The influence of landing mat composition on ankle injury risk during a gymnastic landing: a biomechanical quantification.
Bibliographic record
Abstract
PURPOSE: About 70% injury of gymnasts happened during landing - an interaction between gymnast and landing mat. The most injured joint is the ankle. The current study examined the effect of mechanical properties of landing mat on ankle loading with aims to identify means of decreasing the risk of ankle injury. METHOD: Gymnastic skill - salto backward stretched with 3/2 twist was captured by two high-speed camcorders and digitized by using SIMI-Motion software. A subject-specific, 14-segment rigid-body model and a mechanical landing-mat model were built using BRG.LifeMODTM. The landings were simulated with varied landing-mat mechanical properties (i.e., stiffness, dampness and friction coefficients). RESULT: Real landing performance could be accurately reproduced by the model. The simulations revealed that the ankle angle was relatively sensitive to stiffness and dampness of the landing mat, the ankle loading rate increased 26% when the stiffness was increased by 30%, and the changing of dampness had notable effect on horizontal ground reaction force and foot velocity. Further, the peak joint-reaction force and joint torque were more sensitive to friction than to stiffness and dampness of landing mat. Finally, ankle muscles would dissipate about twice energy (189%) when the friction was increased by 30%. CONCLUSION: Loads to ankles during landing would increase as the stiffness and dampness of the landing mat increase. Yet, increasing friction would cause a substantial rise of the ankle internal loads. As such, the friction should be a key factor influencing the risk of injury. Unfortunately, this key factor has rarely attracted attention in practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".