| description abstract | Computational human body models (HBMs) provide the ability to explore numerous candidate injury metrics ranging from local strain based criteria to global combined criteria such as the Tibia Index. Despite these efforts, there have been relatively few studies that focus on determining predicted injury risk from HBMs based on observed postmortem human subjects (PMHS) injury data. Additionally, HBMs provide an opportunity to construct risk curves using measures that are difficult or impossible to obtain experimentally. The Global Human Body Models Consortium (GHBMC) M50-O v 6.0 lower extremity was simulated in 181 different loading conditions based on previous PMHS tests in the underbody blast (UBB) environment and 43 different biomechanical metrics were output. The Brier Metric Score were used to determine the most appropriate metric for injury risk curve development. Using survival analysis, three different injury risk curves (IRC) were developed: “any injury,” “calcaneus injury,” and “tibia injury.” For each injury risk curve, the top three metrics selected using the Brier Metric Score were tested for significant covariates including boot use and posture. The best performing metric for the “any injury,” “calcaneus injury” and “tibia injury” cases were calcaneus strain, calcaneus force, and lower tibia force, respectively. For the six different injury risk curves where covariates were considered, the presence of the boot was found to be a significant covariate reducing injury risk in five out of six cases. Posture was significant for only one curve. The injury risk curves developed from this study can serve as a baseline for model injury prediction, personal protective equipment (PPE) evaluation, and can aid in larger scale testing and experimental protocols. | |
