{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:44:52Z","timestamp":1778154292010,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"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 Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62267007"],"award-info":[{"award-number":["62267007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Gansu Provincial Department of Education Industral Support Plan Project","award":["2022CYZC-16"],"award-info":[{"award-number":["2022CYZC-16"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-023-08814-4","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T19:01:45Z","timestamp":1689361305000},"page":"19979-19995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["MFGCN: an efficient graph convolutional network based on multi-order feature information for human skeleton action recognition"],"prefix":"10.1007","volume":"35","author":[{"given":"Yongfeng","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongtao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"8814_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116566","volume":"195","author":"F Setiawan","year":"2022","unstructured":"Setiawan F, Yahya BN, Chun S et al (2022) Sequential inter-hop graph convolution neural network (sihgcn) for skeleton-based human action recognition. Expert Syst Appl 195:116566","journal-title":"Expert Syst Appl"},{"key":"8814_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104675","volume":"110","author":"C Ding","year":"2022","unstructured":"Ding C, Wen S, Ding W et al (2022) Temporal segment graph convolutional networks for skeleton-based action recognition. Eng Appl Artif Intell 110:104675","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"8814_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1111\/mice.12714","volume":"37","author":"J Chen","year":"2022","unstructured":"Chen J, Li S, Liu D, Lu W (2022) Indoor camera pose estimation via style-transfer 3d models. Comput Aided Civ Infrastruct Eng 37(3):335\u2013353","journal-title":"Comput Aided Civ Infrastruct Eng"},{"key":"8814_CR4","doi-asserted-by":"publisher","first-page":"2782","DOI":"10.1109\/TIP.2022.3161081","volume":"31","author":"L Ke","year":"2022","unstructured":"Ke L, Chang M, Qi H, Lyu S (2022) Detposenet: Improving multi-person pose estimation via coarse-pose filtering. IEEE Trans Image Process 31:2782\u20132795","journal-title":"IEEE Trans Image Process"},{"key":"8814_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2021.04.023","volume":"569","author":"H Rao","year":"2021","unstructured":"Rao H, Xu S, Hu X et al (2021) Augmented skeleton based contrastive action learning with momentum lstm for unsupervised action recognition. Inf Sci 569:90\u2013109","journal-title":"Inf Sci"},{"issue":"6","key":"8814_CR6","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1109\/TCSVT.2020.3019293","volume":"31","author":"A Banerjee","year":"2020","unstructured":"Banerjee A, Singh PK, Sarkar R (2020) Fuzzy integral-based cnn classifier fusion for 3d skeleton action recognition. IEEE Trans Circ Syst Video Technol 31(6):2206\u20132216","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"8814_CR7","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2019.10.047","volume":"513","author":"T Huynh-The","year":"2020","unstructured":"Huynh-The T, Hua C-H, Ngo T-T et al (2020) Image representation of pose-transition feature for 3d skeleton-based action recognition. Inf Sci 513:112\u2013126","journal-title":"Inf Sci"},{"key":"8814_CR8","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang Z, Yuan C et al (2021) Channel-wise topology refinement graph convolution for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 13359\u201313368","DOI":"10.1109\/ICCV48922.2021.01311"},{"key":"8814_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3157033","author":"Y Song","year":"2022","unstructured":"Song Y, Zhang Z, Shan C, Wang L (2022) Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2022.3157033","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8814_CR10","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"},{"issue":"13","key":"8814_CR11","doi-asserted-by":"publisher","first-page":"4342","DOI":"10.3390\/s21134342","volume":"21","author":"V Silva","year":"2021","unstructured":"Silva V, Soares F, Le\u00e3o CP et al (2021) Skeleton driven action recognition using an image-based spatial-temporal representation and convolution neural network. Sensors 21(13):4342","journal-title":"Sensors"},{"key":"8814_CR12","doi-asserted-by":"crossref","unstructured":"Cheng K, Zhang Y, Cao C et al (2020) Decoupling gcn with dropgraph module for skeleton-based action recognition. In: European Conference on Computer Vision, pp 536\u2013553","DOI":"10.1007\/978-3-030-58586-0_32"},{"key":"8814_CR13","doi-asserted-by":"crossref","unstructured":"Huang L, Huang Y, Ouyang W, Wang L (2019) Hierarchical graph convolutional network for skeleton-based action recognition. In: International Conference on Image and Graphics, pp 93\u2013102","DOI":"10.1007\/978-3-030-34120-6_8"},{"key":"8814_CR14","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12026\u201312035","DOI":"10.1109\/CVPR.2019.01230"},{"key":"8814_CR15","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TIP.2021.3129117","volume":"31","author":"H