Combat Helmet Suspension System Stiffness Influences ...

MILITARY MEDICINE, 183, 3/4:276, 2018

Combat Helmet Suspension System Stiffness Influences LinearHead Acceleration and White Matter Tissue Strains: Implications

for Future Helmet Design

Connor Bradfield, MS; Nicholas Vavalle, PhD; Brian DeVincentis, MS; Edna Wong, MS;Quang Luong, MS; Liming Voo, PhD; Catherine Carneal, MS

ABSTRACT Combat helmets are expected to protect the warfighter from a variety of blunt, blast, and ballisticthreats. Their blunt impact performance is evaluated by measuring linear headform acceleration in drop tower tests,which may be indicative of skull fracture, but not necessarily brain injury. The current study leverages a blunt impactbiomechanics model consisting of a head, neck, and helmet with a suspension system to predict how pad stiffnessaffects both (1) linear acceleration alone and (2) brain tissue response induced by both linear and rotational accelera-tion. The approach leverages diffusion tensor imaging information to estimate how pad stiffness influences white mat-ter tissue strains, which may be representative of diffuse axonal injury. Simulation results demonstrate that a softer padmaterial reduces linear head accelerations for mild and moderate impact velocities, but a stiffer pad design minimizeslinear head accelerations at high velocities. Conversely, white matter tract-oriented strains were found to be smallestwith the softer pads at the severe impact velocity. The results demonstrate that the current helmet blunt impact testingstandards’ standalone measurement of linear acceleration does not always convey how the brain tissue responds tochanges in helmet design. Consequently, future helmet testing should consider the brain’s mechanical response whenevaluating new designs.

INTRODUCTIONThe warfighter is exposed to a variety of threats in theater thatcan result in traumatic brain injury (TBI). It is estimated thatas many as 320,000 deployed personnel experienced TBI dur-ing Operations Enduring Freedom and Iraqi Freedom inAfghanistan and Iraq.1 To mitigate the risk of head injury,combat helmets absorb energy from blunt impacts in additionto arresting ballistic threats. Improving the performance of thecurrent helmets and suspension systems is critical to reducingthe occurrence of different types of head injuries, such as TBI.

The current US combat helmet method for blunt impactperformance assessment relies on measuring linear headformaccelerations during a drop tower test.2 Although linear headacceleration has been correlated with skull fracture throughthe Head Injury Criterion3 (HIC), previous studies have dem-onstrated that the brain’s mechanical response does not corre-late well with resultant linear acceleration.4,5 As TBI occurs atthe tissue level, it is imperative to assess the brain tissue’smechanical response, in addition to head kinematic-basedinjury criteria such as HIC, when assessing how combat hel-met design can influence brain injuries.

One of the induced brain injuries possible from these bluntimpact events is diffuse axonal injury (DAI), whose presencehas been correlated with impaired neurological function follow-ing trauma.6 It is hypothesized that the stretching of the axonsplays a role in initiating DAI. Although previous work hasmeasured gross brain tissue motion during blunt impact,7,8 nostudies have characterized white matter tract-oriented tissuedeformation in situ under dynamic loading. To make such pre-dictions, previous studies have leveraged finite element model-ing (FEM) and diffusion tensor imaging (DTI) to predictmacroscopic-level tensile strains in the direction of the mainwhite matter fiber tracts.9–17 White matter tract-oriented strains(TOS) have demonstrated to be better predictors of traumaticaxonal injury when compared with maximum principal strainin a porcine FEM,15 as well as better indicators of concussionwhen compared with other tissue-based and kinematic-basedmetrics in both automotive and sports accident reconstructionFEMs.14,17

Although finite element modeling can estimate tract-oriented strains when the head is subjected to loading, itcan also serve as a tool to optimize combat helmet designfor different loading environments. This can inform futurehelmet designs that seek to mitigate various types of inju-ries for specific threats. The purposes of this study are (1)to demonstrate a methodology that assesses how altering ahelmet’s suspension system stiffness can influence both lin-ear head acceleration and corresponding white matter tissuestrains due to combined linear and rotational accelerationand (2) to determine if both metrics respond similarly tochanges in helmet design.

