ISSN: 2226-6348
Open access
With the modernization of sports, the application of digital technology in university physical education has gained increasing attention. This study explores the effectiveness of OpenPose skeleton tracking technology in sports dance instruction using a quantitative approach and an eight-week experimental intervention to evaluate students' movement accuracy, learning engagement, and skill improvement. The results indicate that intelligent-assisted teaching is widely recognized, with 95% of students reporting enhanced learning outcomes and interactivity. However, some teachers remain skeptical, highlighting the need for additional technical training. The experimental group demonstrated significantly higher motor skills (T = 7.435, P = 0.000) and overall scores (T = 2.64, P = 0.01) than the control group, confirming the effectiveness of the intervention. Performance improvements were notable in the first five weeks (P < 0.05) but plateaued after the sixth week (P > 0.05). OpenPose received a 72% satisfaction rate, significantly improving movement accuracy (92%), skill acquisition (92%), and motion correction (88%), though its impact on movement fluidity (56%) and injury prevention (80%) requires further optimization. In conclusion, OpenPose technology effectively enhances the quality of sports dance instruction, stimulates student interest in learning, and demonstrates the potential of intelligent technology in this field.
Athow, S., & McGough, J. (2021). Using computer vision to assist the scoring of modern fencing. Proceedings of the 49th Annual Midwest Instruction and Computing Symposium (MICS).
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 7291–7299.
Chen, X. (2018). Motion evaluation system based on human 3D posture [Doctoral dissertation, Zhejiang University]. Zhejiang University Repository.
Hara, K., & Chellappa, R. (2017). Growing regression tree forests by classification for continuous object pose estimation. International Journal of Computer Vision, 122(2), 1–21.
He, D., & Li, L. (2020). A new Kinect-based posture recognition method in physical sports training based on urban data. Wireless Communications and Mobile Computing, 2020, 1–10.
Li, D., & Sun, Y. (2024). Improvement of students’ sports performance in VR-based smart sports teaching in colleges and universities. Applied Mathematics and Nonlinear Sciences, 9(1), 1–13.
Li, P., Zhang, H., Zhang, D., Jiang, G., He, J., & Liu, H. (2021). Using wearable electronic devices to quantify the physical and physiological response of collegiate students participating in leisure-time team sports. 2021 International Conference on Information Technology and Contemporary Sports (TCS), 1–5.
Li, R. (2020). Research on fine classification and evaluation methods of human motion based on visual data [Doctoral dissertation, University of Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences].
Li, S., Song, K., Ouyang, B., Zhang, B., Liu, C., Liu, P., & Liu, R. (2022). Intelligent evaluation system for standing long jump movements based on human posture recognition. Artificial Intelligence, 2022(02), 75–87.
Mu, J. (2022). Pose estimation-assisted dance tracking system based on convolutional neural network. Computational Intelligence and Neuroscience, 2022, 1–10.
Nogueira, M. R., Menezes, P., & de Carvalho, J. M. (2024). Exploring the impact of machine learning on dance performance: A systematic review. International Journal of Performance Arts and Digital Media, 20(1), 60–109.
Wang, Y. (2020). Human motion posture estimation and recognition based on deep neural networks [Doctoral dissertation, University of Electronic Science and Technology of China].
Yang, J., Zhang, S., Zhang, C., & Huang, J. (2021). Comparative study on human motion recognition based on OpenPose. Sensors and Microsystems, 40(1), 11–14.
Zhang, Z. (2012). Microsoft Kinect sensor and its effect. IEEE Multimedia, 19(2), 4–10.
Zhou, J. (2021). Virtual reality sports auxiliary training system based on embedded system and computer technology. Microprocessors and Microsystems, 82, 103944.
Zhou, X., & Huang, J. (2024). Motion capture sensor-based pose estimation for dance canonical movements. Applied Mathematics and Nonlinear Sciences, 9(1), 1–14.
Ge, Y., Abdullah, B., & Saad, H. A. (2025). A Study on the Digital Application of OpenPose Skeleton Tracking Technology in Sports Dance Teaching in Chinese Universities from a Modernization Perspective. International Journal of Academic Research in Progressive Education and Development, 14(1), 1685-1698.
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