{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T02:14:45Z","timestamp":1782785685832,"version":"3.54.5"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["Ministry of Education"],"award-info":[{"award-number":["Ministry of Education"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2221-E-003-007"],"award-info":[{"award-number":["NSTC 112-2221-E-003-007"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2221-E-003-008"],"award-info":[{"award-number":["NSTC 112-2221-E-003-008"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2221-E-003-010"],"award-info":[{"award-number":["NSTC 112-2221-E-003-010"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10489-024-06082-w","type":"journal-article","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T19:00:43Z","timestamp":1736622043000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Skeleton-based human action recognition using LSTM and depthwise separable convolutional neural network"],"prefix":"10.1007","volume":"55","author":[{"given":"Hoangcong","family":"Le","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5819-0754","authenticated-orcid":false,"given":"Cheng-Kai","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Chien","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao-Kang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,11]]},"reference":[{"key":"6082_CR1","doi-asserted-by":"publisher","first-page":"4145","DOI":"10.1007\/s00521-022-07937-4","volume":"35","author":"G Al Saleem","year":"2023","unstructured":"Al Saleem G, Bajwa UI, Raza RH (2023) Toward human activity recognition: a survey. Neural Comput Applic 35:4145\u20134182","journal-title":"Neural Comput Applic"},{"key":"6082_CR2","doi-asserted-by":"publisher","first-page":"24142","DOI":"10.1007\/s10489-023-04808-w","volume":"53","author":"N Wu","year":"2023","unstructured":"Wu N, Kera H, Kawamoto K (2023) Improving zero-shot action recognition using human instruction with text description. Appl Intell 53:24142\u201324156","journal-title":"Appl Intell"},{"key":"6082_CR3","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.image.2018.09.003","volume":"71","author":"Chih-Yao","year":"2019","unstructured":"Chih-Yao, Ma et al (2019) TS-LSTM and temporal-inception: exploiting spatiotemporal dynamics for activity recognition. Sig Process Image Commun 71:76\u201387","journal-title":"Sig Process Image Commun"},{"key":"6082_CR4","volume-title":"Computer vision \u2013 ECCV 2020 Workshops. ECCV 2020","author":"ME Kalfaoglu","year":"2020","unstructured":"Kalfaoglu ME et al (2020) Late temporal modeling in 3D CNN architectures with BERT for action recognition. In: Bartoli A, Fusiello A (eds) Computer vision \u2013 ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham"},{"key":"6082_CR5","doi-asserted-by":"crossref","unstructured":"Gowda SN, Rohrbach M, Sevilla-Lara L (2021) SMART frame selection for action recognition. In: 2021 the AAAI Conference on Artificial Intelligence. AAAI, pp 1451\u20131459","DOI":"10.1609\/aaai.v35i2.16235"},{"key":"6082_CR6","doi-asserted-by":"publisher","first-page":"3528","DOI":"10.1007\/s11227-023-05611-7","volume":"80","author":"SB Khobdeh","year":"2024","unstructured":"Khobdeh SB, Yamaghani MR, Sareshkeh SK (2024) Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network. J Supercomput 80:3528\u20133553","journal-title":"J Supercomput"},{"key":"6082_CR7","doi-asserted-by":"publisher","first-page":"4145","DOI":"10.1007\/s00521-022-07937-4","volume":"35","author":"G Saleem","year":"2023","unstructured":"Saleem G, Bajwa UI, Raza RH (2023) Toward human activity recognition: a survey. Neural Comput Applic 35:4145\u20134182","journal-title":"Neural Comput Applic"},{"key":"6082_CR8","doi-asserted-by":"publisher","unstructured":"Liu Y, Li Y, Zhang H, Zhang X, Xu D (2024) Decoupled knowledge embedded graph convolutional network for skeleton-based human action recognition. In: IEEE