Assessing Kinematic Variables in Short-Track Speed Skating Helmets: A Comparative Study between Traditional Rigid Foam and Anti-Rotation Designs
Bibliographic record
Abstract
Purpose: Short-track speed skating results in high-energy crashes with an elevated risk of head injury. The goal of this study was to evaluate the resulting kinematics of an anti-rotation helmet technology for speed skating. Methods: Two traditional rigid foam speed-skating helmets (BT and ST) were compared with one anti-rotation speed skating helmet (MIPS). Each helmet was impacted with a pneumatic device across three locations. The resulting linear or rotational accelerations (PLA or PRA) and rotational velocities (PRV) were measured with accelerometers placed on a Hybrid III head form. Additionally, the head impact criterion (HIC) was calculated from accelerations and the brain injury criterion (BrIC) was obtained from rotational velocities. Results: MIPS showed significantly higher values of accelerations (PLA = 111.24 ± 9.21 g and PRA = 8759.11 ± 2601.81 rad/s2) compared with the other helmets at all three impact locations (p < 0.01, ES = 3.00 to 4.11). However, velocities were lowest, but not significantly different, for the MIPS helmet (25.77 ± 1.43 rad/s). Furthermore, all resulting kinematics except peak linear accelerations were significantly different across impact locations. Conclusion: Helmet designs specific to the collision characteristics of speed skating may still be lacking, but would decrease the risk of sport-related concussions.
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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 | 0.001 | 0.002 |
| 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".