Gender Differences in Time-Frequency EMG Analysis of Unanticipated Cutting Maneuvers
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to compare the time-frequency characteristic, using nonlinearly scaled wavelets, of the EMG signal as well as the three-dimensional (3D) knee kinematics of female and male elite soccer players performing an unanticipated cutting maneuver. METHODS: Fifteen female and 15 male elite soccer players performed several cutting maneuvers during which EMG of eight muscles of the leg and 3D kinematics of the knee were recorded. To create an unanticipated condition, the participants executed one of three tasks, which were signaled to them with an illuminated target board. RESULTS: Male participants generally executed the unanticipated cutting maneuver with a quadriceps activation of higher frequency components. These gender differences were also found at initial ground contact (IC) for the vastii and biceps femoris (BF) muscles. These higher frequencies dominated the signal earlier in time for the BF and later for the tibialis anterior (TA) in women. Furthermore, women performed the cutting task with greater knee abduction than did the men. CONCLUSION: Female athletes adopted a different motor unit recruitment strategy that was particularly evident at, and near, IC resulting in lower frequency components in the EMG signal of the lateral hamstring. This strategy may play a role in explaining the gender bias in anterior cruciate ligament (ACL) injury rates. Gender differences in knee kinematics were also observed, exposing the female ACL to higher strain, which may be the result of differences in neuromuscular strategies to stabilize the knee joint.
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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 | 0.002 |
| 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) | 0.003 | 0.001 |
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".