Impact Location Dependence of Behind Armor Blunt Trauma Injury Assessed Using a Human Body Finite Element Model

Behind armor blunt trauma (BABT), resulting from dynamic deformation of protective ballistic armor into the thorax, is currently assessed assuming a constant threshold of maximum backface deformation (BFDs) (44 mm). Although assessed for multiple impacts on the same armor, testing is focused on armor performance (shot-to-edge and shot-to-shot) without consideration of the underlying location on the thorax. Previous studies identified the importance of impacts on organs of animal surrogates wearing soft armor. However, the effect of impact location was not quantified outside the threshold of 44 mm. In the present study, a validated biofidelic advanced human thorax model (50th percentile male) was utilized to assess the BABT outcome from varying impact location. The thorax model was dynamically loaded using a method developed for recreating BABT impacts, and BABT events within the range of real-world impact severities and locations were simulated. It was found that thorax injury depended on impact location for the same BFDs. Generally, impacts over high compliance locations (anterolateral rib cage) yielded increased thoracic compression and loading on the lungs leading to pulmonary lung contusion (PLC). Impacts at low compliance locations (top of sternum) yielded hard tissue fractures. Injuries to the sternum, ribs, and lungs were predicted at BFDs lower than 44 mm for low compliance locations. Location-based injury risk curves demonstrated greater accuracy in injury prediction. This study quantifies the importance of impact location on BABT injury severity and demonstrates the need for consideration of location in future armor design and assessment.

Deflection-based parametric survival analysis side impact chest injury risk curves AIS 2015

OBJECTIVES

The objective of this study was to reanalyze lateral postmortem human surrogate (PMHS) sled test chestband data to construct updated lateral thoracic injury risk curves (IRCs) using survival analysis.

METHODS

Chestband and injury data were gathered from 16 previously conducted PMHS sled tests. Briefly, 2 chestbands were wrapped around the thorax's circumference at the levels of ribs 4 and 8. Tests were conducted at 6.7 m/s on a rigid and padded load wall fixed to the top of a rebound sled. The injuries were reclassified using the Abbreviated Injury Scale (AIS) 2015 coding scheme. Chestband signals were combined with pretest specimen measurements to calculate the chest deflection contour time history. Deflections were determined using updated processing techniques calculating the change in length of every point on the contour from the impacted side using the thorax's midpoint as the origin. Four candidate metrics were selected: the deflection from rib 4, the deflection from rib 8, the greater of the deflections from ribs 4 and 8, and the average of the deflections from ribs 4 and 8. AIS 3+ IRCs were developed considering outcomes of AIS ≥3 injuries. All injury data were uncensored, and noninjury data were right-censored. Three specimen mass-based IRCs were determined using the IRC with the lowest Brier score metric (BSM): The first corresponded to the 5th percentile female mass (49 kg), the second to the 50th percentile male mass (77 kg), and the third to the average mass of the PMHS ensemble (65 kg).

RESULTS

Sixteen PMHS were used in the current study. Six specimens were right-censored, and 10 were uncensored. The average metric had the lowest BSM, and mass was a significant covariate with 50% risk of AIS3+ injury at 72mm of chest deflection. The 50% risk deflection magnitudes for the 5th percentile female (49 kg), 50th percentile male (77 kg), and PMHS ensemble (PMHS-E) (65 kg) were 59, 81, and 71 mm. IRCs for the 4 metrics and the 3 occupant masses are given.

CONCLUSIONS

IRCs were developed using survival analysis, and the average of the peak deflections was found to best represent the thoracic chest deflection response. Mass-based side impact IRCs were calculated for occupants representing the WorldSID 5th percentile female and 50th percentile male anthropomorphic test device.

Uncertainty Evaluations for Risk Assessment in Impact Injuries and Implications for Clinical Practice

Injury risk curves (IRCs) represent the quantification of risk of adverse outcomes, such as a bone fracture, quantified by a biomechanical metric such as force or deflection. From a biomechanical perspective, they are crucial in crashworthiness studies to advance human safety. In clinical settings, they can be used as an assistive tool to aid in the decision-making process for surgical or conservative treatment. The estimation of risk corresponding to a level of biomechanical metric is done using a regression technique, such as a parametric survival regression model. As with any statistical procedure, error measures are computed for the IRC, representing the quality of the estimated risk. For example, confidence intervals (CIs) are recommended by the International Standards Organization, and the normalized confidence interval width (NCIW) is computed based on the width of the CI. This is a surrogate for the quality of the risk curve. A 95% CI means that if the same experiment were hypothetically repeated 100 times, at least 95 of the computed CIs should contain the true risk curve. Such an interpretation is problematic in most biomechanical contexts as rarely the same experiment is repeated. The notion that a wider confidence interval implies a poorer quality risk curve can be misleading. This article considers the evaluation of CIs and its implications in biomechanical settings for safety engineering and clinical practice. Alternatives are suggested for future studies.

