Capturing in Season Change-of-Direction Movement Pattern Change in Youth Soccer Players with Inertial Measurement Units
Bibliographic record
Abstract
This study aimed to examine the utility of inertial measurement unit (IMU) technology to identify angle, step-specific, and side-specific differences between youth soccer players with and without a history of lower limb injury during soccer-specific field tests. Thirty-two youths (mean age 16.4 years) who were elite soccer players (Females n = 13, Males n = 19) wore IMUs during pre- and postseason soccer-specific change-of-direction assessments. A response feature analysis was used to compare the change in peak resultant acceleration of the groups at a level of significance of p < 0.05. Statistical analysis revealed significant differences in change of peak resultant acceleration of right leg final foot contact in a 180° pivot turn (p = 0.012, ES = 1.0) and a 90° cut (p = 0.04, ES = 0.75) between the two groups. These data suggest that players with a history of lower limb injury might experience greater angle and side-specific change within a season in peak resultant acceleration when compared with injury-free athletes. This study demonstrates that IMUs may present a useful method to analyze youth soccer players’ change of direction movement after returning to play. These results can inform future studies investigating player monitoring and may prove to be a useful tool for coaches when designing individualized training programs in this population.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".