{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:07:20Z","timestamp":1790197640402,"version":"4.1.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272236"],"award-info":[{"award-number":["62272236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-46093-w","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:28:40Z","timestamp":1774963720000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RehabMate: an explainable framework for action detection and corrective feedback in pediatric stroke rehabilitation"],"prefix":"10.1038","volume":"16","author":[{"given":"Shitao","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"46093_CR1","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s13311-023-01373-5","volume":"20","author":"LR Sun","year":"2023","unstructured":"Sun, L. R. & Lynch, J. K. Advances in the diagnosis and treatment of pediatric arterial ischemic stroke. Neurotherapeutics 20, 633\u2013654 (2023).","journal-title":"Neurotherapeutics"},{"key":"46093_CR2","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2023-064477","volume":"154","author":"MM Yale","year":"2024","unstructured":"Yale, M. M. et al. Parent council for a pediatric stroke rehabilitation clinical trial. Pediatrics 154, e2023064477 (2024).","journal-title":"Pediatrics"},{"key":"46093_CR3","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s00530-024-01274-3","volume":"30","author":"Y Guo","year":"2024","unstructured":"Guo, Y., Liu, H., Sun, Y. & Ren, Y. Virtual human pose estimation in a fire education system for children with autism spectrum disorders. Multimedia Syst. 30, 84 (2024).","journal-title":"Multimedia Syst."},{"key":"46093_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10198-018-0984-0","volume":"20","author":"S Rajsic","year":"2019","unstructured":"Rajsic, S. et al. Economic burden of stroke: A systematic review on post-stroke care. Eur. J. Health Econ. 20, 107\u2013134 (2019).","journal-title":"Eur. J. Health Econ."},{"key":"46093_CR5","first-page":"2063","volume":"7","author":"D Ertelt","year":"2012","unstructured":"Ertelt, D. & Binkofski, F. Action observation as a tool for neurorehabilitation to moderate motor deficits and aphasia following stroke. Neural Regen. Res. 7, 2063\u20132074 (2012).","journal-title":"Neural Regen. Res."},{"key":"46093_CR6","doi-asserted-by":"publisher","DOI":"10.1111\/cch.13190","volume":"50","author":"CL Hurd","year":"2024","unstructured":"Hurd, C. L., Pritchard, L. & Yang, J. F. Perspectives of parents partnering with physical therapists to deliver intensive rehabilitation for their young children with perinatal stroke: a qualitative study. Child: Care, Health and Development 50, e13190 (2024).","journal-title":"Child: Care, Health and Development"},{"key":"46093_CR7","doi-asserted-by":"crossref","unstructured":"Mohottala, S., Abeygunawardana, S., Samarasinghe, P., Kasthurirathna, D. & Abhayaratne, C. 2d pose estimation based child action recognition. In TENCON 2022-2022 IEEE Region 10 Conference (TENCON), 1\u20137 (IEEE, 2022).","DOI":"10.1109\/TENCON55691.2022.9977799"},{"key":"46093_CR8","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1093\/gerona\/60.10.1304","volume":"60","author":"M Montero-Odasso","year":"2005","unstructured":"Montero-Odasso, M. et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J. Gerontol. A Biol. Sci. Med. Sci. 60, 1304\u20131309 (2005).","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"46093_CR9","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1161\/CIRCULATIONAHA.109.849166","volume":"121","author":"DM Lloyd-Jones","year":"2010","unstructured":"Lloyd-Jones, D. M. Cardiovascular risk prediction: Basic concepts, current status, and future directions. Circulation 121, 1768\u20131777 (2010).","journal-title":"Circulation"},{"key":"46093_CR10","doi-asserted-by":"publisher","first-page":"e100","DOI":"10.1161\/01.STR.0000180861.54180.FF","volume":"36","author":"PW Duncan","year":"2005","unstructured":"Duncan, P. W. et al. Management of adult stroke rehabilitation care: a clinical practice guideline. stroke 36, e100\u2013e143 (2005).","journal-title":"stroke"},{"key":"46093_CR11","doi-asserted-by":"publisher","first-page":"3851","DOI":"10.1109\/JBHI.2023.3332735","volume":"28","author":"F Jin","year":"2023","unstructured":"Jin, F., Zou, M., Peng, X., Lei, H. & Ren, Y. Deep learning-enhanced internet of things for activity recognition in post-stroke rehabilitation. IEEE J. Biomed. Health Inform. 28, 3851\u20133859 (2023).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"46093_CR12","doi-asserted-by":"crossref","unstructured":"Postolache, O., Viegas, V. & Pereira, J. M.\u00a0D. Gait assesment using microwave radars mounted on standard walker. In 2024 IEEE International Conference And Exposition On Electric And Power Engineering (EPEi), 687\u2013692 (IEEE, 2024).","DOI":"10.1109\/EPEi63510.2024.10758068"},{"key":"46093_CR13","doi-asserted-by":"publisher","DOI":"10.2196\/20641","volume":"22","author":"E Park","year":"2020","unstructured":"Park, E., Lee, K., Han, T. & Nam, H. S. Automatic grading of stroke symptoms for rapid assessment using optimized machine learning and 4-limb kinematics: Clinical validation study. J. Med. Internet Res. 22, e20641 (2020).","journal-title":"J. Med. Internet Res."