Title Evaluation of Dynamic Response and Brain Deformation

Title Evaluation of Dynamic Response and Brain Deformation
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Evaluation of Dynamic Response and Brain Deformation
Metrics for a Helmeted and Non-Helmeted Hybrid III
Headform Using a Monorail Centric/Non-Centric Protocol
Nishizaki, Kyle; Marino, Wayne; Hoshizaki, Thomas Blaine; et
Ashare, Alan; Ziejewski, Mariusz (eds.). Mechanism of
Concussion in Sports
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Evaluation of dynamic response and brain
deformation metrics for a helmeted and non-helmeted
Hybrid III headform using a monorail centric/noncentric protocol
Kyle Nishizaki, Department of Kinesiology, University of Windsor, 225 Hemlock Rd, Rockliffe,
ON, K1M 1K4, CAN. Phone: 1-613-842-5358 Email: kylenishizaki@gmail.com
Wayne Marino, Department of Kinesiology, University of Windsor, Windsor, ON, N9B 3P4,
CAN. Phone: 1-519-253-3000 ext. 2438. Email: wmarino@uwindsor.ca
T Blaine Hoshizaki, Department of Human Kinetics, University of Ottawa, 200 Lees A106,
Ottawa, ON, K1S 5S9, CAN. Phone: 1-613-562-5851. Email: thoshiza@uottawa.ca
Andrew Post (Corresponding author) Department of Human Kinetics, University of Ottawa,
200 Lees A106, Ottawa, ON, K1S 5S9, CAN. Phone: 1-613-562-5800 ext. 7210. Email:
Anna Oeur, Department of Human Kinetics, University of Ottawa, 200 Lees A106, Ottawa, ON,
K1S 5S9, CAN. Phone: 1-613-562-5800 ext. 7207. Email: aoeur016@uottawa.ca
Evan S Walsh, Department of Human Kinetics, University of Ottawa, 200 Lees A106, Ottawa,
ON, K1S 5S9, CAN. Phone: 1-613-562-5800 ext. 7212. Email: ewals056@uottawa.ca
Michael D Gilchrist, School of Mechanical and Materials Engineering, University College
Dublin, Dublin, Belfield 4, IRL. Phone: +353-1-2830534. Email: Michael.gilchrist@ucd.ie
Marshall Kendall, Department of Human Kinetics, University of Ottawa, 200 Lees A106,
Ottawa, ON, K1S 5S9, CAN. Phone: 1-613-562-5800 ext. 7207. Email: mkend072@uottawa.ca
Head injuries and concussion in particular has become a source of interest in the sport of ice
hockey. This study proposes a monorail test methodology combined with a finite element
method to evaluate ice hockey helmets in a centric/non-centric protocol with performance
metrics more closely associated with risk of concussion. Two conditions were tested using the
protocol a) helmeted vs no helmet, and b) vinyl nitrile lined hockey helmet vs expanded
polypropylene lined hockey helmet. Results indicated that the impact velocities and locations
produced distinct responses. Also, the protocol distinguished important design characteristics
between the two helmet liner types with the vinyl nitrile lined helmet producing lower strain
responses in the cerebrum. Furthermore, it was discovered that low risk of injury peak linear and
rotational acceleration values can combine to produce much higher risks of injury when using
brain deformation metrics. In conclusion, the use of finite element modeling of the human brain
along with a centric/non-centric protocol provides an opportunity for researchers and helmet
developers to observe how the dynamic response produced from these impacts influence brain
tissue deformation and injury risk. This type of centric/non centric physical to finite element
modeling methodology could be used to guide innovation for new methods to prevent
Keywords: Ice hockey, Helmets, Standards, Concussion
1.0 Introduction
Mandatory protective headgear in impact and contact sports help protect athletes against
traumatic brain injuries (TBI) including intracranial bleeds and skull fractures. However, mild
traumatic brain injuries (mTBI), such as concussions, are still common with studies reporting
helmets not effective in managing the risk of mTBI [1,2]. The National Hockey League (NHL)
report an increase in mTBIs’ over the last decade accounting for 18% of all hockey injuries [4].
These statistics suggest that changes in the game including improved helmet technology have
had little effect on the incidence of concussion.
Present helmet technology is designed to minimize peak linear acceleration during a
direct impact [6]. Linear acceleration was chosen as the performance metric for evaluating
helmets as this measure has been associated with TBI [6;7;8]. As a result, linear dominant impact
conditions have been utilized in standards to evaluate helmets [9;10]. These standards typically
use a headform and monorail system for primarily centric (defined as the impact vector passing
through the center of gravity of the head) impacts. However, rotational acceleration has also been
identified as an important factor in the incidence of concussion and must also be measured.
