Ground reaction forces produced by two different hockey skating arm swing techniques
Bibliographic record
Abstract
The arm swing in hockey skating can have a positive effect on the forces produced by each skate, and the resulting velocity from each push off. The main purpose of this study was to measure the differences in ground reaction forces (GRFs) produced from an anteroposterior versus a mediolateral style hockey skating arm swing. Twenty-four elite-level female hockey players performed each technique while standing on a ground-mounted force platform, and all trials were filmed using two video cameras. Force data was assessed for peak scaled GRFs in the frontal and sagittal planes, and resultant GRF magnitude and direction. Upper limb kinematics were assessed from the video using Dartfish video analysis software, confirming that the subjects successfully performed two distinct arm swing techniques. The mediolateral arm swing used a mean of 18.38° of glenohumeral flexion/extension and 183.68° of glenohumeral abduction/adduction while the anteroposterior technique used 214.17° and 28.97° respectively. The results of this study confirmed that the mediolateral arm swing produced 37% greater frontal plane and 33% less sagittal plane GRFs than the anteroposterior arm swing. The magnitudes of the resultant GRFs were not significantly different between the two techniques; however, the mediolateral technique produced a resultant GRF with a significantly larger angle from the direction of travel (44.44°) as compared to the anteroposterior technique (31.60°). The results of this study suggest that the direction of GRFs produced by the mediolateral arm swing more closely mimic the direction of lower limb propulsion during the skating stride.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".