Efforts to approximate the WSTC inspired severity metrics that aimed to quantify impact severity using kinematics. Based on animal and human exposure data, 11 this work identified the maximum allowable linear acceleration the head can withstand a given time duration, defining a relationship between linear head acceleration and time duration and severe head injury. 2Ī group at Wayne State University performed some of the earliest work on injury thresholds in the 1960s, developing the cerebral concussion tolerance curve (WSTC). The text in contemporary helmet standards generally does not include a rationale on the choice of attenuation metric, however it is generally accepted that the choice of head acceleration is at least partially motivated by research on head injury biomechanics dating back to the 1950s and 1960s. Acceleration, 1, 5 or functionals using acceleration, 29 establish helmet ability to attenuate impact. Minimum helmet protective capacity is currently established through standard laboratory impact testing. At the same time, international organizations are discussing how helmet certification methods might change towards assessing helmet ability to protect wearers from diffuse brain injury. 10 It is understood that helmet use mitigates the risk of severe focal head injury, however the perceived increase in rates of sport-related brain injuries has led to increased research efforts examining the role of helmets in brain protection. 21 Despite the widespread use of helmets, sport and recreation-related head injury remains the second most common cause of hospitalization for traumatic brain injury (TBI). A 2012 study considering football impacts dating back to 1961 found instances of brain injuries causing disability continually increased each year. In nearly all cases, the best two-variable model included peak resultant angular acceleration, α R, and Δ ω R.īrain injuries, such as concussion, occur in hockey at rates up to 0.54 for high school, 19 0.41–3.1 for collegiate 8, 15 and 1.81 for professional, exposures. Resultant change in angular velocity, Δ ω R, better predicted CSDM-15 and MPS than the current helmet certification metric, peak g, and was the most efficient model for predicting strain, regardless of impact location. Linear regression models, compared through multiple regression techniques, calculating adjusted R 2 and the F-statistic, determined the most efficient set of kinematics capable of predicting SIMon-computed brain strain, including the cumulative strain damage measure (specifically CSDM-15) and maximum principal strain (MPS). Impacts to the helmet front, back and side included impact speeds from 1.2 to 5.8 ms −1. To understand the relationship between kinematic measures and brain strain, we completed hundreds of impacts using a 50th percentile Hybrid III head-neck wearing an ice hockey helmet and input three-dimensional impact kinematics to a finite element brain model called the Simulated Injury Monitor (SIMon) ( n = 267). ![]() Due to growing concern on brain injury in sport, and the role that helmets could play in preventing brain injury caused by impact, biomechanics researchers and helmet certification organizations are discussing how helmet assessment methods might change to assess helmets based on impact parameters relevant to brain injury.
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