Preliminary Head-Supported Mass Performance Guidance for Dismounted Soldier Environments.

INTRODUCTION: The helmet is an ideal platform to mount technology that gives U.S. Soldiers an advantage over the enemy; the total system is recognized quantitatively as head-supported mass (HSM). The stress placed on the head and neck is magnified by adding mass and increasing the center of mass offset away from the atlanto-occipital complex, the head's pivot point on the spine. Previous research has focused on HSM-related spinal degeneration and performance decrement in mounted environments. The increased capabilities and protection provided by helmet systems for dismounted Soldiers have made it necessary to determine the boundaries of HSM and center of mass offset unique to dismounted operations. MATERIALS AND METHODS: A human subject volunteer study was conducted to characterize the head and neck exposures and assess the impact of HSM on performance in a simulated field-dismounted operating environment. Data were analyzed from 21 subjects who completed the Load Effects Assessment Program-Army obstacle course at Fort Benning, GA, while wearing three different experimental HSM configurations. Four variable groups (physiologic/biomechanical, performance, kinematic, and subjective) were evaluated as performance assessments. Weight moments (WMs) corresponding to specific performance decrement levels were calculated using the quantitative relationships developed between each metric and the study HSM configurations. Data collected were used to develop the performance decrement HSM threshold criteria based on an average of 10% total performance decrement of dismounted Soldier performance responses. RESULTS: A WM of 134 N-cm about the atlanto-occipital complex was determined as the preliminary threshold criteria for an average of 10% total performance decrement. A WM of 164 N-cm was calculated for a corresponding 25% average total performance decrement. CONCLUSIONS: The presented work is the first of its kind specifically for dismounted Soldiers. Research is underway to validate these limits and develop dismounted injury risk guidance.

Initial analysis of archived non-human primate frontal and rear impact data from the biodynamics data resource.

OBJECTIVE: The research objective was to conduct an initial analysis of non-human primate (NHP) data from frontal and rear impact events archived in the Biodynamics Data Resource (BDR) records of the Naval Biodynamics Laboratory (NBDL). These rare data, collected between 1973 and 1989, will inform the safety community of upper-end tolerance limits of NHP and may be related to severe crash scenarios. METHODS: Data from frontal and rear acceleration tests to 93 macaque NHP were examined. Each NHP was fully torso restrained, whereas the head-neck complex was unrestrained. Each NHP underwent between 1 and 21 total runs; 2 total runs was most common-a low-level run and then a high-level run. Following each impact exposure, the NHP was evaluated using a series of medical examinations. Now part of the legacy collection in the BDR, these evaluations were used to assess NHP exposures to be in one of 3 categories: noninjurious, injurious, or fatal. Using reported peak sled acceleration values, data were amenable to survival analysis statistical methodology to derive injury probability curves (IPCs). IPCs were derived for injury and fatality outcomes. RESULTS: Fatal injuries for both frontal and rear impacts were mostly at the cranio-vertebral junction. In addition to hemorrhage, fatal frontal and rear impact tests both produced predominantly atlanto-occipital dislocations, with and without spinal cord transection. After exclusions, IPCs were derived for frontal and rear impact for both (1) fatal outcome and (2) injurious outcome (any injury including fatal injury). For frontal impact, 53 NHP qualified with 5, 25, and 50% risk for fatality at 89, 105, and 114 peak sled Gs, respectively, and for injurious outcome at 70, 92, and 106 Gs, respectively. For rear impact, 34 NHP qualified with 5, 25, and 50% risk for fatality at 96, 122, 138 peak sled Gs, respectively, and for injurious outcome at 75, 99, and 115 Gs, respectively. CONCLUSIONS: The majority of injuries were at the cranio-vertebral junction, indicating that the inertial head mass caused a tensile loading mechanism to the cervical spine. These data may be used in conjunction with finite element modeling to estimate risks to the human population. The most direct application in the automotive environment could be to the well-restrained child. The Nij neck injury criteria, currently based on data from piglet studies, could also benefit because the NHP is a more accurate human surrogate. These types of tests are likely to never be repeated and will form an upper bound of tolerance information valuable to safety system designers.

Evaluation of Head and Brain Injury Risk Functions Using Sub-Injurious Human Volunteer Data.

Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.