transactions on circuits and systems for Video Technology. https:\/\/doi.org\/10.1109\/TCSVT.2024.3399126","DOI":"10.1109\/TCSVT.2024.3399126"},{"key":"6082_CR9","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/TPAMI.2021.3107160","volume":"44","author":"P Koniusz","year":"2022","unstructured":"Koniusz P, Anoop Cherian (2022) Tensor representations for action recognition. IEEE Trans Pattern Anal Mach Intell 44:648\u2013665. https:\/\/doi.org\/10.1109\/TPAMI.2021.3107160","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6082_CR10","doi-asserted-by":"crossref","unstructured":"Wang H, Wang L (2017) Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks. In: 2017 the IEEE conference on computer vision and pattern recognition. IEEE, pp 499\u2013508","DOI":"10.1109\/CVPR.2017.387"},{"key":"6082_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_50","volume-title":"Computer vision \u2013 ECCV 2016. ECCV 2016","author":"J Liu","year":"2016","unstructured":"Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision \u2013 ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9907. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-46487-9_50."},{"key":"6082_CR12","doi-asserted-by":"publisher","first-page":"16526","DOI":"10.1109\/ACCESS.2023.3246127","volume":"11","author":"Z Xie","year":"2023","unstructured":"Xie Z, Zheng G, Miao L, Huang W (2023) STGL-GCN: spatial\u2013temporal mixing of global and local self-attention graph convolutional networks for human action recognition. IEEE Access 11:16526\u201316532","journal-title":"IEEE Access"},{"key":"6082_CR13","doi-asserted-by":"publisher","unstructured":"Shah A, et al (2022) Pose and joint-aware action recognition. In: 2022 the IEEE\/CVF Winter Conference on Applications of Computer Vision. IEEE, pp 3850\u20133860. https:\/\/doi.org\/10.1109\/WACV51458.2022.00022","DOI":"10.1109\/WACV51458.2022.00022"},{"key":"6082_CR14","doi-asserted-by":"publisher","first-page":"108487","DOI":"10.1016\/j.patcog.2021.108487","volume":"124","author":"Vittorio","year":"2022","unstructured":"Mazzia V, et al (2022) Action Transformer: a self-attention model for short-time pose-based human action recognition. Pattern Recogn 124:108487. https:\/\/doi.org\/10.1016\/j.patcog.2021.108487","journal-title":"Pattern Recogn"},{"key":"6082_CR15","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.3390\/app13042058","volume":"13","author":"J Shi","year":"2023","unstructured":"Shi J, Zhang Y, Wang W, Xing B, Hu D, Chen L (2023) A novel two-stream transformer-based framework for multi-modality human action recognition. Appl Sci 13:2058. https:\/\/doi.org\/10.3390\/app13042058","journal-title":"Appl Sci"},{"key":"6082_CR16","doi-asserted-by":"publisher","unstructured":"Ahn D, et al (2023) STAR-Transformer: a spatio-temporal cross attention transformer for human action recognition. In: 2023 the IEEE\/CVF Winter Conference on Applications of Computer Vision. IEEE, pp 3330\u20133339. https:\/\/doi.org\/10.1109\/WACV56688.2023.00333","DOI":"10.1109\/WACV56688.2023.00333"},{"key":"6082_CR17","doi-asserted-by":"publisher","first-page":"67541","DOI":"10.1109\/ACCESS.2022.3185058","volume":"10","author":"J Cha","year":"2022","unstructured":"Cha J, Saqlain M, Kim D, Lee S, Lee S, Baek S (2022) Learning 3D skeletal representation from transformer for action recognition. IEEE Access 10:67541\u201367550. https:\/\/doi.org\/10.1109\/ACCESS.2022.3185058","journal-title":"IEEE Access"},{"key":"6082_CR18","doi-asserted-by":"publisher","first-page":"102833","DOI":"10.1016\/j.jvcir.2020.102833","volume":"70","author":"X Liu","year":"2020","unstructured":"Liu X, Li Y, Guo T, Xia R (2020) Relative view based holistic-separate representations for two person interaction recognition using multiple graph convolutional networks. J Vis Commun Image Represent 70:102833","journal-title":"J Vis Commun Image