Effective stiffness, damping and mass of the body during laboratory simulations of shoulder checks in ice hockey
Bibliographic record
Abstract
Ice hockey is a fast-paced sport with a high incidence of collisions between players. Shoulder checks are especially common, accounting for a large portion of injuries including concussions. The forces generated during these collisions depend on the inertial and viscoelastic characteristics of the impacting bodies. Furthermore, the effect of shoulder pads in reducing peak force depends on the baseline (unpadded) properties of the shoulder. We conducted experiments with nine men's ice hockey players (aged 19-26) to measure their effective shoulder stiffness, damping and mass during the impact stage of a shoulder check. Participants delivered a style of check commonly observed in men's university ice hockey, involving lateral impact to the deltoid region, with the shoulder brought stationary by the collision. The effective stiffness and damping coefficient of the shoulder averaged 12.8 kN/m and 377 N-s/m at 550 N, and the effective mass averaged 47% of total body mass. The damping coefficient and stiffness increased with increasing force, but there was no significant difference in the damping coefficient above 350 N. Our results provide new evidence on the dynamics of shoulder checks in ice hockey, as a starting point for designing test systems for evaluating and improving the protective value of shoulder pads.
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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) | 0.001 | 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".