{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:37:15Z","timestamp":1782967035259,"version":"3.54.5"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":39,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>For a long time, dance education in Chinese universities has relied on teachers watching students and students practicing over and over again. This method often makes it hard to give objective feedback, correct mistakes quickly, and give personalized feedback, especially in big or diverse classes. In these circumstances, it is challenging to detect and rectify subtle biomechanical and rhythmic deviations using traditional teaching methods. Recent developments in artificial intelligence (AI) and wearable sensor technologies provide an alternative by facilitating continuous motion capture, quantitative movement analysis, and data-driven instructional support. This project creates an AI-based Dance Movement Teaching Support System (DM-TSS) that aims to improve the accuracy of motion acquisition, the reliability of feedback, and the effectiveness of instruction in higher education dance training. The system combines wearable inertial sensors with deep learning and reinforcement learning models to analyze dance movements that involve more than one joint in real time and give personalized feedback. We present a new framework called Namib Beetle Optimization\u2013Twin-Stage Hierarchical Deep Reinforcement Learning (NBO\u2013TSH-DRL) that helps with adaptive feature selection and hierarchical decision-making for classifying and evaluating dance movements. We used sensor data from traditional Chinese dance training sessions to test the experiment. The proposed framework outperformed baseline methods, such as GRU, 3D-CNN, and PSO-optimized models, achieving an accuracy of 97.9%, an F1-score of 0.98, and an AUC of 0.99. The system not only makes things work better, but it also lets teachers give real-time feedback and lets students see their movements through AI-assisted dashboards. These results show that combining AI with wearable sensing technologies can make dance education more objective, personalized, and consistent. The suggested method shows how intelligent teaching systems could help modernize dance training in Chinese universities while also helping to preserve cultural heritage and making teaching methods more flexible. Future efforts will concentrate on augmenting cross-style datasets, enhancing model generalization, and integrating multimodal feedback to facilitate wider educational implementation.<\/jats:p>","DOI":"10.1007\/s44163-026-01172-9","type":"journal-article","created":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T05:15:41Z","timestamp":1774761341000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of dance movement teaching support system based on artificial intelligence and wearable technology"],"prefix":"10.1007","volume":"6","author":[{"given":"Ting","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yayun","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,29]]},"reference":[{"key":"1172_CR1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129156425408113","author":"T Lu","year":"2025","unstructured":"Lu T, Xiao Y. Development and implementation effects on the evaluation of dance teaching system integrated with wearable devices. Int J High Speed Electron Syst. 2025. https:\/\/doi.org\/10.1142\/S0129156425408113.","journal-title":"Int J High Speed Electron Syst"},{"issue":"5","key":"1172_CR2","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3390\/asi8050127","volume":"8","author":"Y Zhong","year":"2025","unstructured":"Zhong Y, Fu X, Liang Z, Chen Q, Yao R, Ning H. The application of artificial intelligence technology in the field of dance. Appl Syst Innov. 2025;8(5):127. https:\/\/doi.org\/10.3390\/asi8050127.","journal-title":"Appl Syst Innov"},{"key":"1172_CR3","doi-asserted-by":"publisher","unstructured":"Jing W, Xiaolong Z. Wearable sensor-based motion data analysis and dance performance using images and cloud computing. Mobile Information Systems. 2022. https:\/\/doi.org\/10.1155\/2022\/4305073","DOI":"10.1155\/2022\/4305073"},{"key":"1172_CR4","doi-asserted-by":"publisher","unstructured":"Wang Z. 2024. Artificial intelligence in dance education: Using immersive technologies for teaching dance skills, Technology in Society, Elsevier, vol. 77(C). https:\/\/doi.org\/10.1016\/j.techsoc.2024.102579","DOI":"10.1016\/j.techsoc.2024.102579"},{"key":"1172_CR5","doi-asserted-by":"publisher","unstructured":"Zhang Y. (2022). Application of knowledge model in dance teaching based on wearable device based on deep learning. Computational Intelligence and Neuroscience. 2022. https:\/\/doi.org\/10.1155\/2022\/3299592","DOI":"10.1155\/2022\/3299592"},{"key":"1172_CR6","doi-asserted-by":"publisher","first-page":"32058","DOI":"10.1038\/s41598-024-83608-9","volume":"14","author":"J Qu","year":"2024","unstructured":"Qu J. A dance movement quality evaluation model using transformer encoder and convolutional neural network. Sci Rep. 2024;14:32058. https:\/\/doi.org\/10.1038\/s41598-024-83608-9.","journal-title":"Sci Rep"},{"key":"1172_CR7","doi-asserted-by":"publisher","DOI":"10.1051\/matecconf\/202236501056","author":"S Gao","year":"2022","unstructured":"Gao S. Simulation and analysis of dance teaching action based on multimedia technology. 