Represent"},{"key":"6082_CR19","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1109\/TIP.2021.3056895","volume":"30","author":"C Bian","year":"2021","unstructured":"Bian C, Feng W, Wan L, Wang S (2021) Structural knowledge distillation for efficient skeleton-based action recognition. IEEE Trans Image Process 30:2963\u20132976. https:\/\/doi.org\/10.1109\/TIP.2021.3056895","journal-title":"IEEE Trans Image Process"},{"key":"6082_CR20","doi-asserted-by":"publisher","first-page":"109486","DOI":"10.1016\/j.sigpro.2024.109486","volume":"221","author":"Cuiwei","year":"2024","unstructured":"Liu C, et al (2024) Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation. Sig Process 221:109486","journal-title":"Sig Process"},{"key":"6082_CR21","unstructured":"Bazarevsky V, et al (2020) BlazePose: On-device real-time body pose tracking. arXiv preprint arXiv:2006.10204"},{"key":"6082_CR22","unstructured":"Google Research (posted by Ronny Votel and Na Li) (17 (2021) Next-generation pose detection with MoveNet and TensorFlow.j. https:\/\/blog.tensorflow.org\/2021\/05\/next-generation-pose-detection-with-movenet-and-tensorflowjs.html. Accessed 17 May 2021"},{"key":"6082_CR23","doi-asserted-by":"publisher","first-page":"120080","DOI":"10.1016\/j.eswa.2023.120080","volume":"226","author":"Ming","year":"2023","unstructured":"Yin M, et al (2023) Efficient skeleton-based action recognition via multi-stream depthwise separable convolutional neural network. Expert Syst Appl 226:120080. https:\/\/doi.org\/10.1016\/j.eswa.2023.120080","journal-title":"Expert Syst Appl"},{"key":"6082_CR24","doi-asserted-by":"publisher","first-page":"31065","DOI":"10.1007\/s10489-023-05173-4","volume":"53","author":"K Wu","year":"2023","unstructured":"Wu K, Gong X (2023) Asymmetric information-regularized learning for skeleton-based action recognition. Appl Intell 53:31065\u201331076. https:\/\/doi.org\/10.1007\/s10489-023-05173-4","journal-title":"Appl Intell"},{"key":"6082_CR25","doi-asserted-by":"publisher","first-page":"6760","DOI":"10.1007\/s10489-021-02760-1","volume":"52","author":"S Mi","year":"2022","unstructured":"Mi S, Zhang Y (2022) Pose-guided action recognition in static images using lie-group. Appl Intell 52:6760\u20136768. https:\/\/doi.org\/10.1007\/s10489-021-02760-1","journal-title":"Appl Intell"},{"key":"6082_CR26","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/s10489-021-02367-6","volume":"52","author":"Z Du","year":"2022","unstructured":"Du Z, Mukaidani H (2022) Linear dynamical systems approach for human action recognition with dual-stream deep features. Appl Intell 52:452\u2013470. https:\/\/doi.org\/10.1007\/s10489-021-02367-6","journal-title":"Appl Intell"},{"key":"6082_CR27","doi-asserted-by":"publisher","first-page":"7043","DOI":"10.1007\/s10489-021-02195-8","volume":"51","author":"G Jiang","year":"2021","unstructured":"Jiang G, Jiang X, Fang Z et al (2021) An efficient attention module for 3d convolutional neural networks in action recognition. Appl Intell 51:7043\u20137057. https:\/\/doi.org\/10.1007\/s10489-021-02195-8","journal-title":"Appl Intell"},{"key":"6082_CR28","doi-asserted-by":"publisher","first-page":"110461","DOI":"10.1016\/j.asoc.2023.110461","volume":"144","author":"Ziyu","year":"2023","unstructured":"Sheng Z, et al (2023) Residual LSTM based short-term load forecasting. Appl Soft Comput 144:110461","journal-title":"Appl Soft Comput"},{"key":"6082_CR29","doi-asserted-by":"publisher","first-page":"99152","DOI":"10.1109\/ACCESS.2019.2927134","volume":"7","author":"Abdu","year":"2019","unstructured":"Gumaei A, et al (2019) A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access 7:99152\u201399160. