Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory
Abstract
Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports.
Keywords
Full Text:
PDFReferences
Ahmad, R., Iqbal, A., Mohsin Jadoon, M., Ahmad, N., & Javed, Y. (2024). XEmoAccent: Embracing Diversity in Cross-Accent Emotion Recognition Using Deep Learning. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3376379
Anand Thoutam, V., Srivastava, A., Badal, T., Kumar Mishra, V., Sinha, G. R., Sakalle, A., Bhardwaj, H., & Raj, M. (2022). Yoga Pose Estimation and Feedback Generation Using Deep Learning. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4311350
Cossich, V. R. A., Carlgren, D., Holash, R. J., & Katz, L. (2023). Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis. Applied Sciences, 13(23). https://doi.org/10.3390/app132312965
Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2023). Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review. International Journal of Environmental Research and Public Health, 20(1). https://doi.org/10.3390/ijerph20010173
El-Sayed, R. S. (2023). A Hybrid CNN-LSTM Deep Learning Model for Classification of the Parkinson Disease. IAENG International Journal of Applied Mathematics, 53(4).
Mohammady, Z., & Safari, L. (2022). WITHDRAWN: A semi-supervised method to generate Persian dataset for suggestion classification. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2022.100296
Moreira da Silva, F., Malico Sousa, P., Pinheiro, V. B., López-Torres, O.,
Refoyo Roman, I., & Mon-López, D. (2021). Which are the most determinant psychological factors in olympic shooting performance? A self-perspective from elite shooters. International Journal of Environmental Research and Public Health, 18(9). https://doi.org/10.3390/ijerph18094637
Nguyen, D. K., Lan, C. H., & Chan, C. L. (2021). Deep ensemble learning approaches in healthcare to enhance the prediction and diagnosing performance: the workflows, deployments, and surveys on the statistical, image-based, and sequential datasets. International Journal of Environmental Research and Public Health, 18(20). https://doi.org/10.3390/ijerph182010811
Palermi, S., Vecchiato, M., Saglietto, A., Niederseer, D., Oxborough, D., Ortega-Martorell, S., Olier, I., Castelletti, S., Baggish, A.,
Maffessanti, F., Biffi, A., Andrea, A. D., Zorzi, A., Cavarretta, E., & Ascenzi, F. D. (2024). Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete’s heart? In European Journal of Preventive Cardiology (Vol. 31, Issue 4). https://doi.org/10.1093/eurjpc/zwae008
Rahmadani, A., Dewantara, B. S. B., & Sari, D. M. (2022). Human Pose Estimation for Fitness Exercise Movement Correction. IES 2022 - 2022 International Electronics Symposium: Energy Development for Climate Change Solution and Clean Energy Transition, Proceeding. https://doi.org/10.1109/IES55876.2022.9888451
Rizki, A. B., & Zuliarso, E. (2022). Klasifikasi Teknik Bulutangkis Berdasarkan Pose Dengan Convulutional Neural Network. Jurnal Ilmiah Informatika, 10(02). https://doi.org/10.33884/jif.v10i02.5559
Safari, L., & Mohammady, Z. (2024). A semi-supervised method to generate a persian dataset for suggestion classification. Language Resources and Evaluation, 58(2). https://doi.org/10.1007/s10579-023-09688-7
Siddesh Padala, V., Gandhi, K., & Pushpalatha, D. V. (2019). Machine learning: The new language for applications. IAES International Journal of Artificial Intelligence, 8(4). https://doi.org/10.11591/ijai.v8.i4.pp411-421
Sowjanya, K., Poojitha, G., Saran, Ch. K., Priyanka, B., & Ahalya, D. (2023). Pulmonary Tuberculosis Detection from Chest X-Ray Images Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 11(4). https://doi.org/10.22214/ijraset.2023.50382
Vardar, T., & Senduran, F. (2021). The cognitive workload of air pistol shooters on the aiming task. Pakistan Journal of Medical and Health Sciences, 15(9). https://doi.org/10.53350/pjmhs211592610
Verma, S., Bhattacharya, S., Chowdhury, N. U. M. K., & Tian, M. (2021). A new workflow for multi-well lithofacies interpretation integrating joint petrophysical inversion, unsupervised and supervised machine learning. SEG Technical Program Expanded Abstracts, 2021-September. https://doi.org/10.1190/segam2021-3584118.1
Wen, L., Qiao, Z., & Mo, J. (2024). Modern technology, artificial intelligence, machine learning and internet of things based revolution in sports by employing graph theory matrix approach. AIMS Mathematics, 9(1). https://doi.org/10.3934/math.2024060
Yu, Z. (2024). Analysis of the prospective application of artificial intelligence in swimming. Applied and Computational Engineering, 37(1). https://doi.org/10.54254/2755-2721/37/20230472
Zhang, W. (2022). Artificial Intelligence-Based Soccer Sports Training Function Extraction: Application of Improved Genetic Algorithm to Soccer Training Path Planning. In Journal of Sensors (Vol. 2022). https://doi.org/10.1155/2022/8375916
Zhao, Y., Wang, X., Li, J., Li, W., Sun, Z., Jiang, M., Zhang, W., Wang, Z., Chen, M., & Li, W. J. (2023). Using IoT Smart Basketball and Wristband Motion Data to Quantitatively Evaluate Action Indicators for Basketball Shooting. Advanced Intelligent Systems, 5(12). https://doi.org/10.1002/aisy.202300239
DOI: https://doi.org/10.31294/inf.v12i2.25566
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Bahar Sobari, Moedjiono Moedjiono, M. Asep Rizkiawan

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Index by:
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
Published LPPM Universitas Bina Sarana Informatika with supported by Relawan Jurnal Indonesia
Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Jakarta Pusat, DKI Jakarta 10450, Indonesia

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License