EnjoyPoomsae: A Pose-Based Taekwondo Poomsae Forms Dataset

This dataset, EnjoyPoomsae, contains pose-based skeletal time-series data extracted from Taekwondo Poomsae form executions (Forms 1–4). The source videos were collected from publicly available online resources and include unconstrained real-world recording conditions such as camera motion, multiple persons in the scene, and varying viewpoints. To ensure consistent subject selection, a multi-stage processing pipeline was applied including human detection, multi-object tracking, and pose estimation. Only one tracked subject per clip was retained. For each video clip, 2D body keypoints were extracted and converted into temporal sequences. To improve invariance and model robustness, two geometric normalization strategies were applied: Translation normalization: the nose joint is shifted to the origin (0,0) Scale normalization: the torso length (shoulder-to-hip distance) is scaled to one Four dataset variants are provided: no normalization translation normalization only scale normalization only combined translation and scale normalization To reduce sequence length and memory requirements while preserving motion structure, temporal frame subsampling with stride=4 was applied during preprocessing. Each sample is stored as a NumPy NPZ file containing: X: pose keypoint sequence array (T × D) y: class label (form ID) The dataset contains 540 samples with balanced class distribution. Each class is performed by different individuals to improve subject independence. The full preprocessing, feature extraction, and training pipelines are publicly available in the associated GitHub repository (see Related Works section).