Higher rotational acceleration responses tend to result from non-centric impacts (defined as
impacts whose vector does not pass through the centre of gravity of the head) [11;12]. These
rotations cause shear stress within the brain which has been proposed as a predictor for mTBI
[11;12]. Current helmet standards do not consider rotational acceleration when assessing helmet
performance despite several studies associating rotational acceleration to risk of sustaining a
concussion [5;12;13]. However, definitive thresholds of injury for concussion using linear and
rotational acceleration have yet to be elucidated; this difficulty has been identified by researchers
to be due to the kinematics not accounting for the interaction between the impact induced
motions and the brain tissue [15;16]. As a result, advanced computational models have been
developed to better understand the effect of impact head kinematics on brain tissue damage
[13;14;15]. Measuring brain tissue deformation using finite element models of the human brain
is considered an effective method in evaluating risk of sustaining an mTBI [16].
Finite element modeling of the brain during impact allows for the examination of the
effect of complex loading curves on brain tissue deformations. The characteristics of these linear
and rotational acceleration loading curves can then be used as input parameters into complex
brain models which can then simulate the deformation of tissue resulting from the kinematics of
an impact event [17;18]. Past research has shown how this method can predict the effect of linear
and rotational accelerations on the stresses and strains imparted to the brain through car crash
analysis as well as hockey and football helmet impacts [13;19;20]. As a result, finite element
models for the head and brain provide an opportunity to use brain deformation values to evaluate
the ability of a hockey helmet to reduce the risk of brain injury [20].
There is presently no standard which uses a centric/non-centric impact method coupled
with finite element analysis to measure brain deformations from helmeted impacts. If such a
method was developed it may aid in supplying more information on helmet performance using
linear and rotational acceleration as well as brain deformation metrics [13;14;15;21]. Previous
research has investigated this type of protocol using a linear impactor system, which was created
to replicate player to player collisions [5]. This linear impactor method is different from current
drop tower methods used by certification bodies to certify helmets. The development of this type
of protocol using the monorail drop system to include centric and non-centric impacts may allow
for easier adoption this new protocol using current test equipment.
The objective of this study was to use a monorail centric/non-centric impact methodology
to compare the dynamic responses of a helmeted and un-helmeted Hybrid III headform. In
addition, VN and EPP helmets were tested determine if there is any difference in the
management of linear and rotational acceleration between these impact absorbing liners using the
proposed protocol.
2.0 Methodology
2.1 Equipment
A monorail drop rig was used (Figure 1) to complete the proposed testing protocol for the
evaluation of the performance of hockey helmets. For the purpose of this study a 50th percentile
male Hybrid III head- and neckform (mass 6.08kg ± 0.01kg) was attached to the drop carriage by
the base of the neckform with a special jig designed to ensure a 90° angle between the z-axis of
the headform and the monorail (Figure 2). A 0.46 ± 0.01m tall anvil extension 0.104 ± 0.05m in
diameter was firmly fixed to the monorail base. For non-centric impacts the anvil extension was
moved horizontally 6.5cm in line with the x-axis of the headform and secured with C-clamps.
Secured on the tip of the impact anvil was a hemispherical nylon pad (diameter 0.126 ± 0.01m)
covering a modular elastomer programmer (MEP) 60 Shore Type A (0.025 ± 0.05m thickness)
disc (Figure 3). Together the nylon pad and MEP disc weighed 0.908 ± 0.001kg. The MEP was
chosen as it is a common material used in helmet standards (CSA; NOCSAE). The nylon and
MEP disc combination was not designed to reflect any particular impact scenario on the ice.
A 50th percentile adult male Hybrid III headform (mass 4.54 kg ± 0.01kg) (Figure 4) was
used in this study. This type of headform is designed to respond in a reproducible and reliable
manner and is primarily used in impact reconstructions [22]. The headform was instrumented
with nine single-axis Endevco7264C-2KTZ-2-300 accelerometers according to Padgaonkar’s
orthogonal 3-2-2-2 linear accelerometer array protocol to measure the three dimensional
kinematics of the head from an impact [23]. The headform coordinate system was defined with a
left-hand rule. Positive axes were directed toward the anterior, toward the right ear and caudally
for x, y and z respectively. The Hybrid III neck with a mass of 1.54 ± 0.05 kg was composed of 4
butyl rubber discs interlocked between five aluminum plates to simulate human vertebrae. The
discs were offset towards the front 0.5cm and were slit to elicit a different response in flexion
from that in extension [24].
