Windpact applies these powerful predictive methods when running finite element analysis (FEA) to design better helmet configurations for mitigating impact. Biomechanical engineering, as well as clinical research and medical sciences, rely on the predictive power of data and inputs to drive recommendations for design, care, and treatment, respectively. Prediction and pattern completion are mainstays of neural networks, but these principles hold true in designing tangible solutions. Using predictive simulations to guide clinical and product recommendations is the newest frontier in applied engineering Namely, how injury risk increases depending on the velocity of the impactor, and its general point of contact. While a neural network is, by design, limited to the patterns within its own data set, the sheer size of the data being looked at in combination with robust, clean iteration suggests greater applicability to broader issues with head protection. Resulting coup and contrecoup injuries from side impacts may lead to deeper brain trauma than previously known. These findings align with known risks of lateral impacts in automobile, sports, and blast impact events. This implies that the side bones are generally more prone to breakage at low velocities, while the front ones are more at risk during high-velocity events. On average, the frontal bones could resist up to 3 kN (kilonewtons) more force than the side bones when the velocity of the simulated impactor was increased to its upper bound, interestingly, the frontal bones were more susceptible to breaking than the side. The team found that the side bones in the skull required less force of impact to break in comparison to the frontal bones this correlates strongly to the known greater risk of concussion posed by impacts to the side of the head. Frontal bones in the skull can tolerate injury better than the side bones in human head modeling simulations This has the added benefit of elucidating more nuanced physical properties of the skull, namely how failure threshold varies at different parts. This is how a neural network is implemented. Thus, to truly understand how skull anatomy and force factor into impact risk, we need to break the skull up into geometric coordinates that better relate the effect of impact on skull fracture likelihood.ĭoing this accomplishes two main things: standardizing the data points and outcomes from the independent studies, while also producing an intuitive model that generates accurate predictions. To accomplish this, a more intuitive, predictive method is called for. Researchers acknowledge that noisy data sets often lack the skull fracture component, as acquiring human skulls for testing remains difficult, and translational studies from animals are not particularly powerful for establishing tangible recommendations for helmet manufacturers. When exceptionally strong forces are acting on the head, such as in the case of TBIs, the resulting stresses must be absorbed or redirected by the helmet however, factors like location of the impact, individual thresholds of different parts of the skull, and the directionality of the colliding object influence how severe the injury may be. No helmet can definitively prevent concussions and skull injuries, despite their use being associated with a significantly reduced risk of skull fractures occurring. Skull injuries can happen even while wearing a helmet Neural networks are simply intuitive models that take data, identify trends and patterns, and produce a prediction output based on the inputted data.Īs the field looks more towards the predictive power of novel models, newer ways of approaching safety equipment design are becoming clear. Using a reliable statistical package to normalize the data, researchers proposed a novel neural network model predicting the skull fracture threshold. That’s why researchers with Wright State University (WSU) processed different data sets from high-profile published experiments examining skull fractures. Oftentimes, these measures cannot account for each impact’s effect on the skull in every scenario. 6, 2020.With each impact, comes a host of issues with standardization many different metrics measure concussion risk, skull fracture risk, and relative damage to the human head, but each comes with specific limitations. Minor head trauma in infants and children: Evaluation. Minor head trauma in infants and children: Management. Acute mild traumatic brain injury (concussion) in adults. American College of Emergency Physicians. Centers for Disease Control and Prevention.
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