Hierarchical process using Brier Score Metrics for lower leg injury risk curves in vertical impact

INTRODUCTION

Parametric survival models are used to develop injury risk curves (IRCs) from impact tests using postmortem human surrogates (PMHS). Through the consideration of different output variables, input parameters and censoring, different IRCs could be created. The purpose of this study was to demonstrate the feasibility of the Brier Score Metric (BSM) to determine the optimal IRCs and derive them from lower leg impact tests.

METHODS

Two series of tests of axial impacts to PMHS foot-ankle complex were used in the study. The first series used the metrics of force, time and rate, and covariates of age, posture, stature, device and presence of a boot. Also demonstrated were different censoring schemes: right and exact/uncensored (RC-UC) or right and uncensored/left (RC-UC-LC). The second series involved only one metric, force, and covariates age, sex and weight. It contained interval censored (IC) data demonstrating different censoring schemes: RC-IC-UC, RC-IC-LC and RC-IC-UC-LC.

RESULTS

For each test set combination, optimal IRCs were chosen based on metric-covariate combination that had the lowest BSM value. These optimal IRCs are shown along with 95% CIs and other measures of interval quality. Forces were greater for UC than LC data sets, at the same risk levels (10% used in North Atlantic Treaty Organisation (NATO)). All data and IRCs are presented.

CONCLUSIONS

This study demonstrates a novel approach to examining which metrics and covariates create the best parametric survival analysis-based IRCs to describe human tolerance, the first step in describing lower leg injury criteria under axial loading to the plantar surface of the foot.

Biomechanical tolerance of whole lumbar spines in straightened posture subjected to axial acceleration

Quantification of biomechanical tolerance is necessary for injury prediction and protection of vehicular occupants. This study experimentally quantified lumbar spine axial tolerance during accelerative environments simulating a variety of military and civilian scenarios. Intact human lumbar spines (T12-L5) were dynamically loaded using a custom-built drop tower. Twenty-three specimens were tested at sub-failure and failure levels consisting of peak axial forces between 2.6 and 7.9 kN and corresponding peak accelerations between 7 and 57 g. Military aircraft ejection and helicopter crashes fall within these high axial acceleration ranges. Testing was stopped following injury detection. Both peak force and acceleration were significant (p < 0.0001) injury predictors. Injury probability curves using parametric survival analysis were created for peak acceleration and peak force. Fifty-percent probability of injury (95%CI) for force and acceleration were 4.5 (3.9-5.2 kN), and 16 (13-19 g). A majority of injuries affected the L1 spinal level. Peak axial forces and accelerations were greater for specimens that sustained multiple injuries or injuries at L2-L5 spinal levels. In general, force-based tolerance was consistent with previous shorter-segment lumbar spine testing (3-5 vertebrae), although studies incorporating isolated vertebral bodies reported higher tolerance attributable to a different injury mechanism involving structural failure of the cortical shell. This study identified novel outcomes with regard to injury patterns, wherein more violent exposures produced more injuries in the caudal lumbar spine. This caudal migration was likely attributable to increased injury tolerance at lower lumbar spinal levels and a faster inertial mass recruitment process for high rate load application. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. J Orthop Res 36:1747-1756, 2018.

Age-Infusion Approach to Derive Injury Risk Curves for Dummies from Human Cadaver Tests

Injury criteria and risk curves are needed for anthropomorphic test devices (dummies) to assess injuries for improving human safety. The present state of knowledge is based on using injury outcomes and biomechanical metrics from post-mortem human subject (PMHS) and mechanical records from dummy tests. Data from these models are combined to develop dummy injury assessment risk curves (IARCs)/dummy injury assessment risk values (IARVs). This simple substitution approach involves duplicating dummy metrics for PMHS tested under similar conditions and pairing with PMHS injury outcomes. It does not directly account for the age of each specimen tested in the PMHS group. Current substitution methods for injury risk assessments use age as a covariate and dummy metrics (e.g., accelerations) are not modified so that age can be directly included in the model. The age-infusion methodology presented in this perspective article accommodates for an annual rate factor that modifies the dummy injury risk assessment responses to account for the age of the PMHS that the injury data were based on. The annual rate factor is determined using human injury risk curves. The dummy metrics are modulated based on individual PMHS age and rate factor, thus “infusing” age into the dummy data. Using PMHS injuries and accelerations from side-impact experiments, matched-pair dummy tests, and logistic regression techniques, the methodology demonstrates the process of age-infusion to derive the IARCs and IARVs.