Investigating the Impact of Blunt Force Trauma: A Probabilistic Study of Behind Armor Blunt Trauma Risk.

Evaluating Behind Armor Blunt Trauma (BABT) is a critical step in preventing non-penetrating injuries in military personnel, which can result from the transfer of kinetic energy from projectiles impacting body armor. While the current NIJ Standard-0101.06 standard focuses on preventing excessive armor backface deformation, this standard does not account for the variability in impact location, thorax organ and tissue material properties, and injury thresholds in order to assess potential injury. To address this gap, Finite Element (FE) human body models (HBMs) have been employed to investigate variability in BABT impact conditions by recreating specific cases from survivor databases and generating injury risk curves. However, these deterministic analyses predominantly use models representing the 50th percentile male and do not investigate the uncertainty and variability inherent within the system, thus limiting the generalizability of investigating injury risk over a diverse military population. The DoD-funded I-PREDICT Future Naval Capability (FNC) introduces a probabilistic HBM, which considers uncertainty and variability in tissue material and failure properties, anthropometry, and external loading conditions. This study utilizes the I-PREDICT HBM for BABT simulations for three thoracic impact locations-liver, heart, and lower abdomen. A probabilistic analysis of tissue-level strains resulting from a BABT event is used to determine the probability of achieving a Military Combat Incapacitation Scale (MCIS) for organ-level injuries and the New Injury Severity Score (NISS) is employed for whole-body injury risk evaluations. Organ-level MCIS metrics show that impact at the heart can cause severe injuries to the heart and spleen, whereas impact to the liver can cause rib fractures and major lacerations in the liver. Impact at the lower abdomen can cause lacerations in the spleen. Simulation results indicate that, under current protection standards, the whole-body risk of injury varies between 6 and 98% based on impact location, with the impact at the heart being the most severe, followed by impact at the liver and the lower abdomen. These results suggest that the current body armor protection standards might result in severe injuries in specific locations, but no injuries in others.

The I-PREDICT 50th Percentile Male Warfighter Finite Element Model: Development and Validation of the Thoracolumbar Spine.

Military personnel are commonly at risk of lower back pain and thoracolumbar spine injury. Human volunteers and postmortem human subjects have been used to understand the scenarios where injury can occur and the tolerance of the warfighter to these loading regimes. Finite element human body models (HBMs) can accurately simulate the mechanics of the human body and are a useful tool for understanding injury. In this study, a HBM thoracolumbar spine was developed and hierarchically ï»¿validated as part of the Incapacitation Prediction for Readiness in Expeditionary Domains: an Integrated Computational Tool (I-PREDICT) program. Constitutive material models were sourced from literature and the vertebrae and intervertebral discs were hexahedrally meshed from a 50th percentile male CAD dataset. Ligaments were modeled through attaching beam elements at the appropriate anatomical insertion sites. 94 simulations were replicated from experimental PMHS tests at the vertebral body, functional spinal unit (FSU), and regional lumbar spine levels. The BioRank (BRS) biofidelity ranking system was used to assess the response of the I-PREDICT model. At the vertebral body level, the I-PREDICT model showed good agreement with experimental results. The I-PREDICT FSUs showed good agreement in tension and compression and had comparable stiffness values in flexion, extension, and axial rotation. The regional lumbar spine exhibited "good" biofidelity when tested in tension, compression, extension, flexion, posterior shear, and anterior shear (BRS regional average = 1.05). The validated thoracolumbar spine of the I-PREDICT model can be used to better understand and mitigate injury risk to the warfighter.

Lower Extremity Injury Risk Curve Development for a Human Body Model in the Underbody Blast Environment.

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.

Lower Extremity Validation of a Human Body Model for High Rate Axial Loading in the Underbody Blast Environment.

While the use of Human Body Models (HBMs) in the underbody blast (UBB) environment has increased and shown positive results, the potential of these models has not been fully explored. Obtaining accurate kinematic and kinetic response are necessary to better understand the injury mechanisms for military safety applications. The objective of this study was to validate the Global Human Body Models Consortium (GHBMC) M50 lower extremity using a combined objective rating scheme in vertical and horizontal high-rate axial loading. The model's lower extremity biomechanical response was compared to Post Mortem Human Subjects (PMHS) subjects for vertically and horizontally-applied high rate axial loading. Two distinct experimental setups were used for model validation, comprising a total of 33 distinct end points for validation. A combined Correlation and Analysis (CORA) score that incorporates CORA, time-to-peak (TTP) and peak magnitude of the experimental signals and ISO TS 18571 was used to evaluate the model response. For the horizontal impacts, the combined CORA scores were 0.80, 0.84, and 0.81 for compression, force, and strain respectively. For the vertical impacts combined CORA scores for the knee Z force, compression and heel Z displacement ranged from 0.70-0.81, 0.87-0.91, and 0.82-0.99 respectively. The GHBMC lower extremity model showed good agreement with PMHS experimental data in the horizontal and vertical loading environment in 33 unique tests. The accuracy is demonstrated by using the ISO TS 18571 standard and a combined CORA score that takes into consideration the peak and time to peak of the signal. The results of this study show that GHBMC v 6.0 HBM lower extremity can be used for kinetic and kinematic predictions in the UBB environment.

