Helmet Shape and Size Considerations in Short Track Speed Skating Crash Pad Impacts
Bibliographic record
Abstract
Worldwide, short track (ST) speed skaters are incurring a notable number of concussions when hitting the padding that surrounds the dasher boards in their training and racing environments. Recent findings regarding the influence of helmet shape and size on peak linear deceleration values suggest that these factors matter in ST speed skating where impacts take place against compliant surfaces. This work expands upon the initial findings in several ways. In both of these studies, two types of test articles were dropped in free fall from various heights onto a crash pad. The “shape” test articles were cylindrical missiles with an end-cap of fixed area but variable radius of curvature. The expanded polystyrene end-caps had diameter values of 8, 12, 15.6, 24, and 30 in. (20.3, 30.5, 39.6, 61.0, and 76.2 cm). The “size” test articles were expanded polystyrene hemispheres of these same five diameters. Wireless 3D accelerometers (MicroStrain) were used to record acceleration while an Olympus iSpeed 2 high speed camera (1000 fps) recorded impact velocity. In the current work, impact velocities up to 12 m/s were employed. Peak linear deceleration, Head Injury Criterion (HIC), and peak jerk were determined for every impact condition. The influence of impact position on the crash mat (side, end, middle, and corner) was characterized with differences of up to 30 % in peak deceleration and 60 % in HIC values being measured between middles and corners. Penetration depths into the middle of the crash pad were also measured. Current results present a more complete picture of how helmet size and shape can affect some variables implicated in concussions. Smaller helmets produce lower peak deceleration and HIC values, and higher penetration depths. Rounder helmets penetrate more deeply into pads. At high impact speeds, the thickness and stiffness of pads likely affect the optimal helmet shape and size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".