2022 3rd ISC Int Conf Intell Syst Control Biomedical Eng (ISC-BE 2022). 2022. https:\/\/doi.org\/10.1051\/matecconf\/202236501056.","journal-title":"2022 3rd ISC Int Conf Intell Syst Control Biomedical Eng (ISC-BE 2022)"},{"key":"1172_CR8","doi-asserted-by":"publisher","first-page":"e2342","DOI":"10.7717\/peerj-cs.2342","volume":"10","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Wang W. Enhancing dance education through convolutional neural networks and blended learning. PeerJ Comput Sci. 2024;10:e2342. https:\/\/doi.org\/10.7717\/peerj-cs.2342.","journal-title":"PeerJ Comput Sci"},{"key":"1172_CR9","doi-asserted-by":"publisher","unstructured":"Trajkova M, Long D, Deshpande M, Knowlton A, Magerko B. (2024). Exploring collaborative movement improvisation towards the design of LuminAI\u2014a co-creative AI dance partner. In Proceedings of the CHI conference on human factors in computing systems (CHI \u201924) (pp. 1\u201322). ACM. https:\/\/doi.org\/10.1145\/3613904.3642677","DOI":"10.1145\/3613904.3642677"},{"issue":"1","key":"1172_CR10","doi-asserted-by":"publisher","first-page":"16856","DOI":"10.1038\/s41598-025-01879-2","volume":"15","author":"N Zhen","year":"2025","unstructured":"Zhen N, Keun PJ. Ethnic dance movement instruction guided by artificial intelligence and 3D convolutional neural networks. Sci Rep. 2025;15(1):16856. https:\/\/doi.org\/10.1038\/s41598-025-01879-2. PMID: 40374887; PMCID: PMC12081739.","journal-title":"Sci Rep"},{"key":"1172_CR11","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1038\/s41598-025-85407-2","volume":"15","author":"M Li","year":"2025","unstructured":"Li M. The analysis of dance teaching system in deep residual network fusing gated recurrent unit based on artificial intelligence. Sci Rep. 2025;15:1305. https:\/\/doi.org\/10.1038\/s41598-025-85407-2.","journal-title":"Sci Rep"},{"key":"1172_CR12","doi-asserted-by":"publisher","first-page":"12111","DOI":"10.1007\/s10639-023-11649-0","volume":"28","author":"J Kang","year":"2023","unstructured":"Kang J, Kang C, Yoon J, et al. Dancing on the inside: A qualitative study on online dance learning with teacher-AI cooperation. Educ Inf Technol. 2023;28:12111\u201341. https:\/\/doi.org\/10.1007\/s10639-023-11649-0.","journal-title":"Educ Inf Technol"},{"issue":"1","key":"1172_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJITSA.386846","volume":"18","author":"S Yang","year":"2025","unstructured":"Yang S, Li X, Sun Y, Zhang AY. Development of standardized training model and system for dance moves based on intelligent teaching. Int J Inform Technol Syst Approach (IJITSA). 2025;18(1):1\u201317. https:\/\/doi.org\/10.4018\/IJITSA.386846.","journal-title":"Int J Inform Technol Syst Approach (IJITSA)"},{"key":"1172_CR14","doi-asserted-by":"publisher","unstructured":"Gao Y. (2025, April 3). Path of artificial intelligence technology in college dance education. In Proceedings of the 2024 3rd international conference on educational science and social culture (ESSC 2024) (Advances in Social Science, Education and Humanities Research 914, pp. 324\u2013337). Atlantis Press. https:\/\/doi.org\/10.2991\/978-2-38476-384-9_38","DOI":"10.2991\/978-2-38476-384-9_38"},{"key":"1172_CR15","doi-asserted-by":"publisher","unstructured":"Yan L. (2024). Research on artificial intelligence-assisted teaching methods in Chinese classical dance movement training. Applied Mathematics and Nonlinear Sciences. 2024;9:(1). https:\/\/doi.org\/10.2478\/amns-2024-3173","DOI":"10.2478\/amns-2024-3173"},{"key":"1172_CR16","doi-asserted-by":"publisher","unstructured":"Zhou L. (2023). Research on the application of dance movement skill analysis in teaching in the context of artificial intelligence in universities. 2023, Applied Mathematics and Nonlinear Sciences, \u2116 1. https:\/\/doi.org\/10.2478\/amns.2023.1.00482","DOI":"10.2478\/amns.2023.1.00482"},{"key":"1172_CR17","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.aej.2025.02.014","volume":"121","author":"H Zhao","year":"2025","unstructured":"Zhao H, Du B, Jia Y, Zhao H. DanceFormer: Hybrid transformer model for real-time dance pose estimation and feedback. Alexandria Eng J. 2025;121:66\u201376. https:\/\/doi.org\/10.1016\/j.aej.2025.02.014.","journal-title":"Alexandria Eng J"},{"key":"1172_CR18","doi-asserted-by":"publisher","unstructured":"Xu L-J, Wu J, Zhu J-D, Chen L. Effects of AI-assisted dance skills teaching, evaluation and visual feedback on dance students\u2019 learning performance, motivation and self-efficacy. Int J Hum Comput Stud Article. 