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927134","journal-title":"IEEE Access"},{"key":"6082_CR30","doi-asserted-by":"publisher","first-page":"9763","DOI":"10.1007\/s10489-022-03968-5","volume":"53","author":"Y Qi","year":"2023","unstructured":"Qi Y, Hu J, Zhuang L et al (2023) Semantic-guided multi-scale human skeleton action recognition. Appl Intell 53:9763\u20139778. https:\/\/doi.org\/10.1007\/s10489-022-03968-5","journal-title":"Appl Intell"},{"key":"6082_CR31","doi-asserted-by":"publisher","first-page":"17629","DOI":"10.1007\/s10489-022-04365-8","volume":"53","author":"H Zhang","year":"2023","unstructured":"Zhang H, Liu X, Yu D et al (2023) Skeleton-based action recognition with multi-stream, multi-scale dilated spatial-temporal graph convolution network. Appl Intell 53:17629\u201317643. https:\/\/doi.org\/10.1007\/s10489-022-04365-8","journal-title":"Appl Intell"},{"key":"6082_CR32","doi-asserted-by":"publisher","first-page":"17796","DOI":"10.1007\/s10489-022-04442-y","volume":"53","author":"Q Zhu","year":"2023","unstructured":"Zhu Q, Deng H (2023) Spatial adaptive graph convolutional network for skeleton-based action recognition. Appl Intell 53:17796\u201317808. https:\/\/doi.org\/10.1007\/s10489-022-04442-y","journal-title":"Appl Intell"},{"key":"6082_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_27","volume-title":"Computer vision \u2013 ECCV 2020. ECCV 2020","author":"Y Cai","year":"2020","unstructured":"Cai Y et al (2020) Learning delicate local representations for multi-person pose estimation. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) Computer vision \u2013 ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12348. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-58580-8_27."},{"key":"6082_CR34","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S, Sheikh Y (2021) OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43:172\u2013186. https:\/\/doi.org\/10.1109\/TPAMI.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6082_CR35","first-page":"38571","volume":"35","author":"Y Xu","year":"2022","unstructured":"Xu Y, Zhang J, Zhang Q, Tao D (2022) ViTPose: simple vision transformer baselines for human pose estimation. Adv Neural Inf Process Syst 35:38571\u201338584","journal-title":"Adv Neural Inf Process Syst"},{"key":"6082_CR36","unstructured":"Jayagopal JK (2021) Finding headache moments from youtube videos using weak supervision. Master\u2019s thesis, Texas A&M, University US. https:\/\/hdl.handle.net\/1969.1\/195104. Accessed 2021"},{"key":"6082_CR37","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"I G\u00fcl Varol","year":"2017","unstructured":"Varol G, Laptev I, Schmid C (2017) Long-term temporal convolutions for action recognition. IEEE Trans Pattern Anal Mach Intell 40:1510\u20131517. https:\/\/doi.org\/10.1109\/TPAMI.2017.2712608","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6082_CR38","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11571-022-09851-w","volume":"17","author":"S Chen","year":"2023","unstructured":"Chen S, Tang J, Zhu L (2023) A multi-stage dynamical fusion network for multimodal emotion recognition. Cogn Neurodyn 17:671\u2013680. https:\/\/doi.org\/10.1007\/s11571-022-09851-w","journal-title":"Cogn Neurodyn"},{"key":"6082_CR39","first-page":"2825","volume":"12","author":"Pedregosa","year":"2021","unstructured":"Pedregosa et al (2021) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"6082_CR40","doi-asserted-by":"publisher","unstructured":"Maharana K, Mondal S, Nemade B (2022) A review: Data pre-processing and data augmentation techniques. Glob Transit Proc 3:91\u201399. https:\/\/doi.org\/10.1016\/j.gltp.2022.04.020","DOI":"10.1016\/j.gltp.2022.04.020"},{"key":"6082_CR41","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1109\/TMM.2021.3050642","volume":"24","author":"M Perez","year":"2022","unstructured":"Perez M, Liu J, Kot AC (2022) Interaction relational network for mutual action recognition. IEEE Trans Multimedia 24:366\u2013376","journal-title":"IEEE Trans Multimedia"},{"key":"6082_CR42","doi-asserted-by":"publisher","unstructured":"Zhang W, et al (2013) From actemes to action: A strongly-supervised representation