},{"key":"46093_CR14","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1049\/iet-cvi.2018.5274","volume":"12","author":"N Eichler","year":"2018","unstructured":"Eichler, N. et al. 3d motion capture system for assessing patient motion during fugl-meyer stroke rehabilitation testing. IET Comput. Vis. 12, 963\u2013975 (2018).","journal-title":"IET Comput. Vis."},{"key":"46093_CR15","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/17434440.2023.2171289","volume":"20","author":"S Spanos","year":"2023","unstructured":"Spanos, S. et al. Reliability and applicability of a low-cost, camera-based gait evaluation method for clinical use. Expert Rev. Med. Devices 20, 63\u201370 (2023).","journal-title":"Expert Rev. Med. Devices"},{"key":"46093_CR16","doi-asserted-by":"publisher","first-page":"286","DOI":"10.3390\/jpm10040286","volume":"10","author":"TS Heo","year":"2020","unstructured":"Heo, T. S. et al. Prediction of stroke outcome using natural language processing-based machine learning of radiology report of brain mri. J. Pers. Med. 10, 286 (2020).","journal-title":"J. Pers. Med."},{"key":"46093_CR17","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s12028-022-01513-3","volume":"37","author":"MI Miller","year":"2022","unstructured":"Miller, M. I. et al. Natural language processing of radiology reports to detect complications of ischemic stroke. Neurocrit. Care 37, 291\u2013302 (2022).","journal-title":"Neurocrit. Care"},{"key":"46093_CR18","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.027672","volume":"12","author":"DM Kent","year":"2023","unstructured":"Kent, D. M. et al. Association of incidentally discovered covert cerebrovascular disease identified using natural language processing and future dementia. J. Am. Heart Assoc. 12, e027672 (2023).","journal-title":"J. Am. Heart Assoc."},{"key":"46093_CR19","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.023486","volume":"10","author":"S-F Sung","year":"2021","unstructured":"Sung, S.-F., Chen, C.-H., Pan, R.-C., Hu, Y.-H. & Jeng, J.-S. Natural language processing enhances prediction of functional outcome after acute ischemic stroke. J. Am. Heart Assoc. 10, e023486 (2021).","journal-title":"J. Am. Heart Assoc."},{"key":"46093_CR20","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12911-019-1010-x","volume":"20","author":"E Kogan","year":"2020","unstructured":"Kogan, E. et al. Assessing stroke severity using electronic health record data: A machine learning approach. BMC Med. Inform. Decis. Mak. 20, 8 (2020).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"46093_CR21","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1109\/TNSRE.2019.2908125","volume":"27","author":"D Rathee","year":"2019","unstructured":"Rathee, D. et al. Brain-machine interface-driven post-stroke upper-limb functional recovery correlates with beta-band mediated cortical networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 1020\u20131031 (2019).","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"46093_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2024.3431025","author":"J Su","year":"2024","unstructured":"Su, J. et al. An adaptive hybrid brain computer interface for hand function rehabilitation of stroke patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering https:\/\/doi.org\/10.1109\/TNSRE.2024.3431025 (2024).","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"46093_CR23","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s11063-022-10744-6","volume":"55","author":"V Bijalwan","year":"2023","unstructured":"Bijalwan, V., Semwal, V. B., Singh, G. & Mandal, T. K. HDL-PSR: Modelling spatio-temporal features using hybrid deep learning approach for post-stroke rehabilitation. Neural Process. Lett. 55, 279\u2013298 (2023).","journal-title":"Neural Process. Lett."},{"key":"46093_CR24","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/s10489-022-03436-0","volume":"53","author":"W Yang","year":"2023","unstructured":"Yang, W., Zhang, J., Cai, J. & Xu, Z. Hybridnet: Integrating GCN and CNN for skeleton-based action recognition. Appl. Intell. 53, 574\u2013585 (2023).","journal-title":"Appl. Intell."},{"key":"46093_CR25","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y. & Lin, D. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, vol.\u00a032 (2018).","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"46093_CR26","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J. & Lu, H. Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 12026\u201312035 (2019).","DOI":"10.1109\/CVPR.2019.01230"},{"key":"46093_CR27","doi-asserted-by":"publisher","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","volume":"29","author":"L Shi","year":"2020","unstructured":"Shi, L., Zhang, Y., Cheng, J. & Lu, H. Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans. Image Process. 29, 9532\u20139545 (2020).","journal-title":"IEEE Trans. Image Process."