Development of an Advanced Football Helmet to Provide Increased Protection against Concussion
Bibliographic record
Abstract
The majority of football concussions occur during tackles and other collisions and, thus, concussion associated with head impact is of primary concern for improving football helmet design. Our group is developing a helmet to minimize head-injury risk in head impacts in football. The Pro-Neck-Tor (PNT) football helmet consists of a commercially available football helmet outer shell and a custom carbon-fiber inner shell within the outer shell. A PNT mechanism is used to connect the two shells. Upon an impact to the top of the head, the PNT mechanism will deploy and the inner shell will rotate in either a flexion or extension manner. This deployment allows an escape path for the neck, provides acceleration ride-down, and significantly reduces head accelerations. Furthermore, in oblique impacts, the outer shell will deform into the void between the two shells, absorbing more energy than what padding alone can absorb. Testing conducted against a contemporary commercially available football helmet to show the effectiveness of the double-shell concept at mitigating head accelerations has shown reductions of 17 % in linear acceleration in impacts to the back of the head (velocity ∼ 3.0 m/s). In impacts to the top of the head (velocity ∼ 3.3 m/s) where the inner shell deployed, resultant linear head accelerations were reduced by approximately 38 %. Reductions in head rotational accelerations upward of 33 % were also recorded. The potential of a PNT helmet to prevent concussions in college football was estimated. The PNT helmet reduced the potential for concussion in every impact sustained and, in four instances, it decreased the potential from 85 %, 56 %, 83 %, and 77 % to 32 %, 15 %, 29 %, and 24 %, respectively.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".