Sports and recreation are a leading cause of traumatic brain injury (TBI) in North America, accounting for nearly 30% of TBI cases in youth in Canada. Contact sports, such as hockey and football, put athletes at greater risk of suffering mild traumatic brain injuries (mTBI, such as concussion), relative to the general population, despite mandated helmeted use. The ability of today’s helmets to protect against brain injuries is now under consideration. Nearly all modern helmets are certified against linear acceleration, which has been linked to severe focal injuries such as contusions or hemorrhages. In mitigating linear acceleration, today’s helmets are credited with providing life-saving protection in direct head impacts.

Diffuse brain injury (of which concussion is one example) refers to widespread injury in the white matter of the brain. Angular kinematics are linked to diffuse brain injury, though helmet certification standards to date focus on limiting linear kinematics, with the exception of the National Operating Committee on Standards for Athletic Equipment (NOCSAE) that now also considers angular acceleration in helmet certification. Discussions surround how helmets may be assessed relative to kinematics linked to diffuse injury, however, a consensus has not been reached regarding which kinematics this would include.

The objective of this thesis is to identify head kinematics that predict finite element model brain strain metrics and use the results to develop a kinematic metric that could be appropriate for use in helmet assessment.

Helmeted impacts were performed with a guided rail drop using the Hybrid III head and neck as well as with the Hybrid III head without a neck. Impacts were conducted at various locations to ice hockey and football helmets at impact velocities ranging from 1.2 to 5.8 m/s and 3.9 to 6.1 m/s for hockey and football helmets, respectively, monitoring linear and angular head kinematics throughout each impact. Directional linear acceleration and angular velocity were input to the Improved Simulated Injury Monitor (SIMon) to compute brain strain metrics CSDM-15 and MPS.

Multiple regression techniques compared linear regression models based on different linear and angular kinematics from one single kinematic predictor to five kinematics in a single regression model. Adjusted R² was calculated for each model to determine which model best fit the data for each impact scenario. Comparing models that had similar R², the F-statistic was calculated to determine whether one of the compared models was significantly more descriptive of the data, than the other.

Peak resultant angular velocity (ω_R) overall yielded the greatest R² and F statistic values relative to other single kinematics as well as multi-kinematic regression models, for certification-style helmeted impacts with or without the Hybrid III neck. Arguably, ω_R could be chosen as a single kinematic predictor for strain. Choosing a single kinematic variable maximized the F-statistic as it required only a single variable to predict strain with R²>​0.8. This finding was consistent for impacts with and without the Hybrid III neck for hockey and football helmet impacts.

This study also found that the impact time duration for simulation influences maximum strain values for impacts without the Hybrid III neck. Strain values continued to increase after linear and angular impact kinematics had returned to zero or stabilized. Strain plots for no-neck impacts reached a local maximum at approximately 25 ms after initial impact and it was noted that after impact, the headform continued to translate and rotate away from the impact site, an unlikely occurrence in a real human impact. Therefore, time duration for no-neck simulation was limited to 25 ms. It is noted that research groups conducting no-neck analyses should consider the effect of time duration.

This thesis documents which kinematics best predict brain strain metrics for certification style guided drop impacts using HybridIII test equipment for two plausible impact paradigms:​ a head tethered to a neck and a head free falling, absent a neck. Additionally, it documents that the time duration of simulated kinematics influence magnitude of brain strains. These findings will be of particular interest to the helmet assessment community which is currently discussing how certification methods might change. Therefore, this thesis also documents one possible approach to use the presented experimental methods and kinematics to identify a pass/fail threshold based on predicted brain strain.