Implications of Finite Element Method-based Analysis of Wushu Movement Mechanics for Performance Techniques and Injury Prevention
Bibliographic record
Abstract
In this paper, the 3D reconstruction of the finite element model of the knee joint is completed by first generating and editing the 3D images of the martial arts movements through Mimics software.After that, Hypermesh and Abaqus software are used to pre-and post-process the properties of materials in the knee joint biomechanical finite element model.Visual 3D software and low-pass filter smoothing technique were used to obtain and process the kinematic and kinetic data of the martial arts maneuvers, and the processed data were used as boundary and loading conditions to import the data of the three martial arts maneuvers, namely, horse stance, lunge stance, and servant stance into the finite element model for calculating and comparing the biomechanical responses of the articular cartilage and meniscus.The results showed that the movement pattern of horse stance has a larger knee range of motion and a smaller peak ground reaction force compared to the lunge and servant stance movements in the martial arts maneuvers.Finite element simulations showed that the straddling knee stance produced smaller peak contact stresses on the knee cartilage and meniscus, and the peak stress area changed more during the movement.Three-dimensional finite element simulation analysis obtained four characteristic moments, namely: the first peak ground reaction force moment, the maximum external rotation-external rotation moment, the maximum dorsiflexion moment, and the second peak ground reaction force moment, which corresponded to a greater difference in ground reaction force values.Therefore, it is recommended to wear protective equipment in advance for the injury-prone areas to reduce the risk of injury before the wushu performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".