{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:14Z","timestamp":1740160274907,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2019YFA0706200","2019YFB1703600"],"award-info":[{"award-number":["2019YFA0706200","2019YFB1703600"]}]},{"name":"the National Natural Science Foundation of China grant","award":["U1813203","U1801262","61751202","61751205"],"award-info":[{"award-number":["U1813203","U1801262","61751202","61751205"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s13042-020-01132-4","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T22:03:32Z","timestamp":1588716212000},"page":"2625-2636","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["View-independent representation with frame interpolation method for skeleton-based human action recognition"],"prefix":"10.1007","volume":"11","author":[{"given":"Yingguo","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-6365","authenticated-orcid":false,"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,5]]},"reference":[{"issue":"4","key":"1132_CR1","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/JAS.2017.7510604","volume":"4","author":"W He","year":"2017","unstructured":"He W, Li Z, Chen CP (2017) A survey of human-centered intelligent robots: issues and challenges. IEEE\/CAA J Autom Sin 4(4):602\u2013609","journal-title":"IEEE\/CAA J Autom Sin"},{"doi-asserted-by":"crossref","unstructured":"Zhang T, Wang X, Xu X, Chen CP (2019) Gcb-net: graph convolutional broad network and its application in emotion recognition. IEEE Trans Affect Comput","key":"1132_CR2","DOI":"10.1109\/TAFFC.2019.2937768"},{"issue":"4","key":"1132_CR3","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1109\/LSP.2014.2361901","volume":"22","author":"Y Song","year":"2014","unstructured":"Song Y, Liu S, Tang J (2014) Describing trajectory of surface patch for human action recognition on rgb and depth videos. IEEE Signal Process Lett 22(4):426\u2013429","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"1132_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13042-010-0009-5","volume":"2","author":"S Zhang","year":"2011","unstructured":"Zhang S, McCullagh P, Nugent C, Zheng H, Baumgarten M (2011) Optimal model selection for posture recognition in home-based healthcare. Int J Mach Learn Cybern 2(1):1\u201314","journal-title":"Int J Mach Learn Cybern"},{"key":"1132_CR5","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.patcog.2017.07.013","volume":"72","author":"EP Ijjina","year":"2017","unstructured":"Ijjina EP, Chalavadi KM (2017) Human action recognition in rgb-d videos using motion sequence information and deep learning. Pattern Recognit 72:504\u2013516","journal-title":"Pattern Recognit"},{"issue":"9","key":"1132_CR6","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1007\/s13042-018-0870-1","volume":"10","author":"H Naveed","year":"2019","unstructured":"Naveed H, Khan G, Khan AU, Siddiqi A, Khan MUG (2019) Human activity recognition using mixture of heterogeneous features and sequential minimal optimization. Int J Mach Learn Cybern 10(9):2329\u20132340","journal-title":"Int J Mach Learn Cybern"},{"issue":"12","key":"1132_CR7","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1007\/s13042-019-00947-0","volume":"10","author":"H Arshad","year":"2019","unstructured":"Arshad H, Khan MA, Sharif M, Yasmin M, Javed MY (2019) Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int J Mach Learn Cybern 10(12):3601\u20133618","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"1132_CR8","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s13042-017-0714-4","volume":"10","author":"Q Zhao","year":"2019","unstructured":"Zhao Q, Tsai CM, Chen RC, Huang CY (2019) Resident activity recognition based on binary infrared sensors and soft computing. Int J Mach Learn Cybern 10(2):291\u2013299","journal-title":"Int J Mach Learn Cybern"},{"doi-asserted-by":"crossref","unstructured":"Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 1290\u20131297","key":"1132_CR9","DOI":"10.1109\/CVPR.2012.6247813"},{"unstructured":"Hussein ME, Torki M, Gowayyed MA, El-Saban M (2013) Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations. In: Twenty-third international joint conference on artificial intelligence","key":"1132_CR10"},{"issue":"1","key":"1132_CR11","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/THMS.2014.2362520","volume":"45","author":"C Chen","year":"2014","unstructured":"Chen C, Jafari R, Kehtarnavaz N (2014) Improving human action recognition using fusion of depth camera and inertial sensors. IEEE Trans Hum Mach Syst 45(1):51\u201361","journal-title":"IEEE Trans Hum Mach Syst"},{"doi-asserted-by":"crossref","unstructured":"Wasenm\u00fcller O, Stricker D (2016) Comparison of kinect v1 and v2 depth images in terms of accuracy and precision. In: Asian conference on computer vision. Springer, pp 34\u201345","key":"1132_CR12","DOI":"10.1007\/978-3-319-54427-4_3"},{"doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d CNNS retrace the history of 2D CNNS and imagenet? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6546\u20136555","key":"1132_CR13","DOI":"10.1109\/CVPR.2018.00685"},{"unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008","key":"1132_CR14"},{"issue":"8","key":"1132_CR15","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, Zeng W, Xue J, Zheng N (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":"1132_CR16","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu M, Liu H, Chen C (2017) Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognit 68:346\u2013362","journal-title":"Pattern Recognit"},{"doi-asserted-by":"crossref","unstructured":"Papadakis A, Mathe E, Spyrou E, Mylonas P (2019) A geometric approach for cross-view human action recognition using deep learning. In: 2019 11th international symposium on image and signal processing and analysis (ISPA). IEEE, pp 258\u2013263","key":"1132_CR17","DOI":"10.1109\/ISPA.2019.8868717"},{"doi-asserted-by":"crossref","unstructured":"Song S, Lan C, Xing J, Zeng W, Liu J (2017) An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Thirty-first AAAI conference on artificial intelligence","key":"1132_CR18","DOI":"10.1609\/aaai.v31i1.11212"},{"doi-asserted-by":"crossref","unstructured":"Si C, Chen W, Wang W, Wang L, Tan T (2019) An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1227\u20131236","key":"1132_CR19","DOI":"10.1109\/CVPR.2019.00132"},{"doi-asserted-by":"crossref","unstructured":"Yang Z, Li Y, Yang J, Luo J (2018) Action recognition with visual attention on skeleton images. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 3309\u20133314","key":"1132_CR20","DOI":"10.1109\/ICPR.2018.8546012"},{"doi-asserted-by":"crossref","unstructured":"Shoemake K (1985) Animating rotation with quaternion curves. In: Proceedings of the 12th annual conference on computer graphics and interactive techniques, pp 245\u2013254","key":"1132_CR21","DOI":"10.1145\/325165.325242"},{"doi-asserted-by":"crossref","unstructured":"Zhao Y, Gao L, He D, Guo H, Wang H, Zheng J, Yang X (2019) Multi-feature fusion action recognition based on key frames. In: 2019 seventh international conference on advanced cloud and big data (CBD). IEEE, pp 279\u2013284","key":"1132_CR22","DOI":"10.1109\/CBD.2019.00057"},{"key":"1132_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-018-6670-5","volume":"78","author":"Y Xu","year":"2019","unstructured":"Xu Y, Hou Z, Liang J, Chen C, Jia L, Song Y (2019) Action recognition using weighted fusion of depth images and skeleton\u2019s key frames. Multimed Tools Appl 78:1\u201316","journal-title":"Multimed Tools Appl"},{"doi-asserted-by":"crossref","unstructured":"Xiao R, Hou Y, Guo Z, Li C, Wang P, Li W (2019) Self-attention guided deep features for action recognition. In: 2019 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1060\u20131065","key":"1132_CR24","DOI":"10.1109\/ICME.2019.00186"},{"unstructured":"Dietterich TG, Kong EB (1995) Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report, Department of Computer Science, Oregon State University","key":"1132_CR25"},{"issue":"5","key":"1132_CR26","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.brat.2009.12.008","volume":"48","author":"RE Zinbarg","year":"2010","unstructured":"Zinbarg RE, Mineka S, Craske MG, Griffith JW, Sutton J, Rose RD, Nazarian M, Mor N, Waters AM (2010) The northwestern-ucla youth emotion project: associations of cognitive vulnerabilities, neuroticism and gender with past diagnoses of emotional disorders in adolescents. Behav Res Therapy 48(5):347\u2013358","journal-title":"Behav Res Therapy"},{"doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng TT, 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","key":"1132_CR27","DOI":"10.1109\/CVPR.2016.115"},{"unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980","key":"1132_CR28"},{"unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105","key":"1132_CR29"},{"unstructured":"Akiba T, Suzuki S, Fukuda K (2017) Extremely large minibatch sgd: training resnet-50 on imagenet in 15 minutes. arXiv preprint arXiv:171104325","key":"1132_CR30"},{"doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","key":"1132_CR31","DOI":"10.1109\/CVPR.2017.195"},{"doi-asserted-by":"crossref","unstructured":"Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3d skeletons as points in a lie group. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 588\u2013595","key":"1132_CR32","DOI":"10.1109\/CVPR.2014.82"},{"key":"1132_CR33","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.ins.2018.12.050","volume":"480","author":"Y Xiao","year":"2019","unstructured":"Xiao Y, Chen J, Wang Y, Cao Z, Zhou JT, Bai X (2019) Action recognition for depth video using multi-view dynamic images. Inf Sci 480:287\u2013304","journal-title":"Inf Sci"},{"key":"1132_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.patcog.2018.03.030","volume":"81","author":"H Wang","year":"2018","unstructured":"Wang H, Wang L (2018) Learning content and style: joint action recognition and person identification from human skeletons. Pattern Recognit 81:23\u201335","journal-title":"Pattern Recognit"},{"doi-asserted-by":"crossref","unstructured":"Lee I, Kim D, Kang S, Lee S (2017) Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks. In: Proceedings of the IEEE international conference on computer vision, pp 1012\u20131020","key":"1132_CR35","DOI":"10.1109\/ICCV.2017.115"},{"doi-asserted-by":"crossref","unstructured":"Anirudh R, Turaga P, Su J, Srivastava A (2015) Elastic functional coding of human actions: from vector-fields to latent variables. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3147\u20133155","key":"1132_CR36","DOI":"10.1109\/CVPR.2015.7298934"},{"doi-asserted-by":"crossref","unstructured":"Zhang P, Lan C, Zeng W, Xue J, Zheng N (2019) Semantics-guided neural networks for efficient skeleton-based human action recognition. arXiv preprint arXiv:190401189","key":"1132_CR37","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"1132_CR38","doi-asserted-by":"publisher","first-page":"3959","DOI":"10.1109\/TIP.2019.2907048","volume":"28","author":"Q Nie","year":"2019","unstructured":"Nie Q, Wang J, Wang X, Liu Y (2019) View-invariant human action recognition based on a 3d bio-constrained skeleton model. IEEE Trans Image Process 28:3959\u20133972","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"1132_CR39","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TMM.2018.2802648","volume":"20","author":"S Zhang","year":"2018","unstructured":"Zhang S, Yang Y, Xiao J, Liu X, Yang Y, Xie D, Zhuang Y (2018) Fusing geometric features for skeleton-based action recognition using multilayer lstm networks. IEEE Trans Multimed 20(9):2330\u20132343","journal-title":"IEEE Trans Multimed"},{"doi-asserted-by":"crossref","unstructured":"Wang P, Li W, Wan J, Ogunbona P, Liu X (2018) Cooperative training of deep aggregation networks for rgb-d action recognition. In: Thirty-second AAAI conference on artificial intelligence","key":"1132_CR40","DOI":"10.1609\/aaai.v32i1.12228"},{"key":"1132_CR41","first-page":"8561","volume":"33","author":"B Li","year":"2019","unstructured":"Li B, Li X, Zhang Z, Wu F (2019) Spatio-temporal graph routing for skeleton-based action recognition. Proc AAAI Conf Artif Intell 33:8561\u20138568","journal-title":"Proc AAAI Conf Artif Intell"},{"doi-asserted-by":"crossref","unstructured":"Xie C, Li C, Zhang B, Chen C, Han J, Zou C, Liu J (2018) Memory attention networks for skeleton-based action recognition. arXiv preprint arXiv:180408254","key":"1132_CR42","DOI":"10.24963\/ijcai.2018\/227"},{"key":"1132_CR43","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neucom.2019.08.043","volume":"370","author":"J Zhu","year":"2019","unstructured":"Zhu J, Zou W, Zhu Z, Hu Y (2019) Convolutional relation network for skeleton-based action recognition. Neurocomputing 370:109\u2013117","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7912\u20137921","key":"1132_CR44","DOI":"10.1109\/CVPR.2019.00810"},{"unstructured":"Zhang T, Su G, Qing C, Xu X, Cai B, Xing X (2019) Hierarchical lifelong learning by sharing representations and integrating hypothesis. IEEE Trans Syst Man Cybern Syst","key":"1132_CR45"},{"doi-asserted-by":"crossref","unstructured":"He W, Gao H, Zhou C, Yang C, Li Z (2020) Reinforcement learning control of a flexible two-link manipulator: an experimental investigation. IEEE Trans Syst Man Cybern Syst","key":"1132_CR46","DOI":"10.1109\/TSMC.2020.2975232"},{"issue":"12","key":"1132_CR47","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TCYB.2018.2846186","volume":"48","author":"T Zhang","year":"2018","unstructured":"Zhang T, Chen CP, Chen L, Xu X, Hu B (2018) Design of highly nonlinear substitution boxes based on i-ching operators. IEEE Trans Cybern 48(12):3349\u20133358","journal-title":"IEEE Trans Cybern"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01132-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01132-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01132-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T04:11:57Z","timestamp":1666498317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01132-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,5]]},"references-count":47,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1132"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01132-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2020,5,5]]},"assertion":[{"value":"28 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}