2.2 Data Collection
Inbound velocity was set using the Cadex Impact v5.7a computer program and recorded using a
accelerometers (Endevco, San Juan Capistrano, CA) were sampled at 20 kHz and the signals
were passed through a TDAS Pro Lab system (DTS, Calabasas, CA) prior to being processed by
TDAS software.
2.3 Procedure
The Hybrid III was dropped at three different inbound velocities (2, 4 & 6 m/s) in order to
examine how the dynamic response changes as velocity increased (Marino and Drouin, 2000).
Three impact conditions were chosen for preliminary investigation of non-centric impacts using
the monorail drop rig and are shown/listed in Figure 5 and Table 1 [25].
Two models of helmets were tested, with three helmets of each model used for a total of 6
helmets impacted. Each model had identical two piece polyethylene (2PE) shells with either VN
or EPP liners. Dimensions of the shell and foam liner are described in Table 2.
The headform and helmeted headform was impacted using a monorail drop rig and each
condition tested three consecutive times, which is standard procedure for testing multiple-impact
helmets [9;10]. During testing the average time between impacts was 5 ± 0.50 min, which
exceeds requirements by current standards [9;10]. Impact site accuracy was ensured by marking
the helmet with a permanent marker when it was in contact with the impact cap prior to the first
drop. The helmet was reset after each impact to ensure the mark on the helmet was in line with
the mark on the impact cap. A different helmet was used for each impact velocity; therefore a
total of 162 total helmeted impacts were performed. For the un-helmeted headform condition
there was a total of 81 impacts.
2.4 Finite element model (University College Dublin Brain Trauma Model)
In addition, the resulting three-dimensional loading curve responses (x, y and z) were applied to
the University College Dublin (UCDBTM) finite element model to produce brain deformation
measurements [26;27]. The UCDBTM model geometries were determined through medical
scans of a male cadaver. The head was comprised of the scalp, skull, pia, falx, tentorium,
cerebrospinal fluid, grey and white matter, cerebellum and brain stem; totalling 26,000 elements
[26]. The validation of the model was found to be in agreement with intracranial pressure and
brain motion data taken from select cadaver experiments [28;29].
The material properties of the model were taken from the literature (Table 3, 4) [30 – 34].
The material model used for the brain was linearly viscoelastic combined with a large
deformation theory. The behaviour of this brain was characterized as viscoelastic in shear with a
deviatoric stress rate dependent on the shear relaxation modulus [26]. The compressive nature of
the brain was elastic. The shear characteristics of the viscoelastic behaviour were described thus:
G(t) = G∞ + (G0 - G∞)e-βt
where G∞ is the long term shear modulus, G0 is the short term shear modulus and β is the decay
factor [26]. The hyperelastic material model used for the brain in shear was expressed thus:
C10(t) = 0.9C01(t) = 620.5 + 1930e-t/0.008 + 1103e-t/0.15 (Pa)
where C10 and C01 are temperature-dependent material parameters, and t is in seconds [35]. A
sliding boundary between the CSF and brain was used to simulate the brain skull interface. The
CSF was modelled using solid elements with a high bulk and low shear modulus. This was done
so that the CSF could behave similar to a fluid. The contact interaction was specified as no
separation and used a friction coefficient of 0.2 [36].
The loading curves from the physical reconstructions were input at the centre of gravity
of the model and brain deformations calculated. Von Mises stress and maximum principal strain
were the metrics used to measure the resulting brain deformation in the cerebrum. The cerebrum
was chosen as the region of interest for this study as it remains the only part of the brain to have
been validated for the use of finite element modelling. The deformation metrics chosen for this
study were selected from previous anatomical and reconstructive research showing correlations
to brain injury [13 – 15]. The null hypothesis is that there would be no difference in response
between the centric and non-centric impacting conditions. The second null hypothesis is that
there would be no difference in response between the ice hockey helmets with different foam
liners (VN and EPP). Statistical test for significance between the impact sites (centric vs noncentric) and helmet foam liners (VN and EPP) were conducted by 2-way ANOVA with a 95%
confidence interval. Post hoc analyses were conducted using a Tukey HSD test.