for detailed action understanding. In: 2013 the IEEE international conference on computer vision, 2013. IEEE, pp 2248\u20132255. https:\/\/doi.org\/10.1109\/ICCV.2013.280","DOI":"10.1109\/ICCV.2013.280"},{"key":"6082_CR43","doi-asserted-by":"publisher","unstructured":"Seidenari L, Varano V, Berretti S, Del Bimbo A, Pala P (2013) Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA, 2013. IEEE, pp 479\u2013485. https:\/\/doi.org\/10.1109\/CVPRW.2013.77","DOI":"10.1109\/CVPRW.2013.77"},{"key":"6082_CR44","doi-asserted-by":"publisher","unstructured":"Jhuang H, Gall J, Zuffi S, Schmid C, Black MJ (2013) Towards understanding action recognition. In: 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013. IEEE, pp 3192\u20133199. https:\/\/doi.org\/10.1109\/ICCV.2013.396","DOI":"10.1109\/ICCV.2013.396"},{"key":"6082_CR45","doi-asserted-by":"publisher","unstructured":"Yun K, Honorio J, Chattopadhyay D, Berg TL, Samaras D (2012) Two-person interaction detection using body-pose features and multiple instance learning. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 2012. IEEE, pp 28\u201335. https:\/\/doi.org\/10.1109\/CVPRW.2012.6239234","DOI":"10.1109\/CVPRW.2012.6239234"},{"key":"6082_CR46","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s11760-022-02203-5","volume":"17","author":"H Li","year":"2023","unstructured":"Li H et al (2023) Action recognition based on attention mechanism and depthwise separable residual module. SIViP 17:57\u201365","journal-title":"SIViP"},{"key":"6082_CR47","doi-asserted-by":"publisher","unstructured":"Sandler M, et al (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6082_CR48","doi-asserted-by":"publisher","first-page":"30397","DOI":"10.1007\/s11042-020-09486-1","volume":"79","author":"Erdal Tasci","year":"2020","unstructured":"Tasci E (2020) Voting combinations-based ensemble of fine-tuned convolutional neural networks for food image recognition. Multimedia Tools Appl 79:30397\u201330418. https:\/\/doi.org\/10.1007\/s11042-020-09486-1","journal-title":"Multimedia Tools Appl"},{"key":"6082_CR49","doi-asserted-by":"publisher","unstructured":"Yadav Y et al (2020) Analysis of facial sentiments: a deep-learning way. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India. IEEE, pp 541\u2013545. https:\/\/doi.org\/10.1109\/ICESC48915.2020.9155622","DOI":"10.1109\/ICESC48915.2020.9155622"},{"key":"6082_CR50","doi-asserted-by":"publisher","first-page":"119809","DOI":"10.1016\/j.eswa.2023.119809","volume":"222","author":"G Batchuluun","year":"2023","unstructured":"Batchuluun G et al (2023) CAM-CAN: class activation map-based categorical adversarial network. Expert Syst Appl 222:119809","journal-title":"Expert Syst Appl"},{"key":"6082_CR51","doi-asserted-by":"publisher","unstructured":"Yang F, Wu Y, Sakti S, Nakamura S (2019) Make skeleton-based action recognition model smaller, faster and better. In: 2019 the ACM multimedia asia. ACM, pp 1\u20136. https:\/\/doi.org\/10.1145\/3338533.3366569","DOI":"10.1145\/3338533.3366569"},{"key":"6082_CR52","doi-asserted-by":"publisher","unstructured":"Askar A, et al (2022) 2D Skeleton-based action recognition using action-snippets and sequential deep learning. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 2372\u20132377. https:\/\/doi.org\/10.1109\/SMC53654.2022.9945402","DOI":"10.1109\/SMC53654.2022.9945402"},{"key":"6082_CR53","doi-asserted-by":"publisher","first-page":"116732","DOI":"10.1109\/ACCESS.2023.3325401","volume":"11","author":"D Chen","year":"2023","unstructured":"Chen D, Wu M, Zhang T, Li C (2023) Feature fusion for dual-stream cooperative action recognition. IEEE Access 11:116732\u2013116740","journal-title":"IEEE Access"},{"key":"6082_CR54","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TETCI.2020.3014367","volume":"5","author":"SP Sahoo","year":"2021","unstructured":"Sahoo SP et al (2021) HAR-Depth: a novel framework for human action