JDC

Journal of Discovery Core

JDC

Digital Intelligence Press

Hong KongHKhttps://dipscie.com/index.php/JDC/index

3135-7687

Articles

<bold>Research on Automatic Evaluation Algorithm for Student Sports Movement Quality Based on Deep Learning</bold>

Lei

Huicong

miumiu9805@163.com

Zhou

Miao

miumiu9805@163.com

International Communication and General Education School, Guangxi College of Sports Education, Nanning, Guangxi, China

10 07 2026

19 54

Received: 1072026

2026

Huicong Lei, Miao Zhou (Author)

<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>

Movement quality assessment; Deep learning; Adaptive graph convolution; Temporal multi-scale aggregation; Contrastive learning

[1] Hu, Z., Liu, Z., & Su, Y. (2024). AI-driven smart transformation in physical education: Current trends and future research directions. Applied Sciences, 14(22), 10616. https://doi.org/10.3390/app142210616

[2] Sun, R., Liu, Y., Li, H., & Yim, J. (2026). A Study on the Factors Influencing User Experience of AI Pose Recognition Feedback Systems in Ballet-Class Contexts. Applied Sciences, 16(7), 3431. https://doi.org/10.3390/app16073431

[3] Van Maarseveen, M., Leenhouts, J., de Witte, A., Flux, E., Van Doorn, H., & van der Kamp, J. (2025). Enhancing affordance perception in pre-service physical education teachers: effects of content knowledge, motor experience and visual experience programs. Frontiers in Sports and Active Living, 7, 1583448. https://doi.org/10.3389/fspor.2025.1583448

[4] Feng, L., Zhao, Y., Zhao, W., & Tang, J. (2022). A comparative review of graph convolutional networks for human skeleton-based action recognition. Artificial Intelligence Review, 55(5), 4275-4305. https://doi.org/10.1007/s10462-021-10107-y

[5] Liu, Y., Zhang, H., Li, Y., He, K., & Xu, D. (2023). Skeleton-based human action recognition via large-kernel attention graph convolutional network. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2575-2585. https://doi.org/10.1109/tvcg.2023.3247075

[6] Liu, F., Wang, C., Tian, Z., Du, S., & Zeng, W. (2025). Advancing skeleton-based human behavior recognition: multi-stream fusion spatiotemporal graph convolutional networks. Complex & Intelligent Systems, 11(1), 94. https://doi.org/10.1007/s40747-024-01743-2

[7] Zhang, C., Xu, Y., Xu, Z., Huang, J., & Lu, J. (2022). Hybrid handcrafted and learned feature framework for human action recognition: C. Zhang et al. Applied Intelligence, 52(11), 12771-12787. https://doi.org/10.1007/s10489-021-03068-w

[8] Sakaa, B., Elbeltagi, A., Boudibi, S., Chaffaï, H., Islam, A. R. M. T., Kulimushi, L. C., ... & Wong, Y. J. (2022). Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin. Environmental Science and Pollution Research, 29(32), 48491-48508. https://doi.org/10.1007/s11356-022-18644-x

[9] Adugna, T., Xu, W., & Fan, J. (2022). Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 14(3), 574. https://doi.org/10.3390/rs14030574

[10] Pang, Y., Zhang, K., & Li, F. (2025). Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions. Scientific Reports, 15(1), 323. https://doi.org/10.1038/s41598-024-83475-4

[11] Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O. J., Hernández-Sosa, D., & Castrillón-Santana, M. (2023, July). An x3d neural network analysis for runner’s performance assessment in a wild sporting environment. In 2023 18th International Conference on Machine Vision and Applications (MVA) (pp. 1-5). IEEE. https://doi.org/10.23919/mva57639.2023.10215918

[12] Bratta, C. (2024). Exploring Sex Differences in Diving Performance Analysis. https://hdl.handle.net/20.500.14242/190721

[13] Gan, Q. (2025). Sports Motion Analysis: From Competition Videos to Data-Driven Interpretations (Doctoral dissertation, Institut Polytechnique de Paris). https://doi.org/10.70675/5ae10807zc807z4f8dzb4d3z832fd95de4cb

[14] Yang, L., & Li, Y. (2026). Swimming action recognition algorithm based on improved C3D and attention-residual network. International Journal of Information and Communication Technology, 27(20), 40-62. https://doi.org/10.1504/ijict.2026.10076718

[15] Dong, K., Feng, X., & Dong, L. (2025, September). Dynamic Thermal Gesture Recognition Algorithm Based on C3D. In 2025 5th International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) (pp. 126-133). IEEE. https://doi.org/10.1109/aiahpc66801.2025.11290620

[16] Lovanshi, M., & Tiwari, V. (2024). Human skeleton pose and spatio-temporal feature-based activity recognition using ST-GCN. Multimedia Tools and Applications, 83(5), 12705-12730.

https://doi.org/10.1007/s11042-023-16001-9

[17] Filtjens, B., Vanrumste, B., & Slaets, P. (2022). Skeleton-based action segmentation with multi-stage spatial-temporal graph convolutional neural networks. IEEE Transactions on Emerging Topics in Computing, 12(1), 202-212. https://doi.org/10.1109/tetc.2022.3230912

