An innovative approach for real time determination of power and reaction time in a martial arts quasi- training environment using 3D motion capture and EMG measurements
Bibliographic record
Abstract
Background Power and neural response are two fundamental elements in many martial arts striking. Currently, there are no practical methods exist to present these aspects to coaches and athletes in a training environment. This study introduced a new method for quantifying the two factors that could be used in real-time biofeedback training. Material/Methods: The new method consisted of self-developed optical signal system, EMG measurement and 3D motion capture. The quantification was done by using kinematics of the punching bag and striking limbs analyzed with self-developed dynamic calculation programs. The setup was very close to a training environment with neglect-able influence on an athlete performance. Results: The results showed that such quantification provided both total power and power components (i.e. linear & angular) of striking as well as the related response processes/time. Conclusions: Since the method could offer feedback of power intensity, attack accuracy, central and peripheral reaction time to coachers and athletes, it would have great potential to become a biofeedback tool in practice for increasing training efficiency and effectiveness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".