Applied Physics Laboratory, The Johns Hopkins University, 11100Johns Hopkins Rd, Laurel, MD 20723.

The views expressed in this article are those of the authors and do notnecessarily represent the official position or policy of the U.S. Government,the Department of Defense, or the Department of the Air Force.

doi: 10.1093/milmed/usx181© Association of Military Surgeons of the United States 2018. All rights

reserved. For permissions, please e-mail: [email protected].

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METHODSAs an overview to the devised methodology, a FEM of the hel-met system and human head was developed and subsequentlyvalidated against previously published experimental brain dis-placement data resulting from blunt impacts. Following valida-tion, the head FEM was integrated with DTI information topredict white matter tract-oriented deformations. Next, a FEM-based white matter tract-oriented strain injury risk curve wasderived from previously conducted football impact reconstruc-tion experiments. Finally, the validated model and injury riskcurve were used to assess how altering pad stiffness influencedprobability of injury due to head acceleration and brain defor-mation in a series of frontal impact simulations. The flowchartof this progression can be seen in Figure 1. Each of these stepsare described in detail in the following sections.

FEM DevelopmentGeometries of a helmet and suspension system were generatedby 3D laser surface scans of a large Advanced Combat Helmet(ACH) (Specialty Defense Systems, Dunmore, PA, USA) andthe standard seven-pad configuration (Team Wendy, Cleveland,OH, USA) (Nikon K610; Nikon, Brighton, MI, USA). Finiteelement meshes were created for the helmet shell as well astrapezoidal, oblong, and crown pads in TrueGrid (version3.1; XYZ Scientific Applications, Inc. Pleasant Hill, CA,USA). To develop accurate pad material properties for theFEM, physical pads were tested with an Instron to generateforce/displacement curves. Each pad type was tested at loadingrates of 2 mm/s, 20 mm/s, 200 mm/s, and 2 m/s to 90% strain(n = 3 for each loading rate and pad type). A stress–strain

relationship was obtained for each loading rate, with a 20m/sstress–strain response extrapolated from the experimental data.All stress/strain curves were used as inputs in LS-DYNAwith a Fu-Chang foam material model. Single-pad compres-sion simulations were conducted at the same loading rates toensure the FEM pad response matched the experimentalforce–displacement results. Additionally, the helmet shellwas defined as a linear elastic material, with a Young’s mod-ulus of 12.25 GPa, which was based on average values froma previous study.18

In order to generate numerical predictions of brain tissuestrain, a pre-existing finite element head model19 was re-meshed in TrueGrid to provide increased stability for largedeformations in LS-DYNA (version R7.0; LSTC, Livermore,CA, USA). The updated brain mesh consisted of 15,222 hexa-hedral elements, whereas a single layer of 3,354 hexahedralelements was included to represent the cerebral spinal fluid(CSF) and the pia-arachnoid complex. The falx and tentoriumwere modeled with 2D shell elements with a pre-tension asdefined by a previous study.20 The skull was meshed with58,982 tetrahedral elements and was nodally connected to theCSF/pia-arachnoid complex layer of hexahedral elements. Thecompleted head mesh was scaled to reflect the dimensions ofa 50th percentile male.21 The brain material properties wereadopted from a previous head modeling study22 and are sum-marized in Table I. To allow for head rotation during impactsimulations, the validated head model was integrated with aHybrid III Anthropomorphic Test Dummy neck FEM.

FEM ValidationTo validate the brain response in the current FEM, brain tissuenodal displacements during simulated impacts were comparedwith experimental results from previous studies.7,8 In thesestudies, Post Mortem Human Surrogate (PMHS) heads weresubjected to blunt impacts, and the subsequent motion of neu-tral density targets (NDTs) implanted the brain parenchymawere measured with a bi-planar high-speed X-ray system. Headkinematics were measured with an array of linear acceler-ometers capturing both linear and rotational head accelerationFIGURE 1. Flowchart of the finite element head model’s progression.