recognition using sequential learning and depth estimated history images. IEEE Trans Emerg Top Comput Intell 5:813\u2013825","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"6082_CR55","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s13735-023-00317-1","volume":"13","author":"A Mazari","year":"2024","unstructured":"Mazari A, Sahbi H (2024) Deep multiple aggregation networks for action recognition. Int J Multimed Info Retr 13:9. https:\/\/doi.org\/10.1007\/s13735-023-00317-1","journal-title":"Int J Multimed Info Retr"},{"key":"6082_CR56","doi-asserted-by":"publisher","unstructured":"Ludl D, Gulde T, Curio C (2019) Simple yet efficient real-time pose-based action recognition. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, pp 581\u2013588. https:\/\/doi.org\/10.1109\/ITSC.2019.8917128","DOI":"10.1109\/ITSC.2019.8917128"},{"key":"6082_CR57","doi-asserted-by":"publisher","unstructured":"Asghari-Esfeden S et al (2020) Dynamic motion representation for human action recognition. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA. IEEE, pp 546\u2013555. https:\/\/doi.org\/10.1109\/WACV45572.2020.9093500","DOI":"10.1109\/WACV45572.2020.9093500"},{"key":"6082_CR58","doi-asserted-by":"publisher","unstructured":"Tanfous AB et al (2018) Coding Kendall\u2019s shape trajectories for 3D action recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. IEEE, pp 2840\u20132849. https:\/\/doi.org\/10.1109\/CVPR.2018.00300","DOI":"10.1109\/CVPR.2018.00300"},{"key":"6082_CR59","first-page":"168","volume":"31","author":"Y Li","year":"2022","unstructured":"Li Y et al (2022) Action status based novel relative feature representations for interaction recognition. Chin J Electron 31:168\u2013180","journal-title":"Chin J Electron"},{"key":"6082_CR60","doi-asserted-by":"crossref","unstructured":"Monika et al (2023) Skeleton-based human activity recognition using bidirectional LSTM. In: Proceedings of International Conference on Intelligent Systems Design and Applications. Springer Nature, Cham, pp 150\u2013159","DOI":"10.1007\/978-3-031-35501-1_15"},{"key":"6082_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-71002-6_3","volume-title":"Intelligent Scene modeling and Human-Computer Interaction","author":"J Weng","year":"2021","unstructured":"Weng J, Jiang X, Yuan J (2021) NBNN-Based discriminative 3D action and gesture recognition. In: Thalmann NM, Zhang JJ, Ramanathan M, Thalmann D (eds) Intelligent scene modeling and human-computer interaction. Human\u2013computer interaction series. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-71002-6_3"},{"key":"6082_CR62","doi-asserted-by":"publisher","unstructured":"Zhao R et al (2019) Bayesian hierarchical dynamic model for human action recognition. In: 2019 the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. IEEE, pp 7725\u20137734. https:\/\/doi.org\/10.1109\/CVPR.2019.00792","DOI":"10.1109\/CVPR.2019.00792"},{"key":"6082_CR63","doi-asserted-by":"publisher","unstructured":"Shi L, et al (2019) Two stream adaptive graph convolutional networks for skeleton based action recognition. In: 2019 the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 12026\u201312035. https:\/\/doi.org\/10.1109\/CVPR.2019.01230","DOI":"10.1109\/CVPR.2019.01230"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06082-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06082-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06082-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T17:20:40Z","timestamp":1740244840000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06082-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,11]]},"references-count":63,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["6082"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06082-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,11]]},"assertion":[{"value":"17 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"298"}}