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ISSN : 2092-8475(Print)
ISSN : 2714-0148(Online)
Journal of International Academy of Physical Therapy Research Vol.16 No.1 pp.3401-3409
DOI : https://doi.org/10.20540/JIAPTR.2025.16.1.3401

Towards Automated Taekwondo Poomsae Scoring: Evaluating Modern Neural Network Techniques

Taeseok Choia, Seoyoon Heob
aDepartment of Physical Therapy, Gunjang University, Gunsan, Republic of Korea;
bDepartment of Occupational Therapy, Kyungbok University, Namyangju, Republic of Korea

Abstract

Background: The growing need for objective and accurate evaluation in Taekwondo poomsae competitions has highlighted the limitations of subjective human judgment. Objectives: This study aims to develop an automated scoring framework using camera-based pose estimation and advanced neural networks to improve the consistency and accuracy of poomsae evaluation. Design: Comparative analysis of neural network architectures on a large-scale dataset of poomsae movements. Methods: A dataset of 902,306 labeled frames, captured from 48 participants performing 62 distinct movements using synchronized multi-view cameras, was analyzed. Five neural networks (HNN, 1D CNN, GCN, MLP, SANN) were implemented and evaluated using accuracy, precision, recall, and F1-score. Results: The HNN demonstrated superior performance with an F1-score of 0.78 in classifying Taekwondo poomsae postures. The 1D CNN followed with an F1-score of 0.76, while GCN, MLP, and SANN achieved F1-scores of 0.74, 0.70, and 0.66, respectively. The HNN's hierarchical feature extraction approach proved effective in capturing the complex spatial and temporal patterns inherent in poomsae movements. Conclusion: Hierarchical Neural Networks outperform other architectures in poomsae classification, establishing a foundation for objective and scalable scoring systems in competitive settings.

초록

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