TABLE I. Finite Element Head Model Material Properties in LS-DYNA

Part Density Material Model Material Parameters Reference

Skull 1,600 kg/m3 Elastic E 8= GPa0.22ν =

Brain 1,000 kg/m3 VISCOELASTIC G 7.500 = kPaG 1.50=∞ , kPa

0.08β = 1/msK 2.19= GPa

Mao et al22

Falx/tentorium 1,133 kg/m3 Elastic Plastic Thermal E 36= MPa0.20ν =

Golman et al20

Cerebrospinal fluid/pia-arachnoid complex 1,000 kg/m3 VISCOELASTIC G 0.750 = kPa

G 0.15=∞ kPa0.08β = 1/ms

K 1.07= GPa

Mao et al22

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Combat Helmet Suspension System Stiffness Influences Linear Head Acceleration and White Matter Tissue Strains

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of the PMHS skull. For the current approach, the skull of thehead FEM was prescribed the same linear and rotational accel-erations as the experiments, and the relative displacements ofbrain nodes corresponding to the same location at the NDTswere extracted. CORrelation and Analysis software (CORA;Partnership for Dummy Technology and Biomechanics,Gaimersheim, Germany) was utilized to objectively rate thedifference between FEM nodal and experimental NDT dis-placement time histories for each marker location in thebrain.23,24 CORA scores range from 0 to 1, with a score of 1indicating a perfect correspondence between the model andexperiment. The model was compared with three blunt impactexperiments consisting of a front (383-T1), side (291-T1), andrear (755-T2) impact.7,8

DTI/FEM IntegrationTo calculate tract-oriented strains from the computationalmodel, diffusion tensor imaging data from a white matteratlas25 was co-registered to the head FEM brain mesh inMatlab (version 8.4; The MathWorks Inc., Natick, MA, USA)(Fig. 2). Next, the principal diffusion eigenvectors weremapped to corresponding brain elements with a weightedaverage method previously described.9 For the current study,a fractional anisotropy threshold of 0.2 was used to determinewhite from gray matter.26 If a majority of voxels for a givenelement were white matter, then the element was assigned anaveraged fiber tract unit vector. Each element contained an aver-age and standard deviation of 109.1 ± 75.5 voxels (mean ±1 standard deviation). Each voxel was 1mm3, whereas the aver-age white matter element volume was 73.3 ± 38.2mm3.

For brain elements that were considered to be white mat-ter, the Lagrangian strain tensor was extracted at 2 kHz. Asensitivity study demonstrated that this frequency was sufficient

to capture peak strains while minimizing the computationalcost for post-processing results in Matlab. To calculate thetract-oriented strains for a given element, the followingequation was used:

t a t E t a tTOS( ) ˆ ( ) ∙ ( ) ˆ ( ) (1)=

where E t( ) and a tˆ ( ) are the Lagrangian strain tensor and ele-ment fiber tract unit vector for each timepoint, respectively.It should be noted that orientation of the fiber tract vectorswas updated to account for rigid head rotation throughoutthe duration of the simulation.15

As tract-oriented strains varied throughout the brain spa-tially and temporally, volume-based peak strain thresholdswere derived for each simulation. Peak tract-oriented strainswere chosen to be the maximum value of each white matterelement’s TOS over the entire simulation duration. The tract-oriented strain threshold exceeding the top 10% brain volumeon the cumulative peak strain distribution was referred to asthe V10 threshold. This approach was similar to the method-ology specified by Sullivan and colleagues, with the excep-tion that the strain corresponded with percent brain volume,rather than a population of nodes.15 Elements that exceededthe V10 threshold contained the elements that were amongthe highest TOS for a given simulation and were indicativeof the worst case deformation within the brain’s white matter.

