Deep Learning Sequence Network for Identifying and Analyzing Archery Shooting Patterns
By: Jihoon Park and Hyongjun Choi
References
- Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework (No. arXiv:1907.10902). arXiv.
https://doi.org/10.48550/arXiv.1907.10902 - Brian, H. (2009). A kinematic analysis of finger motion in archery. ISBS-Conference Proceedings Archive.
- Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., … Lin, D. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark (No. arXiv:1906.07155). arXiv.
https://doi.org/10.48550/arXiv.1906.07155 - Chung, J.-L., Ong, L.-Y., & Leow, M.-C. (2022). Comparative analysis of skeleton-based human pose estimation. Future Internet, 14(12), 380.
https://doi.org/10.3390/fi14120380 - Cronin, N. J., Walker, J., Tucker, C. B., Nicholson, G., Cooke, M., Merlino, S., & Bissas, A. (2024). Feasibility of OpenPose markerless motion analysis in a real athletics competition. Frontiers in Sports and Active Living, 5, 1298003.
https://doi.org/10.3389/fspor.2023.1298003 - Deepa, B., & Ramesh, K. (2022). Epileptic seizure detection using deep learning through min max scaler normalization. International Journal of Health Sciences, 10981–10996.
https://doi.org/10.53730/ijhs.v6nS1.7801 - dos Santos Banks, L., Santiago, P. R. P., da Silva Torres, R., de Oliveira, D. C. X., & Moura, F. A. (2024). Accuracy of a markerless system to estimate the position of taekwondo athletes in an official combat area. International Journal of Performance Analysis in Sport, 24(5), 479–494.
https://doi.org/10.1080/24748668.2024.2321738 - Gavilanes, J. O. (2023). Improving performance with OpenPose: Analyzing post-run feedback through a prescriptive dashboard (Bachelor's thesis). University of Twente. Available at
https://essay.utwente.nl/97765/ - Ghadekar, P., Bhagat, H., More, P., More, V., Saraf, C., & Khare, S. (2024). Performance analysis for diving sport using YoLoV8, OpenPose and fuzzy logic. 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), 1–8.
https://doi.org/10.1109/ic-ETITE58242.2024.10493217 - Hemara, C., Ketkan, P., Singchainara, J., & Santiboon, T. T. (2021). Efficient archery posture training analysis of archery performances for the talent development and excellence achievements. International Journal of Human Movement and Sports Sciences, 9(6), 1435–1446.
https://doi.org/10.13189/saj.2021.090640 - Ino, T., Samukawa, M., Ishida, T., Wada, N., Koshino, Y., Kasahara, S., & Tohyama, H. (2024). Validity and reliability of OpenPose-based motion analysis in measuring knee valgus during drop vertical jump test. Journal of Sports Science and Medicine, 23(1), 515–525.
https://doi.org/10.52082/jssm.2024.515 - Ji, X., Al Tamimi, Z., Gao, X., & Piovesan, D. (2025). The impact of draw weight on archers posture and injury risk through motion capture analysis. Applied Sciences, 15(2), 879.
https://doi.org/10.3390/app15020879 - Ji, X., Miller, J., Gao, X., Al Tamimi, Z., Arzalluz, I., & Piovesan, D. (2024). An ergonomics analysis of archers through motion tracking to prevent injuries and improve performance. Sensors, 24(6), 1862.
https://doi.org/10.3390/s24061862 - Kitamura, T., Teshima, H., Thomas, D., & Kawasaki, H. (2022). Refining OpenPose with a new sports dataset for robust 2D pose estimation. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 672–681.
https://doi.org/10.1109/WACVW54805.2022.00074 - Lee, S., Moon, J.-Y., Kim, J., & Lee, E. C. (2024). AI-based analysis of archery shooting time from anchoring to release using pose estimation and computer vision. Applied Sciences, 14(24), 11838.
https://doi.org/10.3390/app142411838 - Liu, L., Dai, Y., & Liu, Z. (2024). Real-time pose estimation and motion tracking for motion performance using deep learning models. Journal of Intelligent Systems, 33(1), 20230288.
https://doi.org/10.1515/jisys-2023-0288 - Lu, P., Jiang, T., Li, Y., Li, X., Chen, K., & Yang, W. (2024). RTMO: Towards high-performance one-stage real-time multi-person pose estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1491–1500.
https://doi.org/10.1109/CVPR52733.2024.00148 - Park, J. (2023). The methodology of the golf swing similarity measurement using deep learning-based 2D pose estimation. Journal of the Korea Society of Computer and Information, 28(1), 39–47.
https://doi.org/10.9708/JKSCI.2023.28.01.039 - Pham, H. H., Khoudour, L., Crouzil, A., Zegers, P., & Velastin, S. A. (2022). Video-based Human Action Recognition using Deep Learning: A Review (No. arXiv:2208.03775). arXiv.
https://doi.org/10.48550/arXiv.2208.03775 - Purnama, B., Erfianto, B., & Wirawan, I. R. (2024). Time series classification of badminton pose using LSTM with landmark tracking. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 7(1).
https://doi.org/10.35882/jeeemi.v7i1.488 - Shah, A., Mishra, S., Bansal, A., Chen, J.-C., Chellappa, R., & Shrivastava, A. (2022). Pose and joint-aware action recognition. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 141–151.
https://doi.org/10.1109/WACV51458.2022.00022 - Vendrame, E., Belluscio, V., Truppa, L., Rum, L., Lazich, A., Bergamini, E., & Mannini, A. (2024). Performance assessment in archery: a systematic review. Sports Biomechanics, 23(12), 2444–2466.
https://doi.org/10.1080/14763141.2022.2049357
DOI: https://doi.org/10.2478/ijcss-2025-0016 | Journal eISSN: 1684-4769
Language: English
Page range: 131 - 142
Published on: May 3, 2026
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
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© 2026 Jihoon Park, Hyongjun Choi, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.