Effect of Ski Binding Parameters on Knee Biomechanics: A Three-Dimensional Computational Study
Bibliographic record
Abstract
INTRODUCTION: Downhill skiing is a relatively safe sport, but many potentially avoidable injuries do occur. Whereas tibia and ankle injuries have been declining, severe knee sprains usually involving the anterior cruciate ligament (ACL) have increased from the 1970s to the 1990s. The goal of the present study was to evaluate the effect of the position of the binding pivot point and binding release characteristics on ACL strain during a phantom-foot fall. METHODS: We computed ACL strain using a biomechanical computer knee model to simulate the phantom-foot ACL-injury mechanism. This mechanism, which is one of the most common mechanisms of ACL injury in downhill skiing, occurs when the weight of the skier is on the inner edge of the ski during a backward fall, resulting in a sharp uncontrolled inward turn of the ski. RESULTS: The model predicts, that under simulated phantom-foot conditions, a binding with fast-release characteristics with a pivot positioned in front of the center of the boot produces less strain on the ACL. Current bindings have their pivot point approximately at the center of the heel radius. A pivot positioned at the back of the binding is more effective for sensing loads that occur at the tip of the ski. However, it is less effective for sensing loads that occur at the tail of the ski and, therefore, offers less protection during a phantom-foot fall. CONCLUSION: A binding with two pivot points, one positioned in front and the other at the back, could sense twist loads applied to the ski both at the front and at the back, and might, therefore, be a solution to reduce the occurrence of ACL injuries.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".