The Effect of Shoulder Pad Design on Reducing Peak Resultant Linear and Rotational Acceleration in Shoulder-to-Head Impacts
Bibliographic record
Abstract
The National Hockey League (NHL) mandated a recent change to the material used in shoulder pads for professional ice hockey players. It has been hypothesized that bigger and harder materials may be one of the causes of increases in the risk of head injuries, by enabling the athletes to collide at higher velocities. This has led the NHL to propose changes involving the removal of plastic caps and reducing the overall size of the shoulder pads. The purpose of this research was to examine how these changes to the design of shoulder pads might influence the peak resultant linear and rotational head acceleration incurred in a shoulder-to-head impact. A helmeted Hybrid III headform was impacted at the front center of gravity using a linear impactor at 6.5 m/s and 7.5 m/s. The impactor cap was fitted with three different shoulder pad conditions: a Pro shoulder pad with a plastic cap; a Pro shoulder pad with no plastic; and a Pro shoulder pad with an expanded polypropylene (EPP) foam cap, which is smaller than the standard Pro shoulder pad. The peak resultant linear acceleration results at 6.5 m/s showed that the EPP cap had the highest value of 112 g and the Pro pad with plastic had the lowest (100 g). At 7.5 m/s the EPP cap had the lowest value (127 g) and both Pro pads (with and without plastic cap) had the same value (141 g). For peak resultant rotational acceleration, the EPP cap had the lowest values at 6.5 m/s (5882 rad/s2) and 7.5 m/s (7358 rad/s2) when compared to the other shoulder pads. These results show that the design of protective equipment can be used to lower the peak linear and peak rotational acceleration incurred by the head during an impact. In conclusion, a smaller shoulder pad may be more effective at reducing rotational accelerations that are associated with the risk of concussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".