Risk Curve FormulationAs the current standards for military helmet evaluation relyon reducing peak linear acceleration to less than the 150 gthreshold determined by Slobodnik, there is little incentiveto further improve helmet design if it passes this standard.27

To encourage helmet manufacturers to continually improve

FIGURE 2. Development of finite element model. (A) Process for obtaining element-specific white matter fiber directions. (B) Frontal impact with head/neck assembly and 30 kg impactor at 4.30 m/s.



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designs, Rowson and Duma established a rating system fordetermining how well collegiate football helmets mitigatedthe risk of concussion for various impact velocities.28 Assessinghelmet performance based on graded injury risk can also benefitthe military community and serve to compare different types ofinjury when considering design parameters (e.g., risk of skullfracture versus risk of DAI).

To determine the probability of injury based on FEM tract-oriented strains, publicly available concussion data were uti-lized from a previous study that reconstructed 24 headimpacts from the National Football League (NFL).29 Out ofthese impacts, nine resulted in concussion. The current studyperformed a simulation for each impact, with the prescribedlinear and rotational FEM head accelerations matching theircorresponding experimental values. Concussion risk was for-mulated with a logistic regression model:

Probability of Concussion1

1 e(2)

( V10 )=

+ − α∗ +β

where α and β are fixed coefficients and V10 is the tract-oriented strain threshold predicted from the FEM.

Frontal Impact SimulationsTo determine how changes in a helmet’s suspension systeminfluence head kinematics and brain response, the pad stress–strain curves were scaled by a factor of 0.5, 1, and 2 to createsoft, baseline, and stiffer responses, respectively. For each padstiffness, a 30-kg, 241-mm-diameter mass impacted the frontof the ACH. This impactor mass produced comparable headaccelerations to those measured from experimental drop towertests. Three different initial velocities were considered with3.05 m/s, 4.30m/s, and 5.27m/s to emulate 1×, 2×, and 3×the baseline kinetic energy from the current drop tower stan-dards.2 Each simulation was assessed for risk of injury based

on (1) peak linear resultant acceleration as determined by theoriginal formulation of injury by the Virginia Tech STAR sys-tem28 and (2) the V10 TOS threshold determined from theFEM. The original STAR injury risk curve was used becauseit was comparable with the current US combat helmet teststandards, which solely rely on peak linear acceleration, ratherthan the updated combined probability risk curve that relieson both linear and rotational acceleration.30

RESULTSWhen the FEM was compared with the experimental brain dis-placement data for validation purposes, the average CORAscores were 0.622 ± 0.161, 0.494 ± 0.054, and 0.466 ± 0.177(mean ± 1 standard deviation) for the rear, frontal, and sideimpacts, respectively. For the markers that received the highestscores (ranging from 0.72 to 0.83), the timing and magnitudesof peak nodal displacements matched the experimental NDTdisplacements (Fig. 3). The majority of cases that receivedlower CORA scores (ranging from 0.2 to 0.33) either overes-timated or underestimated the peak marker displacement. Asmall subset (n = 5) of the FEM nodes that received a poorCORA score underwent displacements in the opposite directionof the experimental NDT results. Exemplar time historiescan be seen in Figure 3 and all validation data are located inFigures 6–8.

In simulation of the NFL reconstruction experiments, thelogistic regression model gave a probability of concussionbased solely on the FEM brain strain, with a 50% risk of con-cussion occurring when the top 10% brain volume experiencedTOS greater or equal to 0.11 mm/mm (Fig. 4). Similarly, 10%and 90% chances of concussion occurred when the V10 TOSthresholds were 0.06 mm/mm and 0.16 mm/mm, respectively.

The relative influence of pad stiffness on linear head accel-eration and brain response can be seen in Figure 5. When thehead, neck, and ACH assembly was impacted at 3.05 m/s, the

FIGURE 3. Exemplar brain node displacement time histories (solid) co-plotted with their corresponding experimental neutral density target displacements(dashed).



