{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:23:44Z","timestamp":1769844224764,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62001176"],"award-info":[{"award-number":["62001176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61871196"],"award-info":[{"award-number":["61871196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Fujian Province, China","award":["2020J01085"],"award-info":[{"award-number":["2020J01085"]}]},{"name":"Natural Science Foundation of Fujian Province, China"},{"name":"National Key Research and Development Program of China","award":["2019YFC1604700"],"award-info":[{"award-number":["2019YFC1604700"]}]},{"name":"Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University","award":["ZQN-YX601"],"award-info":[{"award-number":["ZQN-YX601"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["JP22H03643"],"award-info":[{"award-number":["JP22H03643"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04613-5","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T14:06:06Z","timestamp":1686319566000},"page":"21692-21705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-skeleton structures graph convolutional network for action quality assessment in long videos"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3573-4226","authenticated-orcid":false,"given":"Qing","family":"Lei","sequence":"first","affiliation":[]},{"given":"Huiying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jixiang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Shangce","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"issue":"2","key":"4613_CR1","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/TNSRE.2020.2966249","volume":"28","author":"Y Liao","year":"2020","unstructured":"Liao Y, Vakanski A, Xian M (2020) A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans Neural Syst Rehab Eng 28(2):468\u2013477. https:\/\/doi.org\/10.1109\/TNSRE.2020.2966249","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"4613_CR2","doi-asserted-by":"publisher","unstructured":"Lee MH, Siewiorek DP, Smailagic A, Bernardino A, Badia SBi (2019) Learning to assess the quality of stroke rehabilitation exercises. In: Proceedings of the 24th international conference on intelligent user interfaces. IUI \u201919, Association for Computing Machinery, pp 218\u2013228. https:\/\/doi.org\/10.1145\/3301275.3302273","DOI":"10.1145\/3301275.3302273"},{"key":"4613_CR3","doi-asserted-by":"publisher","first-page":"118969","DOI":"10.1109\/ACCESS.2020.3005189","volume":"8","author":"D Tang","year":"2020","unstructured":"Tang D (2020) Hybridized hierarchical deep convolutional neural network for sports rehabilitation exercises. IEEE Access 8:118969\u2013118977. https:\/\/doi.org\/10.1109\/ACCESS.2020.3005189","journal-title":"IEEE Access"},{"key":"4613_CR4","doi-asserted-by":"publisher","unstructured":"Du C, Graham S, Depp C, Nguyen T (2021) Assessing physical rehabilitation exercises using graph convolutional network with self-supervised regularization. In: 2021 43rd annual international conference of the IEEE engineering in medicine biology society (EMBC), pp 281\u2013285. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629569","DOI":"10.1109\/EMBC46164.2021.9629569"},{"key":"4613_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107388","volume":"229","author":"L-J Dong","year":"2021","unstructured":"Dong L-J, Zhang H-B, Shi Q, Lei Q, Du J-X, Gao S (2021) Learning and fusing multiple hidden substages for action quality assessment. Knowledge-Based Syst 229:107388. https:\/\/doi.org\/10.1016\/j.knosys.2021.107388","journal-title":"Knowledge-Based Syst"},{"key":"4613_CR6","doi-asserted-by":"crossref","unstructured":"Lei Q, Zhang H, Du J (2021) Temporal attention learning for action quality assessment in sports video. Signal, Image Video Process 1575\u20131583","DOI":"10.1007\/s11760-021-01890-w"},{"key":"4613_CR7","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-030-20876-9_10","volume-title":"Computer vision - ACCV 2018","author":"Y Li","year":"2019","unstructured":"Li Y, Chai X, Chen X (2019) Scoringnet: Learning key fragment for action quality assessment with ranking loss in skilled sports. In: Jawahar CV, Li H, Mori G, Schindler K (eds) Computer vision - ACCV 2018. Springer, Cham, pp 149\u2013164"},{"key":"4613_CR8","doi-asserted-by":"publisher","unstructured":"Parmar P, Morris B (2019) Action quality assessment across multiple actions. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 1468\u20131476 https:\/\/doi.org\/10.1109\/WACV.2019.00161","DOI":"10.1109\/WACV.2019.00161"},{"key":"4613_CR9","doi-asserted-by":"publisher","unstructured":"Doughty H, Mayol-Cuevas W, Damen D (2019) The pros and cons: Rank-aware temporal attention for skill determination in long videos. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7854\u20137863. https:\/\/doi.org\/10.1109\/CVPR.2019.00805","DOI":"10.1109\/CVPR.2019.00805"},{"key":"4613_CR10","doi-asserted-by":"publisher","unstructured":"Doughty H, Damen D, Mayol-Cuevas W (2018) Who\u2019s better? who\u2019s best? pairwise deep ranking for skill determination. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 6057\u20136066. https:\/\/doi.org\/10.1109\/CVPR.2018.00634","DOI":"10.1109\/CVPR.2018.00634"},{"issue":"3","key":"4613_CR11","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s11548-018-1704-z","volume":"13","author":"A Zia","year":"2018","unstructured":"Zia A, Sharma Y, Bettadapura V, Sarin EL, Essa I (2018) Video and accelerometer-based motion analysis for automated surgical skills assessment. International Journal of Computer Assisted Radiology and Surgery 13(3):443\u2013455","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"4613_CR12","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s11548-016-1468-2","volume":"11","author":"A Zia","year":"2016","unstructured":"Zia A, Sharma Y, Bettadapura V, Sarin EL, Ploetz T, Clements MA, Essa I (2016) Automated video-based assessment of surgical skills for training and evaluation in medical schools. Int J Comput Assisted Radio Surg 11:1623\u20131636","journal-title":"Int J Comput Assisted Radio Surg"},{"key":"4613_CR13","doi-asserted-by":"publisher","unstructured":"Tang Y, Ni Z, Zhou J, Zhang D, Lu J, Wu Y, Zhou J (2020) Uncertainty-aware score distribution learning for action quality assessment. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9836\u20139845. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00986","DOI":"10.1109\/CVPR42600.2020.00986"},{"key":"4613_CR14","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1609\/aaai.v35i3.16314","volume":"35","author":"S Liu","year":"2021","unstructured":"Liu S, Zhang A, Li Y, Zhou J, Xu L, Dong Z, Zhang R (2021) Temporal segmentation of fine-grained semantic action: A motion-centered figure skating dataset. AAAI conference on artificial intelligence 35:2163\u20132171","journal-title":"AAAI conference on artificial intelligence"},{"key":"4613_CR15","doi-asserted-by":"publisher","unstructured":"Parmar P, Morris BT (2017) Learning to score olympic events. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 76\u201384. https:\/\/doi.org\/10.1109\/CVPRW.2017.16","DOI":"10.1109\/CVPRW.2017.16"},{"issue":"1","key":"4613_CR16","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/JBHI.2019.2904321","volume":"24","author":"A Elkholy","year":"2020","unstructured":"Elkholy A, Hussein ME, Gomaa W, Damen D, Saba E (2020) Efficient and robust skeleton-based quality assessment and abnormality detection in human action performance. IEEE J Biomed Health Inform 24(1):280\u2013291. https:\/\/doi.org\/10.1109\/JBHI.2019.2904321","journal-title":"IEEE J Biomed Health Inform"},{"key":"4613_CR17","doi-asserted-by":"crossref","unstructured":"Hakim T, Shimshoni I (2019) A-mal: Automatic motion assessment learning from properly performed motions in 3d skeleton videos. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 1589\u20131598","DOI":"10.1109\/ICCVW.2019.00198"},{"key":"4613_CR18","doi-asserted-by":"crossref","unstructured":"Zeng L-A, Hong, F-T, Zheng W-S, Yu Q-Z, Zeng W, Wang Y-W, Lai, J-H (2020) Hybrid dynamic-static context-aware attention network for action assessment in long videos In: Proceedings of the 28th ACM international conference on multimedia, pp. 2526\u20132534","DOI":"10.1145\/3394171.3413560"},{"key":"4613_CR19","doi-asserted-by":"crossref","unstructured":"Ismail\u00a0Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P-A (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention, pp 214\u2013221","DOI":"10.1007\/978-3-030-00937-3_25"},{"key":"4613_CR20","doi-asserted-by":"crossref","unstructured":"Forestier G, Petitjean F, Senin P, Despinoy F, Jannin P (2017) Discovering discriminative and interpretable patterns for surgical motion analysis. In: Conference on artificial intelligence in medicine in Europe, pp 136\u2013145","DOI":"10.1007\/978-3-319-59758-4_15"},{"key":"4613_CR21","doi-asserted-by":"crossref","unstructured":"Li Z, Huang Y, Cai M, Sato Y (2019) Manipulation-skill assessment from videos with spatial attention network. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 4385\u20134395","DOI":"10.1109\/ICCVW.2019.00539"},{"issue":"12","key":"4613_CR22","doi-asserted-by":"publisher","first-page":"4578","DOI":"10.1109\/TCSVT.2019.2927118","volume":"30","author":"C Xu","year":"2019","unstructured":"Xu C, Fu Y, Zhang B, Chen Z, Jiang Y-G, Xue X (2019) Learning to score figure skating sport videos. IEEE Trans Circuits Syst Video Technol 30(12):4578\u20134590","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4613_CR23","doi-asserted-by":"crossref","unstructured":"Parmar P, Morris BT (2016) Measuring the quality of exercises. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 2241\u20132244","DOI":"10.1109\/EMBC.2016.7591175"},{"key":"4613_CR24","doi-asserted-by":"publisher","unstructured":"Li Y, Chai X, Chen X (2018) End-to-end learning for action quality assessment. In: Advances in multimedia information processing \u2013 PCM 2018, Springer, pp 125\u2013134 https:\/\/doi.org\/10.1007\/978-3-030-00767-6_12","DOI":"10.1007\/978-3-030-00767-6_12"},{"key":"4613_CR25","doi-asserted-by":"crossref","unstructured":"Gao J, Zheng W-S, Pan J-H, Gao C, Wang Y, Zeng W, Lai J (2020) An asymmetric modeling for action assessment. In: European conference on computer vision, Springer, pp 222\u2013238","DOI":"10.1007\/978-3-030-58577-8_14"},{"key":"4613_CR26","doi-asserted-by":"crossref","unstructured":"Wang S, Yang D, Zhai P, Chen C, Zhang L (2021) Tsa-net: Tube self-attention network for action quality assessment. In: Proceedings of the 29th ACM international conference on multimedia, pp 4902\u20134910","DOI":"10.1145\/3474085.3475438"},{"issue":"6","key":"4613_CR27","doi-asserted-by":"publisher","first-page":"2260","DOI":"10.1109\/TCSVT.2020.3017727","volume":"31","author":"H Jain","year":"2020","unstructured":"Jain H, Harit G, Sharma A (2020) Action quality assessment using siamese network-based deep metric learning. IEEE Trans Circuits Systems Video Technol 31(6):2260\u20132273","journal-title":"IEEE Trans Circuits Systems Video Technol"},{"key":"4613_CR28","doi-asserted-by":"crossref","unstructured":"Yu X, Rao Y, Zhao W, Lu J, Zhou J (2021) Group-aware contrastive regression for action quality assessment. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7919\u20137928","DOI":"10.1109\/ICCV48922.2021.00782"},{"key":"4613_CR29","doi-asserted-by":"crossref","unstructured":"Pirsiavash H, Vondrick C, Torralba A (2014) Assessing the quality of actions. In: European conference on computer vision, Springer, pp 556\u2013571","DOI":"10.1007\/978-3-319-10599-4_36"},{"key":"4613_CR30","unstructured":"Bruce X, Liu Y, Chan KC (2020) Skeleton-based detection of abnormalities in human actions using graph convolutional networks. In: 2020 Second international conference on transdisciplinary AI (TransAI), IEEE, pp 131\u2013137"},{"key":"4613_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108095","volume":"119","author":"X Bruce","year":"2021","unstructured":"Bruce X, Liu Y, Chan KC, Yang Q, Wang X (2021) Skeleton-based human action evaluation using graph convolutional network for monitoring alzheimer\u2019s progression. Pattern Recogn 119:108095","journal-title":"Pattern Recogn"},{"key":"4613_CR32","doi-asserted-by":"crossref","unstructured":"Pan J-H, Gao J, Zheng W-S (2019) Action assessment by joint relation graphs. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6331\u20136340","DOI":"10.1109\/ICCV.2019.00643"},{"key":"4613_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3126534","author":"J-H Pan","year":"2021","unstructured":"Pan J-H, Gao J, Zheng W-S (2021) Adaptive action assessment. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3126534","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4613_CR34","doi-asserted-by":"publisher","unstructured":"Plizzari C, Cannici M, Matteucci M (2021) Skeleton-based action recognition via spatial and temporal transformer networks. Comput Vision Image Understanding 208\u2013209:103219. https:\/\/doi.org\/10.1016\/j.cviu.2021.103219","DOI":"10.1016\/j.cviu.2021.103219"},{"key":"4613_CR35","doi-asserted-by":"crossref","unstructured":"Nekoui M, Cruz FOT, Cheng L (2020) Falcons: Fast learner-grader for contorted poses in sports. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 900\u2013901","DOI":"10.1109\/CVPRW50498.2020.00458"},{"key":"4613_CR36","doi-asserted-by":"crossref","unstructured":"Nekoui M, Cruz FOT, Cheng L (2021) Eagle-eye: Extreme-pose action grader using detail bird\u2019s-eye view. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 394\u2013402","DOI":"10.1109\/WACV48630.2021.00044"},{"key":"4613_CR37","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S-E, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291\u20137299","DOI":"10.1109\/CVPR.2017.143"},{"key":"4613_CR38","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligence, pp 7444\u20137452","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"4613_CR39","doi-asserted-by":"publisher","first-page":"4870","DOI":"10.1109\/TIP.2019.2911488","volume":"28","author":"R Zhang","year":"2019","unstructured":"Zhang R, Li J, Sun H, Ge Y, Luo P, Wang X, Lin L (2019) Scan: Self-and-collaborative attention network for video person re-identification. IEEE Trans Image Process 28:4870\u20134882","journal-title":"IEEE Trans Image Process"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04613-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04613-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04613-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T13:13:05Z","timestamp":1697634785000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04613-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":39,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4613"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04613-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]},"assertion":[{"value":"5 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2023","order":2,"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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}