},{"key":"46093_CR28","unstructured":"Veli\u010dkovi\u0107, P. et al. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"46093_CR29","unstructured":"Sankar, A., Wu, Y., Gou, L., Zhang, W. & Yang, H. Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430 (2018)."},{"key":"46093_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3978-y","volume":"67","author":"L Sun","year":"2024","unstructured":"Sun, L., Wang, Y., Ren, Y. & Xia, F. Path signature-based XAI-enabled network time series classification. Sci. China Inf. Sci. 67, 170305 (2024).","journal-title":"Sci. China Inf. Sci."},{"key":"46093_CR31","doi-asserted-by":"crossref","unstructured":"Duhme, M., Memmesheimer, R. & Paulus, D. Fusion-gcn: Multimodal action recognition using graph convolutional networks. In DAGM German conference on pattern recognition, 265\u2013281 (Springer, 2021).","DOI":"10.1007\/978-3-030-92659-5_17"},{"key":"46093_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000467","volume":"3","author":"J Stenum","year":"2024","unstructured":"Stenum, J., Hsu, M. M., Pantelyat, A. Y. & Roemmich, R. T. Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change. PLoS Digit. Health 3, e0000467 (2024).","journal-title":"PLoS Digit. Health"},{"key":"46093_CR33","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.future.2023.02.011","volume":"143","author":"X Yu","year":"2023","unstructured":"Yu, X., Zhu, S. & Ren, Y. Continuous trajectory similarity search with result diversification. Future Gener. Comput. Syst. 143, 392\u2013400 (2023).","journal-title":"Future Gener. Comput. Syst."},{"key":"46093_CR34","doi-asserted-by":"crossref","unstructured":"Duan, H., Wang, J., Chen, K. & Lin, D. Pyskl: Towards good practices for skeleton action recognition. In Proceedings of the 30th ACM international conference on multimedia, 7351\u20137354 (2022).","DOI":"10.1145\/3503161.3548546"},{"key":"46093_CR35","unstructured":"Liu, Y., Shao, Z., Teng, Y. & Hoffmann, N. Nam: Normalization-based attention module. arXiv preprint arXiv:2111.12419 (2021)."},{"key":"46093_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Z. et al. Open-book video captioning with retrieve-copy-generate network. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 9837\u20139846 (2021).","DOI":"10.1109\/CVPR46437.2021.00971"},{"key":"46093_CR37","doi-asserted-by":"crossref","unstructured":"Shan, W. et al. Diffusion-based 3d human pose estimation with multi-hypothesis aggregation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 14761\u201314771 (2023).","DOI":"10.1109\/ICCV51070.2023.01356"},{"key":"46093_CR38","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu, C., Papava, D., Olaru, V. & Sminchisescu, C. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36, 1325\u20131339 (2013).","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"46093_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X. et al. Vatex: A large-scale, high-quality multilingual dataset for video-and-language research. In Proceedings of the IEEE\/CVF international conference on computer vision, 4581\u20134591 (2019).","DOI":"10.1109\/ICCV.2019.00468"},{"key":"46093_CR40","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L. & Tan, T. 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, 1227\u20131236 (2019).","DOI":"10.1109\/CVPR.2019.00132"},{"key":"46093_CR41","unstructured":"Bai, S., Kolter, J.\u00a0Z. & Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)."},{"key":"46093_CR42","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TVCG.2023.3247075","volume":"29","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Zhang, H., Li, Y., He, K. & Xu, D. Skeleton-based human action recognition via large-kernel attention graph convolutional network. IEEE Trans. Vis. Comput. Graph. 29, 2575\u20132585 (2023).","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"46093_CR43","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/BF02295996","volume":"12","author":"Q McNemar","year":"1947","unstructured":"McNemar, Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 153\u2013157 (1947).","journal-title":"Psychometrika"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46093-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46093-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46093-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:30:11Z","timestamp":1779208211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-46093-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["46093"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-46093-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"12 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Medical Ethics Committee of Children\u2019s Hospital of Nanjing Medical University (Approval No. 202502009-1. Starting from 2024\/12\/10 to 2025\/2\/11). All procedures were conducted in accordance with the \u201dMedical Ethics Committee Charter\u201d of Children\u2019s Hospital of Nanjing Medical University. Informed consent was obtained from all participants\u2019 legal guardians.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"15565"}}