[18] Xu, J., Liu, F., Wang, Q., Zou, R., Wang, Y., Zheng, J., ... & Zeng, W. (2024). Enhancing human behavior recognition with spatiotemporal graph convolutional neural networks and skeleton sequences. EURASIP Journal on Advances in Signal Processing, 2024(1), 60. https://doi.org/10.1186/s13634-024-01156-w

[19] Zhou, C., Huang, Y., & Ling, H. (2022). Uncertainty-driven action quality assessment. arXiv preprint arXiv:2207.14513. https://doi.org/10.48550/arXiv.2207.14513

[20] Lei, Q., Yao, L., Zhang, H., & Du, J. (2025). Skeletal Spatio-Temporal Decoupling Transformer for Long-Duration Action Quality Assessment. Knowledge-Based Systems, 114672. https://doi.org/10.1016/j.knosys.2025.114672

[21] Zhu, S., Chen, J., & Su, Y. (2024). Spatio-temporal articulation & coordination co-attention graph network for human motion prediction. Signal Processing, 223, 109551. https://doi.org/10.1016/j.sigpro.2024.109551

[22] Shao, D., Zhao, Y., Dai, B., & Lin, D. (2020). Finegym: A hierarchical video dataset for fine-grained action understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2616-2625). https://doi.org/10.1109/cvpr42600.2020.00269

[23] Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2022). Efficient acceleration of deep learning inference on resource-constrained edge devices: A review. Proceedings of the IEEE, 111(1), 42-91. https://doi.org/10.1109/jproc.2022.3226481

[24] Sharma, D., & Sarkar, S. (2022). Enabling inference and training of deep learning models for AI applications on IoT edge devices. In Artificial Intelligence-based Internet of Things Systems (pp. 267-283). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-87059-1_10

[25] Shah, A., Bangash, J. I., Khan, A. W., Ahmed, I., Khan, A., Khan, A., & Khan, A. (2022). Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images. Journal of King Saud University-Computer and Information Sciences, 34(3), 505-519. https://doi.org/10.1016/j.jksuci.2020.03.007

[26] Gao, Y., Dang, C., Zhu, J., Xie, Y., Hu, Y., Yan, C., ... & Li, X. (2025). A real-time 3D modeling method for buildings driven by IMU and RGB-D fusion. International Journal of Digital Earth, 18(1), 2506496. https://doi.org/10.1080/17538947.2025.2506496

[27] Lee, H., & Ryu, J. (2025). Toward Efficient Generalization in 3D Human Pose Estimation via a Canonical Domain Approach. IEEE Access. https://doi.org/10.1109/access.2025.3566109

[28] Zhang, J., Tu, Z., Weng, J., Yuan, J., & Du, B. (2024). A modular neural motion retargeting system decoupling skeleton and shape perception. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(10), 6889-6904. https://doi.org/10.1109/tpami.2024.3386777

[29] JunZhang, C., & GuanLi, Y. (2026). A hybrid framework combining adaptive graph learning and global temporal attention for skeleton-based action recognition. Scientific Reports. https://doi.org/10.1038/s41598-026-49915-z

[30] Iorga, A., Jianu, A., Gheorghiu, M., Crețu, B. D., & Eremia, I. A. (2023). Motor coordination and its importance in practicing performance movement. Sustainability, 15(7), 5812. https://doi.org/10.3390/su15075812

[31] Kilic, U., Karadag, O. O., & Ozyer, G. T. (2025). AGMS-GCN: Attention-guided multi-scale graph convolutional networks for skeleton-based action recognition. Knowledge-Based Systems, 311, 113045. https://doi.org/10.1016/j.knosys.2025.113045

[32] Singh, S. K., Bharti, B. K., Yadav, A. N., & Dwivedi, A. K. (2025). Optimized Microwave Ablation with a Novel Applicator: Integration of Taguchi Neural Networks for Enhanced Predictive Accuracy of Ablation Zone. IEEE Journal on Multiscale and Multiphysics Computational Techniques. https://doi.org/10.1109/jmmct.2025.3589163

[33] Tekin, M., Yurdal, M. O., Toraman, Ç., Korkmaz, G., & Uysal, İ. (2025). Is AI the future of evaluation in medical education?? AI vs. human evaluation in objective structured clinical examination. BMC medical education, 25(1), 641. https://doi.org/10.1186/s12909-025-07241-4

[34] Hu, K., Shen, C., Wang, T., Xu, K., Xia, Q., Xia, M., & Cai, C. (2024). Overview of temporal action detection based on deep learning. Artificial Intelligence Review, 57(2).

https://doi.org/10.1007/s10462-023-10650-w

[35] Zahan, S., Hassan, G. M., & Mian, A. (2024). Learning sparse temporal video mapping for action quality assessment in floor gymnastics. IEEE Transactions on Instrumentation and Measurement, 73, 1-11. https://doi.org/10.1109/tim.2024.3398072

[36] Wang, Y., Yue, Y., Lu, R., Han, Y., Song, S., & Huang, G. (2024). Efficienttrain++: Generalized curriculum learning for efficient visual backbone training. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 8036-8055. https://doi.org/10.48550/arXiv.2405.08768