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softer pad (0.5× baseline stiffness) produced the lowest peakaccelerations, whereas the baseline pad produced the highestpeak linear acceleration, with values of 65.2 g and 86.9 g. Thestiffer pad had slightly smaller peak acceleration than the morecompliant baseline pads, with a value of 84.1 g. However, thestiffer pad had an overall higher acceleration during the frontalpad compression phase (30–37ms) (Fig. 5A).

For the 5.27 m/s impact velocity, the softest pad led tothe highest peak linear acceleration of 222 g, whereas thestiffer pads produced the smallest peak acceleration of 181 g(Fig. 5G). During this simulation, it was observed that thesoftest and baseline pads “bottomed out” upon impact, caus-ing increased load transmission between the helmet shell andthe skull. Increasing the material stiffness by a factor of 2prevented the pad from flattening out at this high impactvelocity. It should also be noted that the period between theinitiation of loading and peak acceleration increased withpad compliancy for the 5.27 m/s impact velocity but was rel-atively consistent for the 3.05 m/s and 4.30 m/s impacts.

For all the three impact velocities, the softest pads pro-duced the smallest V10 tract-oriented strain thresholds,despite producing the largest peak linear accelerations at the5.27 m/s impact velocity (Fig. 5C, F, I). At 3.05 m/s, thesofter, baseline, and stiffer pads led to V10 TOS values of0.039 mm/mm, 0.044 mm/mm, and 0.051 mm/mm, respec-tively (Fig. 5C). When the impactor velocity was increasedto 5.27 m/s, this trend was preserved with softer, baseline,and stiffer pad V10 TOS of 0.086 mm/mm, 0.092 mm/mm,and 0.102 mm/mm (Fig. 5I).

When the risk of concussion was estimated with theVirginia Tech injury risk curve, the probability was low(<5%) for the 3.05 m/s and 4.30 m/s impacts (Fig. 5J).However, when the velocity was increased to 5.27 m/s, therisk of concussion was 81.3%, 68.8%, and 35.37% for thesofter, baseline, and stiffer pads, respectively. When the V10TOS injury risk curve predicted concussion, the risk ofinjury was similar for all pad types at the 3.05 m/s impactvelocity but had more of a pronounced effect at the 5.27 m/simpacts (Fig. 5l). The risk of injury for the softer pads was

4.9% 13.8%, and 28.4% for the low, medium, and highimpact velocities, respectively. When the pad stiffness wasincreased by a factor of 2, the probability of concussion was7.9%, 24.9%, and 44.2% for the 3.05 m/s, 4.30 m/s, and5.27 m/s impact velocities, respectively.

DISCUSSIONThe current study seeks to understand how modifying a combathelmet’s suspension system can affect the mechanical responseand predicted injury outcome of the head under blunt impactloading, which can have implications for future helmet designs.The current combat helmet test standards evaluate blunt impactperformance based on linear acceleration, which may be indica-tive of injuries such as skull fracture. In the current study, theinfluence of pad stiffness on (1) linear acceleration alone and(2) brain response due to linear and rotational acceleration wasassessed, the latter of which is hypothesized to have a bettercorrelation to diffuse axonal injury.

When the FEM brain nodal displacements were comparedwith the experimental blunt impact data for purposes of vali-dating the head FEM, the simulations were able to emulatethe timing and magnitudes of the NDT displacements as seenin the PMHS tests. CORA was used to objectively score eachdisplacement time history for all markers. There are currentlyno standards for an acceptable level of validation based onCORA scores. As a means of investigating how the CORAscores might translate into fidelity of the model, the averagescores found for the current model were compared with theCORA scores from a previous study that optimized a high-fidelity brain atlas model on the same experimental PMHSdata.31 The scores from Miller et al were found to be similar,with average values of 0.424, 0.473, and 0.453 for the samefront, side, and rear impacts used in the present analysis. Thissuggests that any lack of anatomical detail (e.g., sulci and gyri)in the current model does not significantly influence validationof the deep brain tissue displacement.

