Kinematic quantification of straight-punch techniques using the preferred and non-preferred fist in taekwon-do
Bibliographic record
Abstract
Summary Study aim : The aim of the current study is to reveal the characteristics of punch techniques applied in taekwon-do. Material and methods : The skill quantification was performed on 10 taekwon-do ITF competitors. During the test, they were asked to perform straight punches using both the preferred and the non-preferred fist into the air (i.e. without a physical target) in the lateral position employing both traditional and sport style. Applying reflective markers on fists, the punching kinematic data were collected in the HML (Human Motion Lab). For data analyses, the average and standard deviation of duration, velocity and acceleration were used. The Mann-Whitney U test was applied to determine possible differences (p < 0.05) between the dominant fist and non-dominant fist as well as between the traditional and sport punch. Results : The results revealed that the sport punch is notably faster (shorter punch duration) with a higher acceleration than the traditional one. There is no significant difference between the preferred and non-preferred fist. The results could suggest that the left and right straight punches during taekwon-do training sessions are equally developed. However, the different goals of the punch techniques, i.e. the traditional punch for generating power and the sport punch for quickness, cause significant differences (p < 0.01) in action time. Conclusion : The results imply that a trade-off strategy may play a role in a match, namely a powerful punch for a possible final win or a quick punch for point collection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".