There are some limitations to the research as presented in this study. Using a rigid
physical head form to measure the response to an impact was chosen to increase the control over
impact vector and helmet interfaces. While this facilitates these interfaces, the Hybrid III is not a
replication of a real human head, and thus the loading curves generated would not be those of a
biofidelic system, however it has been used extensively in impact research. In addition, the
Hybrid III system has only been validated for impacts in the anterior/posterior direction and the
results should be interpreted while considering this limitation. The Hybrid III neck has also been
shown to influence the response of the headform depending on impact condition [20;40]. As
such, the results in this study are not meant to replicate a human injury in the game of ice
hockey, but rather create a dynamic response to be collected and the dependent variables
compared in a controlled situation. The finite element model that has been used, while partially
validated, makes assumptions concerning the characteristics of human brain tissue which would
influence the results. A subset of elements across the tentorium was removed as they were
undergoing slightly abnormal restrictions (Doorly, 2007), however the rest of the cerebrum
underwent a rigorous validation against intracranial pressure and brain motion and was in
agreement with experimental data. As a result, the conclusions from the relationships found
between the dependents variables in this study would be more definitive if improved constitutive
data were used in the finite element models. In addition injury data from this model is compared
to literature, however it would be more ideal to compare it to injury reconstruction research using
the same version of the UCDBTM.
3.0 Results
The results for the impacts are shown in tables 5, 6, and 7. Across all sites and velocities, the use
of a VN or EPP lined ice hockey helmet reduced the dynamic response and brain deformation (p
< 0.05). When comparing the different helmet liners, the VN liner had lower magnitude linear
and rotational acceleration at 2 m/s (p<0.05) at the FBPA site than the EPP liner. At the SCG
impact site, the VN liner had lower magnitude linear and rotational acceleration at 2 m/s as well
as just rotationally at 4 m/s (p<0.05). The VN liner had lower magnitude responses at all three
velocities at the RBNA site (p<0.05). The VN helmet produced lower linear and rotational
acceleration values at 2 and 4 m/s and linearly at 6 m/s (p<0.05).
When examining the results of the brain deformation metrics, the VN helmet had lower
responses than the EPP helmet at the FBPA site at 2.0 m/s, and 6.0 m/s for both maximum
principal strain (MPS) and von Mises stress (VMS). At the 4.0 m/s velocity condition, the VN
helmet had lower values the EPP helmet for MPS but not VMS (p<0.05). For the SCG impact
site, the helmets had similar values at 2.0 m/s and 6.0 m/s for MPS and VMS (p<0.05). At the
4.0 m/s impact velocity, the VN liner had lower values than the EPP helmet when using MPS
and VMS as a metric (p<0.05). For the RBNA impact site the VN yielded lower magnitudes of
MPS and VMS at 4.0 m/s and 6.0 m/s. At the 4.0 m/s impact velocity the VN and EPP helmets
had equivalent values (p<0.05).
4.0 Discussion
The impact protocol used in this study was designed to evaluate helmets under linear dominant
(centric) and rotationally dominant (non-centric) impacts using the standardized monorail drop
rig. The results indicate, as in previous studies, that the material used in the liners of ice hockey
helmets has a significant effect on the dynamic response of the Hybrid III headform. The vinyl
nitrile helmet performed significantly better than the EPP helmet at 2 m/s in both linear and
angular acceleration. Vinyl nitrile was also significantly better in managing angular acceleration
at 4 m/s. At impact site FBPA the VN liner was significantly better in linear and angular
acceleration at 2 m/s. The vinyl nitrile liner was also significantly better at SCG 2 m/s in both
linear and angular acceleration. Also at SCG, the VN liner performed better angularly at 4 m/s.
At RBNA, the VN liner performed better in linear and angular acceleration for 2 and 4 m/s
drops. It also managed linear acceleration better at 6 m/s than the EPP liner. Overall, the results
indicate that the VN liner was more effective at reducing the brain deformation metrics
associated with concussion over the impact sites and velocity ranges tested within this research
which is similar to previous literature [20]. These results show that it is possible to influence the
brain deformation metrics using protective devices such as ice hockey helmets.