In order to predict the probability of injury due to braindeformation, a logistic regression analysis was performed withpublicly available NFL concussion data.29 As the largest tract-oriented strains for each simulation were analyzed (i.e., theV10 TOS threshold), the current study is limited to characteriz-ing the regions of the white matter that underwent highestdeformation. Although post-injury neuroimaging data were notavailable for comparison to the spatial strain patterns in theFEM, the thresholds derived from the logistic analysis allowedfor comparison to previous tissue-based injury studies.According to the risk curve in the current analysis, a 50%probability of concussion occurred when the V10 TOS wasequal to 0.11 mm/mm, which is in the conservative thresh-old range of 0.09–0.18 mm/mm for electrophysiologicalimpairment from an in vivo optic nerve stretch study.32

Changing the helmet suspension pad stiffness led to differ-ent trends for the three impact velocities in the current study.When the impactor had an initial velocity of 3.05m/s, the

FIGURE 4. Injury risk curve created from simulating the NationalFootball League impacts from Newman et al22 with the finite element headmodel.



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softer pads reduced the peak acceleration. When the impactvelocity was increased to 5.27m/s, this trend was reverseddue to the pad bottoming out during impact, creating a higherrisk of injury than the impact with the stiffer pads. This exam-ple demonstrates that the helmet protective capacity and injuryrisk may not scale directly with impact velocity and that dif-ferent suspension types have better performance in variousimpact environments. Although the current analysis was lim-ited to frontal impacts, future work can examine differentcombinations of impact locations and velocities to inform

future helmet suspension development. An improved under-standing of how design elements influence potentially compet-ing factors, such as comfort and protection, will help helmetmanufactures to make informed choices in design trade-off.The space in which the design variables can meet thesenumerous factors within acceptable range is referred to as adesign trade space. Establishing a trade space can help optimizethe suspension system design for protection performancewithout compromising on other important factors. The appli-cation of a trade space analysis can also inform helmet design

FIGURE 5. Summary of impact simulations performed with softer, baseline, and stiffer pads. (A, D, G) Linear acceleration from a node at the head’s cen-ter of gravity; (B, E, H) volume average tract-oriented strain response for anterior forebrain; (C, F, I) cumulative tract-oriented strain distribution with V10thresholds highlighted; (J) concussion probability for each simulation according to the risk curve from Rowson, 2011; (K) brain region used to createvolume-weighted TOS time histories; (L) concussion probability for each simulation according to the derived V10 TOS risk curve.



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optimization for task-specific threats and different types ofinjuries.

Although the current helmet evaluation method measureslinear headform acceleration, there are no requirements formeasuring brain response and thus cannot assess how helmetdesign can influence brain injury. In the current study, bluntimpacts were performed with a compliant neck that allowedfor head rotation. Predicting the brain response from linearacceleration alone is problematic as the V10 TOS thresholdand head kinematics did not always respond similar to changesin pad design. For the 5.27-m/s impact, stiffer pads reducedlinear head acceleration, whereas softer pads reduced brain

strain. If skull fracture corresponds to linear head accelera-tion, diffuse axonal injury corresponds to white matter tract-oriented strains, and if linear head acceleration does not cor-respond to white matter tract-oriented strains, then a singlehelmet design parameter may have different consequencesfor different types of injury. Therefore, future helmet designsshould seek to balance protection for both skull and braininjury for a given threat environment, if not minimizing therisk of both simultaneously.

Previous studies have formulated generalized kinematic-based risk functions indicative of the brain response,4,33,34 butto our knowledge, no one has developed a reliable criterion

FIGURE 6. FEM brain displacement predictions plotted with experimental values for test C755-T2.