2024;103410. https:\/\/doi.org\/10.1016\/j.ijhcs.2024.103410.","DOI":"10.1016\/j.ijhcs.2024.103410"},{"key":"1172_CR19","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.aej.2024.11.025","volume":"114","author":"W Qin","year":"2025","unstructured":"Qin W, Meng J. The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks. Alexandria Eng J. 2025;114:46\u201354. https:\/\/doi.org\/10.1016\/j.aej.2024.11.025.","journal-title":"Alexandria Eng J"},{"issue":"9","key":"1172_CR20","doi-asserted-by":"publisher","first-page":"28","DOI":"10.32629\/rerr.v6i9.2622","volume":"6","author":"T Qi","year":"2024","unstructured":"Qi T, Xu B. Research on the status and development pathways of artificial intelligence technology in dance education. Region - Educational Res Reviews. 2024;6(9):28. https:\/\/doi.org\/10.32629\/rerr.v6i9.2622.","journal-title":"Region - Educational Res Reviews"},{"issue":"1","key":"1172_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10055-021-00529-y","volume":"26","author":"J Iqbal","year":"2022","unstructured":"Iqbal J, Sidhu MS. Acceptance of dance training system based on augmented reality and technology acceptance model (TAM). Virtual Reality. 2022;26(1):33\u201354. https:\/\/doi.org\/10.1007\/s10055-021-00529-y.","journal-title":"Virtual Reality"},{"issue":"4","key":"1172_CR22","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1177\/1089313X231185054","volume":"27","author":"V Volkova","year":"2023","unstructured":"Volkova V, Ferber R, Pasanen K, Kenny S. Perceptions and attitudes toward the use of wearable technology in the dance studio environment. J Dance Med Sci. 2023;27(4):241\u201352. https:\/\/doi.org\/10.1177\/1089313X231185054.","journal-title":"J Dance Med Sci"},{"key":"1172_CR23","doi-asserted-by":"publisher","first-page":"101055","DOI":"10.1016\/j.measen.2024.101055","volume":"32","author":"L Qianwen","year":"2024","unstructured":"Qianwen L. Application of motion capture technology based on wearable motion sensor devices in dance body motion recognition. Measurement: Sens. 2024;32:101055. https:\/\/doi.org\/10.1016\/j.measen.2024.101055.","journal-title":"Measurement: Sens"},{"issue":"4","key":"1172_CR24","doi-asserted-by":"publisher","first-page":"2540492","DOI":"10.1142\/S0129156425404929","volume":"34","author":"Y Li","year":"2025","unstructured":"Li Y. Research on data collection and posture optimization in dance training using smart wearable devices. Int J High-Speed Electron Syst. 2025;34(4):2540492. https:\/\/doi.org\/10.1142\/S0129156425404929.","journal-title":"Int J High-Speed Electron Syst"},{"key":"1172_CR25","doi-asserted-by":"publisher","unstructured":"Otterbein R, Jochum E, Overholt D, Bai S, Dalsgaard A. Dance and movement-led research for designing and evaluating wearable human-computer interfaces. In Proceedings 8th international conference move computing (MOCO \u201822). 2022;Article 9:1\u20139. https:\/\/doi.org\/10.1145\/3537972.3537984.","DOI":"10.1145\/3537972.3537984"},{"key":"1172_CR26","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Bahdanau D, Bengio Y. (2014). On the properties of neural machine translation: encoder\u2013decoder approaches. In Proceedings of SSST-8, 8th workshop on syntax, semantics and structure in statistical translation, 103\u2013111.","DOI":"10.3115\/v1\/W14-4012"},{"issue":"1","key":"1172_CR27","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K. 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 2013;35(1):221\u201331. https:\/\/doi.org\/10.1109\/TPAMI.2012.59.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1172_CR28","doi-asserted-by":"publisher","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings 54th annual meeting association comput linguistics (ACL). 2016;207:212. https:\/\/doi.org\/10.18653\/v1\/P16-2034.","DOI":"10.18653\/v1\/P16-2034"},{"key":"1172_CR29","doi-asserted-by":"publisher","unstructured":"Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, Tassa Y, Silver D, Wierstra D. (2016). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971. https:\/\/doi.org\/10.48550\/arXiv.1509.02971","DOI":"10.48550\/arXiv.1509.02971"},{"key":"1172_CR30","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"J Kennedy","year":"1995","unstructured":"Kennedy J, Eberhart R. Particle swarm optimization. Proc IEEE Int Conf Neural Networks. 1995;4:1942\u20138. https:\/\/doi.org\/10.1109\/ICNN.1995.488968.","journal-title":"Proc IEEE Int Conf Neural Networks"},{"key":"1172_CR31","unstructured":"Mnih V, Badia AP, Mirza M, Graves A, Harley T, Lillicrap TP, Silver D, Kavukcuoglu K. (2016). Asynchronous methods for deep reinforcement learning. In Proceedings of the 33rd international conference on machine learning (ICML), 1928\u20131937. Available: https:\/\/proceedings.mlr.press\/v48\/mniha16.html"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-01172-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01172-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01172-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:41:21Z","timestamp":1778150481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-01172-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,29]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1172"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-01172-9","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,29]]},"assertion":[{"value":"24 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trail"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"407"}}