Deep Learning-Based Autoencoder for Objective Assessment of Taekwondo Poomsae Movements

Mohamed Chaâbane, Imen Ben Said, Imen Ben Said, Adel Chaari

2025

Abstract

Artificial Intelligence (AI) is revolutionizing sports by enhancing performance, improving safety, and creating richer fan experiences. This paper focuses on leveraging AI in Taekwondo, specifically in assessing athlete performance during Poomsae movements, which are foundational to the sport and crucial for success in competitions. Traditionally, the evaluation of Poomsae has been subjective and heavily reliant on human judgment. This study addresses this issue by automating the assessment process. We propose a deep learning approach that utilizes computer vision to analyse athletes' movements captured in video clips of Poomsae. The proposed approach is based on a model that emphasizes the use of autoencoders for training data representing skeleton body points of correct movements. This model can effectively identify anomalies, i.e., incorrect movements by athletes. The SportLand platform implements the proposed approach, providing coaches and athletes with precise and actionable insights into their performance. This platform can serve as an assistant for self-evaluation, allowing Taekwondo athletes to enhance their skills at their own pace.

Paper Citation

in Harvard Style

Chaâbane M., Ben Said I. and Chaari A. (2025). Deep Learning-Based Autoencoder for Objective Assessment of Taekwondo Poomsae Movements. In Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS; ISBN 978-989-758-771-9, SciTePress, pages 170-177. DOI: 10.5220/0013706100003988

in Bibtex Style

@conference{icsports25,
author={Mohamed Chaâbane and Imen Ben Said and Adel Chaari},
title={Deep Learning-Based Autoencoder for Objective Assessment of Taekwondo Poomsae Movements},
booktitle={Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS},
year={2025},
pages={170-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013706100003988},
isbn={978-989-758-771-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS
TI - Deep Learning-Based Autoencoder for Objective Assessment of Taekwondo Poomsae Movements
SN - 978-989-758-771-9
AU - Chaâbane M.
AU - Ben Said I.
AU - Chaari A.
PY - 2025
SP - 170
EP - 177
DO - 10.5220/0013706100003988
PB - SciTePress