Finite element analysis of stress distribution in knee joint structures during soccer instep kick
Bibliographic record
Abstract
This observational study investigates the stress distribution in knee joint structures, specifically focusing on the meniscus and primary ligaments of the supporting leg during the support phase of a maximal-effort soccer instep kick executed by an elite-level player. Understanding these stress patterns is crucial for injury prevention and enhancing performance in high-impact soccer actions. A three-dimensional (3D) knee model was developed utilizing CT and MRI data. Finite element (FE) analysis was conducted to evaluate stress distribution patterns across the lateral and medial menisci, medial collateral ligament (MCL), and anterior cruciate ligament (ACL). The analysis revealed peak von Mises stresses of 16.127 MPa in the lateral meniscus, 10.845 MPa in the medial meniscus, 36.613 MPa in the MCL, and 22.863 MPa in the ACL. These findings indicate significant stress concentrations in the lateral meniscus, proximal MCL, and femoral insertion of the ACL. The identified stress distribution patterns specifically related to the knee joint during the instep kicking phase provide critical insights into the internal mechanical demands placed on the joint structures. This study enhances the understanding of stress concentrations in the meniscus and ligaments during soccer kicks, emphasizing the potential for targeted analysis of these stress patterns to inform injury prevention strategies. It suggests that a deeper comprehension of the stress distribution mechanics could contribute to more effective training protocols and rehabilitation approaches for athletes, ultimately improving performance and reducing the likelihood of knee injuries during soccer activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".