BIOMECHANICAL ANALYSIS OF LANDING TECHNIQUES AND ASSOCIATED INJURY RISK IN LONG JUMP
Bibliographic record
Abstract
Long jump is an athletic sport which is high impact by its nature and is characterized by approach run that accelerates with great intensity followed by dynamic take off.Such conditions often expose the lower extremities to increased biomechanical stress during landing.The force exerted on the body during landing can greatly predispose individuals to various types of musculoskeletal injuries, particularly in the region of the femur.This paper highlights an effort to study different landing techniques heel first, flat foot and forefoot in terms of their effect on stress distribution and injury possibility employing a technique that combines inverse dynamics with finite element analysis.Simulations to replicate the terminal phase of the jump were carried out based on a trained male long jumper's anthropometric and performance data.The inverse dynamics model captured joint reaction forces and moments, which were subsequently applied as boundary conditions in the finite element analysis of a three dimensional femur model derived from imaging data.Results indicated that the heel-first landing technique produced peak ground reaction forces of highest magnitude with stress concentrations localized at the medial region and lateral condyle of the femur.The maximum total displacement and equivalent stress recorded were 0.00077 mm and 191.79 MPa respectively.On the contrary, forefoot landings showed better load attenuation characteristics next to reducing stress magnitudes and distributing forces more equally across the joint.These results underscore a major contribution of landing mechanics to injury prevention and therefore may indicate forefoot landings a biomechanical way of femoral stress mitigation.In addition, the study proves that inverse dynamics integration with FEA can depict the internal loading mechanisms during athletic moves effectively, thus providing a handy platform for injury risk evaluation and technique enhancement for long jump sportsmen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".