Time series measurement of force distribution in ice hockey helmets during varying impact conditions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».