Effects of foot rotation on ankle control and injury risk during badminton lunge landings
Bibliographic record
Abstract
BACKGROUND: The badminton lunge is a decisive yet injury-prone action that places substantial loads on the ankle, and toe-in, neutral, or toe-out orientations may alter joint mechanics and influence injury risk; however, evidence in sport-specific lunge contexts is scarce. This study quantified the biomechanical effects of different toe-orientations in badminton lunge landings using integrated motion capture, force measurement, electromyography, and finite element modeling. By uniquely combining EMG and FEA, the study provides evidence-based guidance for performance optimization and injury prevention. METHODS: Thirty male amateur badminton players performed right-lunge landings under three toe-orientations. Kinematic and kinetic data were collected using motion capture and force platforms. Muscle activation was assessed through surface electromyography and musculoskeletal modeling. A three-dimensional finite element model of the foot simulated Von Mises stress distribution. Statistical analyses included repeated measures ANOVA and statistical parametric mapping. RESULTS: Toe-out landings significantly reduced ankle eversion angles, eversion moments, anterior-posterior shear forces, and joint impulse (p < 0.001, p = 0.025, p = 0.047, p = 0.038). They also promoted smoother power output and greater anteroposterior center-of-mass displacement. Stress shifted from the rearfoot to the forefoot, accompanied by increased activation of the peroneal muscles and triceps surae. In contrast, toe-in landings produced higher eversion loading, stress concentration in the rearfoot, greater tibialis anterior activation, and restricted center-of-mass displacement. CONCLUSION: Toe-out positioning lowered ankle load and rearfoot stress, improved stabilizing muscle activation and shock absorption, and facilitated center-of-mass control. Toe-in positioning elevated stress concentration and injury risk. Moderate toe-out angles were recommended to optimize footwork efficiency and protect joint health.
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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".