Finite Element Simulation of Skull Fracture Evoked by Fall Injuries

This study presents novel predictive equations for von Mises stresses and deflection of bones in the frontal and lateral regions of the skull. The equations were developed based on results of a finite element model developed here. The model was validated for frontal and lateral loading conditions with input values mimetic to fall scenarios. Using neural network processing of the information derived from the model achieved R2 values of 0.9990 for both the stress and deflection. Based on the outcome of the fall victims, a threshold von Mises stress of 40.9 to 46.6 MPa was found to indicate skull fracture given a maximum input force of 26 kN and a load rate of 40 kN/ms

Simulation of Skull Fracture Due to Falls

This study presents novel predictive equations for von Mises stress values of bones in the frontal and lateral regions of the skull. The equations were developed based on results of a finite element model developed during this research. The model was validated for frontal and lateral loading conditions with input values mimetic to fall scenarios. Using neural network processing of the information derived from the model achieved R2 values of 0.9990 for both the stress and deflection. Based on the outcome of the fall victims, a threshold von Mises stress of 40.9 to 46.6 MPa was found to indicate skull fracture given a maximum input force of 26 kN and a load rate of 40 kN/ms

Rheological Model of Force Transmission Through the Helmet and Concussion Sensitivity

In contact sports, head-to-head collisions can lead to concussions, which pose serious health risks to players. This research aims to understand the force transfer from the helmet to the brain that causes concussions in collisions using a rheological model. Experimental data was gathered from players in the National Football League and testing of one type of helmet and padding. The rheological model was verified with published data and good correlation was achieved. Further sensitivity analysis of concussion risk was performed with respect to force, body weight, mass, and impact duration fit to normal and Weibull distributions using Monte Carlo simulations of impacts. A 50% threshold for moderate concussion was found based on these physiological variables. Average weight and velocity values for an NFL player in a collision gave a 50% concussion risk to a helmet to helmet impact that has a deceleration over 6.365 ms or less. Analysis of children ranging from 10 to 15 years of age was also conducted with the assumption of identical equipment to NFL players due a dearth of other research into the properties of equipment used by children. As the equipment is assumed to decrease in quality over time, this established an upper bound to the tolerance values for children. For a 50th percentile weight 10 year old male or female child, this gives thresholds of 2.483 or 2.573 ms respectively

Sensitivity Analysis of Skull Fracture

Results from multiple high profile experiments on the parameters influencing the impacts that cause skull fractures to the frontal, temporal, and parietal bones were gathered and analyzed. The location of the impact as a binary function of frontal or lateral strike, the velocity, the striking area of the impactor, and the force needed to cause skull fracture in each experiment were subjected to statistical analysis using the JMP statistical software pack. A novel neural network model predicting skull fracture threshold was developed with a high statistical correlation (R2=0.978 role= presentation \u3eR2=0.978) and presented in this text. Despite variation within individual studies, the equation herein proposes a 3 kN greater resistance to fracture for the frontal bone when compared to the temporoparietal bones. Additionally, impacts with low velocities (\u3c4.1 m/s) were more prone to cause fracture in the lateral regions of the skull when compared to similar velocity frontal impacts. Conversely, higher velocity impacts (\u3e4.1 m/s) showed a greater frontal sensitivity

Simulation of Skull Fracture Due to Falls

This study presents novel predictive equations for von Mises stress values of bones in the frontal and lateral regions of the skull. The equations were developed based on results of a finite element model developed during this research. The model was validated for frontal and lateral loading conditions with input values mimetic to fall scenarios. Using neural network processing of the information derived from the model achieved R2 values of 0.9990 for both the stress and deflection. Based on the outcome of the fall victims, a threshold von Mises stress of 40.9 to 46.6 MPa was found to indicate skull fracture given a maximum input force of 26 kN and a load rate of 40 kN/ms

Sensitivity Analysis of Skull Fracture

Results from multiple high profile experiments on the parameters influencing the impacts that cause skull fractures to the frontal, temporal, and parietal bones were gathered and analyzed. The location of the impact as a binary function of frontal or lateral strike, the velocity, the striking area of the impactor, and the force needed to cause skull fracture in each experiment were subjected to statistical analysis using the JMP statistical software pack. A novel neural network model predicting skull fracture threshold was developed with a high statistical correlation (R2=0.978 role= presentation \u3eR2=0.978) and presented in this text. Despite variation within individual studies, the equation herein proposes a 3 kN greater resistance to fracture for the frontal bone when compared to the temporoparietal bones. Additionally, impacts with low velocities (\u3c4.1 m/s) were more prone to cause fracture in the lateral regions of the skull when compared to similar velocity frontal impacts. Conversely, higher velocity impacts (\u3e4.1 m/s) showed a greater frontal sensitivity

Rheological Model of Force Transmission Through the Helmet and Concussion Sensitivity

In contact sports, head-to-head collisions can lead to concussions, which pose serious health risks to players. This research aims to understand the force transfer from the helmet to the brain that causes concussions in collisions using a rheological model. Experimental data was gathered from players in the National Football League and testing of one type of helmet and padding. The rheological model was verified with published data and good correlation was achieved. Further sensitivity analysis of concussion risk was performed with respect to force, body weight, mass, and impact duration fit to normal and Weibull distributions using Monte Carlo simulations of impacts. A 50% threshold for moderate concussion was found based on these physiological variables. Average weight and velocity values for an NFL player in a collision gave a 50% concussion risk to a helmet to helmet impact that has a deceleration over 6.365 ms or less. Analysis of children ranging from 10 to 15 years of age was also conducted with the assumption of identical equipment to NFL players due a dearth of other research into the properties of equipment used by children. As the equipment is assumed to decrease in quality over time, this established an upper bound to the tolerance values for children. For a 50th percentile weight 10 year old male or female child, this gives thresholds of 2.483 or 2.573 ms respectively