An Improved Method for Developing Injury Risk Curves Using the Brier Metric Score.

Many injury metrics are routinely proposed from measured or derived quantities from biomechanical experiments using post mortem human subjects (PMHS). The existing literature did not provide guidance on deciding between parameters collected in an experiment that would be best to use for the development of human injury probability curves (HIPC). The objective of this study was to use the Brier Metric Score (BMS) to identify the most appropriate metric from an experiment that predicts injury outcomes. The Brier Metric Score assesses how well a metric predicts the outcome for a censored data point (a lower BMS is better). Survival analysis was then conducted with the selected metric and the best distribution was selected using Akaike information criterion (AIC). Confidence intervals (CIs) and the normalized confidence interval width (NCIS) were calculated for the injury probability curve. The testing and validation of the methods described were performed using biomechanics data in the open literature. The methods for the HIPC development procedure detailed herein have been rigorously tested and used in the generation of WIAMan HIPCs and Injury Assessment Reference Curves (IARCs) for the WIAMan ATD, but can also be used in other ATD or PMHS injury risk curve development.

Injury risk curves in far-side lateral motor vehicle crashes by AIS level, body region and injury code.

OBJECTIVE: The objective of this study was to develop injury risk curves as a function of change in vehicle velocity for occupants in far-side lateral motor vehicle crashes (MVCs) by AIS level, body region, and specific AIS codes that commonly occur in this crash mode. METHODS: The National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) years 2000-2015 database was queried, resulting in 4,495 non-weighted far-side crashes. For each case, occupant age, sex, and the following metadata were collected: vehicle model year, vehicle body type, lateral delta-v, normalized PDOF, multiple impacts, belt use, seat position, object contacted, striking vehicle body type, maximum crush extent and side airbag deployment. Multivariable logistic regression was used to develop risk curves for AIS 2+ through 5+ injuries, AIS 2+ injuries by body region (head, thorax, lower extremity), and for each of the 10 most frequent far-side AIS 2+ injuries. Significant covariates were determined by backwards elimination (p < 0.05). The full dataset and a subsampled dataset of only cases with side airbag deployment were used to develop risk curves. RESULTS: For AIS 2+ through 5+ injury, greater delta-V was associated with greater injury risk (OR's: 2.48-3.66 per 11.9 kph increase) and belt use was associated with lower risk (OR's: 0.04-0.36 compared to unbelted). Multiple impacts were significant predictors of increased AIS 3+, 4+ and 5+ injury risk (OR's: 2.56, 2.27 and 2.83 compared to single impact). For AIS 2+ body region injuries, lateral delta-V and maximum CDC extent were positively associated with increased head, thorax and lower extremity injury risk while belt use was associated with lower risk. Increased lateral delta-v, unbelted status, and greater maximum CDC extent frequently increased injury risk for the most common far-side injuries. Side airbag deployment was not a significant covariate for the injury risk models. CONCLUSIONS: The resulting risk models expand upon previous literature gaps to provide a more comprehensive view of contributors to injury risk for occupants in far-side MVCs. This study yields risk curves based on the latest available NASS-CDS data.

Comparing rib cortical thickness measurements from computed tomography (CT) and Micro-CT.

BACKGROUND: The objective of this study was to compare cortical thickness of rib specimens scanned with clinical computed tomography (clinical-CT) at 0.5 and 1.0 mm slice thickness versus micro-CT at 0.05 mm slice thickness. Cortical thickness variation and accuracy was explored by anatomical region (anterior vs. lateral) and cross-sectional quadrants (superior, interior, inferior, and exterior). METHODS: A validated cortical thickness algorithm was applied to clinical-CT and micro-CT scans of 17 rib specimens from six male post mortem human subjects aged 42-81 years. Each rib specimen was segmented and the thickness measurements were partitioned into cross-sectional quadrants in the anterior and lateral regions of the rib. Within each rib quadrant, the following were calculated: average thickness ±â€¯standard deviation, mean thickness difference between clinical-CT and micro-CT, and a thickness ratio between clinical-CT and micro-CT. Correlations from linear regression and paired-t tests were determined for paired clinical-CT and micro-CT results. RESULTS: On average, the 0.5 mm clinical-CT underestimated the micro-CT thickness by 0.005 mm, while the 1.0 mm clinical-CT overestimated the micro-CT thickness by 0.149 mm. Thickness derived from 0.5 mm clinical-CT showed greater significant linear correlations (p < 0.05) with micro-CT thickness compared to 1.0 mm clinical-CT. CONCLUSIONS: The small mean differences and thickness ratios near 1 show validation for the cortical thickness algorithm when applied to rib clinical-CT scans. Using clinical-CT scans as way to accurately measure rib cortical thickness offers a non-invasive way to analyze millions of CT scans collected each year from males and females of all ages.