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for estimating the brain response, nor the tract-oriented strainapproach used in the current study. Takhounts and colleaguespreviously created a Brain Injury Criterion (BrIC) to estimatethe brain response due to peak angular head velocities.34

Using BrIC for the 5.27-m/s impact velocity in the currentstudy leads to scores of 0.68, 0.67, and 0.64 for the softer,baseline, and stiffer pads, which is not indicative of the brainresponse trend from the same simulations. Although morework is needed to formulate a 1:1 relationship between headkinematics and the brain response, a recent study has demon-strated that a pre-computed brain response atlas can replicatethe FEM brain strain predictions.35 Such an approach may be

useful for future laboratory-based helmet blunt impact evalua-tion as a pre-computed response model can give instantaneousbrain strain predictions without the need to perform traditionalfinite element analysis, which is computationally expensiveand requires technical expertise.

There are limitations in the current study that deservemention. The current FEM brain was modeled as a homoge-neous isotropic material. A study by Maltese and Marguliesdemonstrated that including distinct white and gray mattertissue properties in a high-fidelity porcine, FEM can reducethe strain distribution in the white matter brain regions.36

Additionally, other studies have determined that including




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anisotropy into the brain material can influence results.12,14

Future iterations of the FEM used in the present studyshould incorporate heterogeneous anisotropic material for-mulations for increased biofidelity.

As the V10 TOS injury risk curve used in the currentanalysis was based on a small sample from NFL impactreconstructions (n = 23), it is possible that the derived injurythreshold is disproportionately weighted toward cases inwhich concussion occurred. Further, the injury risk curvesfrom the Virginia Tech STAR system were based on a differ-ent population that consisted of 63,006 impacts.28 Although

the relative trends between TOS and peak acceleration are dif-ferent due to underlying mechanics, it is possible that differ-ences in injury risk can be attributed to the two differentfootball populations considered, including different populationsizes and the distribution of injury versus non-injury cases.Further, the current study assumes that the head accelerationsfrom the NFL dataset and frontal impacts are reasonableapproximation of loading conditions that produces injury dueto blunt impacts in the battlefield. Although the characteriza-tion of head kinematics and resulting injuries observed in thebattlefield is an ongoing area of biomechanical research, the




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linear head accelerations from the current study’s frontalimpacts and NFL concussion dataset are comparable withheadform accelerations measured in accident reconstruction ofmilitary helicopter crashes that produced head injury.27 Futurework should consider additional military populations to ensurethat the loading conditions and derived injury thresholds inthe current study are applicable to the warfighter and a varietyof threat environments.

The current study makes the assumption that tissue stretch-ing in the direction of the white matter tract is important forinitiating diffuse axonal injury. Although a previous studydemonstrated that tract-oriented deformations have goodagreement with histopathology results,15 future work shouldestablish a link between macroscopic tissue strains and axonalinjury that occurs at a microscopic level. Additionally, thecurrent analysis was limited because injury was defined as theoccurrence of concussion, rather than the presence of DAI inthe football players. Future studies should also investigate therelationship between TOS deformation and neuroimagingresults, similar to other work that examined FEM maximumprincipal strain and diffusion tensor imaging of collegiatefootball players.37

SUMMARYIn summary, the current study examines how changes incombat helmet suspension stiffness can lead to differences inboth linear head acceleration and brain tissue response. Forlow and moderate impact velocities, a softer pad systemreduced the linear head acceleration and white matter tract-oriented strains. Although softer pads also reduced the brainstrains at the severe impact velocity, they also led to largerpeak linear accelerations, which may have implications forskull fracture. As linear acceleration and the brain responsedo not always respond similar to changes in pad design,future combat helmet evaluation methodologies shouldexamine both responses, as various types of injury can havedifferent sensitivities to a single design parameter. As aproof of concept, the current study demonstrates that a tissueinjury-based computational model can be used as a tool forhelmet design trade space analysis and optimization foroperation-specific needs.

ACKNOWLEDGEMENTSThe authors would like to thank Biokinetics for generously providing theNFL reconstruction data used in this study.

PRESENTATIONSPresented as a podium presentation at the 2016 Military Health SystemResearch Symposium (abstract number: MHSRS-16-1031).

FUNDINGThis work was funded with a Johns Hopkins University Applied PhysicsLab Independent Research and Development Grant.

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