ArticleSensors (Basel, Switzerland)2020
TUHAD: Taekwondo Unit Technique Human Action Dataset with Key Frame-Based CNN Action Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Revisiting judging reliability in taekwondo freestyle Poomsae: implications for AI-supported evaluation.Frontiers in psychology · 2026Article
- Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.Biomimetics (Basel, Switzerland) · 2025Review
- Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.Sensors (Basel, Switzerland) · 2024Article
- Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets.Sensors (Basel, Switzerland) · 2023Article
- Review
- Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
In taekwondo, poomsae (i.e., form) competitions have no quantitative scoring standards, unlike gyeorugi (i.e., full-contact sparring) in the Olympics. Consequently, there are diverse fairness issues regarding poomsae evaluation, and the demand for quantitative evaluation tools is increasing. Action recognition is a promising approach, but the extreme and rapid actions of taekwondo complicate its application. This study established the Taekwondo Unit technique Human Action Dataset (TUHAD), which consists of multimodal image sequences of poomsae actions. TUHAD contains 1936 action samples of eight unit techniques performed by 10 experts and captured by two camera views. A key frame-based convolutional neural network architecture was developed for taekwondo action recognition, and its accuracy was validated for various input configurations. A correlation analysis of the input configuration and accuracy demonstrated that the proposed model achieved a recognition accuracy of up to 95.833% (lowest accuracy of 74.49%). This study contributes to the research and development of taekwondo action recognition.
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Registered trials
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