Time series measurement of force distribution in ice hockey helmets during varying impact conditions
Bibliographic record
Abstract
Modern sport helmets have been effective in reducing catastrophic head injuries such as skull fracture and subdural hematoma; yet, the high prevalence of minor traumatic brain injuries (mTBI) is an unresolved public health concern. Consequently, there is a need for greater scrutiny in a helmet's ability to mitigate collision forces that may correspond to mTBI risk. Current safety standards primarily assess a helmet's ability to minimize the whole head's peak acceleration during blunt impacts. Absent are dynamic measures local to the impact site itself due to the technical challenge to spatial map high impact force magnitudes with high temporal resolution. Inclusion of the latter measures may enhance the functional assessment of helmets. Thus, the aim of this research was to develop a localized impact mapping system (LIMS) for placement between the helmet and head interface and then to utilize the LIMS to evaluate the mechanical behaviour of various padding foams and helmets during controlled headform drop and projectile collision tests. Interposed between the helmet shell/padding and head surface, this LIMS consists of an array of discrete, thin force sensors connected to a compact signal conditioner and high speed data acquisition digital recorder. A first study demonstrated the feasibility of the LIMS to accurately capture impact events in terms of both force magnitude and temporal response. The results of this initial study demonstrated that the system could capture impact forces with acceptable error (~5%) and high correlation (0.97) between measures of global force and the sensor array. Furthermore, the LIMS demonstrated the ability to capture impact "footprints" that functionally differentiated material properties of density and temperature. A second study incorporated the LIMS as part of a standard controlled surrogate headform drop test for blunt impacts. The LIMS performed equally well on the curved cranial surface geometry of the headform and was able to differentiate unique impact contact distribution patterns based on the ice hockey helmet model's shell and padding configurations, including identification of high focal force concentrations (>16 MPa) during side impact. Of note, global head impact acceleration measures did not correspond to the magnitude of localized contact forces (R-square=0.22), but did correspond to net global contact force (R-square=0.98). A third study used the LIMS between a Hybrid III surrogate headform and an ice hockey helmet during controlled puck projectile collisions. The LIMS was effective at capturing local force distributions dynamics for short impact events lasting 2-4 ms, and again was able to distinguish between varied helmet model's padding materials and installed configurations. Five helmet models were subject to highly localized puck impact at two different velocities (V1=24.2 m/s, V2=33.3 m/s). At V2, peak contact pressures, averaged across all helmet models, were nearly double (393 N/cm^2) those recorded at the same location during vertical drop testing (201 N/cm^2). Again, linear acceleration data did not discern these differences in localized pressures. In summary, this novel testing approach provides an instrument for the assessment of helmet design and material properties on local impact dynamics, and demonstrates merit as an industrial and research tool to enhance head protection.
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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 | 0.001 | 0.001 |
| 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".