asserted-by":"publisher","unstructured":"Zhou ZM, Miao ZJ, Wang JJ (2016) A system for automatic generation of Labanotation from motion capture data. In: International conference on signal processing (ICSP), pp 1031\u20131034. https:\/\/doi.org\/10.1109\/ICSP.2016.7877986","DOI":"10.1109\/ICSP.2016.7877986"},{"key":"8206_CR33","volume-title":"Research on automatic generation of Labanotation based on dynamic programming","author":"ZM Zhou","year":"2017","unstructured":"Zhou ZM (2017) Research on automatic generation of Labanotation based on dynamic programming. Beijing Jiaotong University, Beijing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08206-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08206-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08206-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T12:03:52Z","timestamp":1700136232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08206-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,27]]},"references-count":33,"journal-issue":{"issue":"35","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8206"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08206-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,27]]},"assertion":[{"value":"20 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2023","order":3,"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 that there is no conflict of interests regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}