Recognition and Explainable Quantitative Evaluation of Fundamental Taekwondo Kicks from Smartphone Videos

Accessible and quantitative assessment of rapid martial arts movements remains difficult without specialized equipment or continuous expert observation. Here, we present a desktop prototype that uses smartphone-recorded videos to recognize and evaluate three fundamental taekwondo kicks: front, roundhouse and axe kicks. The TKD-Kick3 protocol planned 790 recordings from 150 university students across five physical education classes and retained 765 cleaned pose sequences; because two first-year classes had not yet learned the roundhouse kick, front and axe kick recordings were planned for all students, whereas roundhouse kick recordings were planned only for 95 second-year students. MediaPipe Pose extracted 13 body keypoints, which were encoded as 52-dimensional frame features combining normalized two-dimensional positions and first-order velocities. Because participant identifiers were unavailable, recognition was assessed using a file-level validation split; recordings from the same participant may therefore have crossed subsets, potentially inflating performance estimates. Across three random seeds, the selected BiLSTM with uniform resampling and argmax inference achieved 75.3 ± 2.3% accuracy, 74.3 ± 2.3% macro-F1 and 74.5 ± 2.3% balanced accuracy; within the same validation setting, the best Transformer configuration achieved 67.3 ± 1.7% accuracy. On 30 expert-annotated videos, system scores showed moderate association with expert ratings (Spearman's rho = 0.627; mean absolute error = 0.601 on a 10-point scale), providing preliminary support for the interpretable scoring approach. These results provide proof-of-concept evidence for smartphone-based kick assessment but do not establish participant-independent generalization or expert-equivalent scoring, motivating future evaluation with participant-level metadata and independent test cohorts.