Yang","year":"2021","unstructured":"Yang H, Yan D, Zhang L et al (2021) Feedback graph convolutional network for skeleton-based action recognition. IEEE Trans Image Process 31:164\u2013175","journal-title":"IEEE Trans Image Process"},{"key":"8814_CR16","doi-asserted-by":"crossref","unstructured":"Qin Z, Liu Y, Ji P, et al (2021) Fusing higher-order features in graph neural networks for skeleton-based action recognition. arXiv preprint arXiv:2105.01563","DOI":"10.1109\/TNNLS.2022.3201518"},{"key":"8814_CR17","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/LSP.2022.3144074","volume":"29","author":"J Chen","year":"2022","unstructured":"Chen J, Wang Z, Zeng K et al (2022) Rethinking lightweight: multiple angle strategy for efficient video action recognition. IEEE Signal Process Lett 29:498\u2013502","journal-title":"IEEE Signal Process Lett"},{"key":"8814_CR18","doi-asserted-by":"publisher","first-page":"6001","DOI":"10.1007\/s10489-020-02176-3","volume":"51","author":"MS Islam","year":"2021","unstructured":"Islam MS, Bakhat K, Khan R et al (2021) Action recognition using interrelationships of 3d joints and frames based on angle sine relation and distance features using interrelationships. Appl Intell 51:6001\u20136013","journal-title":"Appl Intell"},{"key":"8814_CR19","doi-asserted-by":"crossref","unstructured":"Zhang P, Lan C, Zeng W et al (2020) Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1112\u20131121","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"8814_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ins.2018.02.042","volume":"444","author":"T Huynh-The","year":"2018","unstructured":"Huynh-The T, Hua C-H, Tu NA et al (2018) Hierarchical topic modeling with pose-transition feature for action recognition using 3d skeleton data. Inf Sci 444:20\u201335","journal-title":"Inf Sci"},{"key":"8814_CR21","doi-asserted-by":"crossref","unstructured":"Li M, Chen S, Chen X et al (2019) Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3595\u20133603","DOI":"10.1109\/CVPR.2019.00371"},{"key":"8814_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2019.05.008","volume":"110","author":"LP Cassinis","year":"2019","unstructured":"Cassinis LP, Fonod R, Gill E (2019) Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft. Progress Aerosp Sci 110:100548","journal-title":"Progress Aerosp Sci"},{"issue":"3","key":"8814_CR23","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1007\/s10462-020-09888-5","volume":"54","author":"G Du","year":"2021","unstructured":"Du G, Wang K, Lian S, Zhao K (2021) Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. Artif Intell Rev 54(3):1677\u20131734","journal-title":"Artif Intell Rev"},{"key":"8814_CR24","doi-asserted-by":"crossref","unstructured":"Liu Z, Zhang H, Chen Z et al (2020) Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 143\u2013152","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"8814_CR25","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.neucom.2021.02.001","volume":"440","author":"J Xie","year":"2021","unstructured":"Xie J, Miao Q, Liu R et al (2021) Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition. Neurocomputing 440:230\u2013239","journal-title":"Neurocomputing"},{"key":"8814_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107293","volume":"103","author":"Y Li","year":"2020","unstructured":"Li Y, Xia R, Liu X (2020) Learning shape and motion representations for view invariant skeleton-based action recognition. Pattern Recogn 103:107293","journal-title":"Pattern Recogn"},{"key":"8814_CR27","doi-asserted-by":"crossref","unstructured":"Cheng K, Zhang Y, He X et al (2020) Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 183\u2013192","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"8814_CR28","doi-asserted-by":"crossref","unstructured":"Li L, Wang M, Ni B et al (2021) 3d human action representation learning via cross-view consistency pursuit. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4741\u20134750","DOI":"10.1109\/CVPR46437.2021.00471"},{"key":"8814_CR29","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1109\/TIP.2021.3051495","volume":"30","author":"X Hao","year":"2021","unstructured":"Hao X, Li J, Guo Y et al (2021) Hypergraph neural network for skeleton-based action recognition. IEEE Trans Image Process 30:2263\u20132275","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"8814_CR30","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1109\/TCSVT.2019.2914137","volume":"30","author":"X Jiang","year":"2019","unstructured":"Jiang X, Xu K, Sun T (2019) Action recognition scheme based on skeleton representation with ds-lstm network. IEEE Trans Circ Syst Video Technol 30(7):2129\u20132140","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"8814_CR31","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1109\/LSP.2022.3142675","volume":"29","author":"J Kong","year":"2022","unstructured":"Kong J, Bian Y, Jiang M (2022) Mtt: Multi-scale temporal transformer for skeleton-based action recognition. IEEE Signal Process Lett 29:528\u2013532","journal-title":"IEEE Signal Process Lett"},{"key":"8814_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari C, Cannici M, Matteucci M (2021) Skeleton-based action recognition via spatial and temporal transformer networks. Comput Vis Image Underst 208:103219","journal-title":"Comput Vis Image Underst"},{"key":"8814_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108146","volume":"240","author":"Y Liu","year":"2022","unstructured":"Liu Y, Zhang H, Xu D, He K (2022) Graph transformer network with temporal kernel attention for skeleton-based action recognition. Knowl Based Syst 240:108146","journal-title":"Knowl Based Syst"},{"key":"8814_CR34","doi-asserted-by":"crossref","unstructured":"He T, Zhang Z, Zhang H et al (2019) Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 558\u2013567","DOI":"10.1109\/CVPR.2019.00065"},{"key":"8814_CR35","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng T-T, Wang G (2016) Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1010\u20131019","DOI":"10.1109\/CVPR.2016.115"},{"issue":"10","key":"8814_CR36","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","volume":"42","author":"J Liu","year":"2019","unstructured":"Liu J, Shahroudy A, Perez M et al (2019) Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding. IEEE Trans Pattern Anal Mach Intell 42(10):2684\u20132701","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8814_CR37","doi-asserted-by":"crossref","unstructured":"Wang J, Nie X, Xia Y et al (2014) Cross-view action modeling, learning and recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2649\u20132656","DOI":"10.1109\/CVPR.2014.339"},{"key":"8814_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8814_CR39","doi-asserted-by":"crossref","unstructured":"Ye F, Pu S, Zhong Q et al (2020) Dynamic gcn: Context-enriched topology learning for skeleton-based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 55\u201363","DOI":"10.1145\/3394171.3413941"},{"key":"8814_CR40","doi-asserted-by":"crossref","unstructured":"Si C, Chen W, Wang W et al (2019) An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1227\u20131236","DOI":"10.1109\/CVPR.2019.00132"},{"issue":"8","key":"8814_CR41","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TPAMI.2019.2896631","volume":"41","author":"P Zhang","year":"2019","unstructured":"Zhang P, Lan C, Xing J et al (2019) View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans Pattern Anal Mach Intell 41(8):1963\u20131978","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8814_CR42","doi-asserted-by":"crossref","unstructured":"Peng W, Hong X, Chen H, Zhao G (2020) Learning graph convolutional network for skeleton-based human action recognition by neural searching. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 2669\u20132676","DOI":"10.1609\/aaai.v34i03.5652"},{"key":"8814_CR43","doi-asserted-by":"crossref","unstructured":"Huang L, Huang Y, Ouyang W, Wang L (2020) Part-level graph convolutional network for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 11045\u201311052","DOI":"10.1609\/aaai.v34i07.6759"},{"key":"8814_CR44","doi-asserted-by":"crossref","unstructured":"Korban M, Li X (2020) Ddgcn: A dynamic directed graph convolutional network for action recognition. In: European Conference on Computer Vision, pp 761\u2013776","DOI":"10.1007\/978-3-030-58565-5_45"},{"key":"8814_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107921","volume":"115","author":"W Peng","year":"2021","unstructured":"Peng W, Hong X, Zhao G (2021) Tripool: Graph triplet pooling for 3d skeleton-based action recognition. Pattern Recogn 115:107921","journal-title":"Pattern Recogn"},{"key":"8814_CR46","doi-asserted-by":"crossref","unstructured":"Si C, Jing Y, Wang W et al (2018) Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 103\u2013118","DOI":"10.1007\/978-3-030-01246-5_7"},{"issue":"5","key":"8814_CR47","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TCSVT.2020.3015051","volume":"31","author":"Y-F Song","year":"2020","unstructured":"Song Y-F, Zhang Z, Shan C, Wang L (2020) Richly activated graph convolutional network for robust skeleton-based action recognition. IEEE Trans Circ Syst Video Technol 31(5):1915\u20131925","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"8814_CR48","doi-asserted-by":"crossref","unstructured":"Ding C, Liu K, Korhonen J, Belyaev E (2021) Spatio-temporal difference descriptor for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 1227\u20131235","DOI":"10.1609\/aaai.v35i2.16210"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08814-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08814-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08814-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:07:34Z","timestamp":1693354054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08814-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":48,"journal-issue":{"issue":"27","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["8814"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08814-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]},"assertion":[{"value":"3 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 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 have no Conflict and competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}