Previous research addressing the potential risk of injury has been conducted by various
researchers employing methods in anatomical and reconstructive areas of brain tissue
deformation. Using similar physical to finite element modeling methods, peak linear and
rotational acceleration risks of injury have been proposed as: 82 g and 5900 rad/s2 for 50%; 106
g and 7200 rad/s2 for 80% risk of concussion [13]. The MPS values indicating a 80% risk of
injury is from 0.225 to 0.244 for 80% [13; 14; 15]. While direct comparisons can’t be made as
the material properties of the models are different, it can be used to place the results into context
of the literature. When using this research to frame the results of this study, interesting
relationships between dynamic response and brain deformation metrics emerge. At the 2 m/s
impact condition the helmets all perform well below the 50% risk of injury for these injury
metrics. When the velocity is increased to 4 m/s the dynamic results would indicate a risk of
injury below 80% whereas the brain deformation metrics used would indicate a risk well in
excess of 80%. These results demonstrate how the linear and rotational acceleration loading
curves combine to create larger deformations in brain tissue. This also indicates that when
evaluating performance in terms of risk of injury, using brain deformation metrics to evaluate
safety of helmets may be beneficial [16].
As previous literature has shown, linearly dominant motion is more likely to cause focal
injury and intracranial pressure [37], while rotationally dominant motions are more likely to
cause diffuse shear strains of the brain [38;39]. These diffuse shear strains of brain tissue are
thought to be related to concussive injuries [11], which has been a major issue for the game of
ice hockey. Finite element modeling research has also shown that when combinations of both
linear and angular acceleration loading curves are input into the model, the resulting risk of
injury is often more severe. The protocol proposed in this study was successful in creating
centric and non-centric impact condition which produced results showing differences in the
linear and rotational performance between the ice hockey helmets. The results indicate that
measuring linear acceleration for testing standards alone may not properly evaluate the
performance of ice hockey helmets when attempting to reduce the incidence of concussion.
5.0 Conclusion
The results of this research show the importance of evaluating the performance of protective
helmets over a range of impact conditions designed to produce both linear and angular
acceleration. The use of brain deformation metrics in this research demonstrates how lower
linear and rotational acceleration magnitudes can create higher risks of injury in terms of brain
tissue stresses in the cerebrum. The use of finite element modeling of the human brain along with
a centric/non-centric protocol provides an opportunity for researchers and helmet developers to
observe how the dynamic response produced from these impacts influence brain tissue
deformation and injury risk. This type of centric/non centric physical to finite element modeling
methodology could be used to guide innovation for new methods to prevent concussion.
[1] Gerberich, S.G., Finke, R., Madden, M., et al (1987) An epidemiological study of high
school ice hockey injuries. Child Nerv Syst, 3, 59-64.
[2] Flik, K., Lyman, S., Marx, R.G., (2005) American collegiate men's ice hockey: An
analysis of injuries. Am J Sport Med, 33(2), 183-187.
[3] Wennberg, R.A., Tator, C.H., (2003) National Hockey League reported concussions,
1986-87 to 2001-02. Can J Neurol Sci, 30(3), 206-209.
[4] Emery, C.A., Meeuwisse, W.H., (2006) Injury rates, risk factors, and mechanisms of
injury in minor hockey. Am J Sport Med, 34(12), 1960-1969.
[5] Rousseau, P., Post, A., Hoshizaki, T.B., (2009) A comparison of peak linear and angular
headform accelerations using ice hockey helmets. J ASTM Int, 6(1), 11 pages.
[6] Hoshizaki, T.B., Brien, S.E. (2004) The science and design of head protection in sport.
Neurosurg, 55(4), 956–967.
[7] Ommaya, A.K., Hirsch, A.E., (1971) Tolerances for cerebral concussion from head
impact and whiplash in primates. J Biomech, 4, 13–21.
[8] Ono, K., Kikuchi, A., Nakamura, M., (1980) Human head tolerance to sagittal impact
reliable estimation deduced from experimental head injury using subhuman primates and
human cadaver skulls. In: 24th Stapp Car Crash Conference, Society of Automotive
Engineers, 103-160.
[9] ASTM F1045-07. American Society for Testing and Materials.: Standard performance
specification for ice hockey helmets. West Conshohocken, USA, 2007, p10.
[10] CAN/CSA Z262.1-09. Canadian Standard Association. Ice hockey helmets. 38:
Mississauga, ON, Canada, 2009.
[11] Holbourn, A.H.S., (1943) Mechanics of head injury. Lancet, 245, 438–441.
[12] Gennarelli, T.A., Thibault, L.E., Adams, J.H., et al (1982) Diffuse axonal injury and
traumatic coma in the primate. Ann Neurol, 12, 564-574.
[13] Zhang, L., Yang, K.H., King, A.I., (2004) A proposed injury threshold for mild
traumatic brain injury. J Biomech Eng, 126, 226-236.
[14] Willinger, R., Baumgartner, D., (2003) Human head tolerance limits to specific injury
mechanisms. Int J Crash, 8(6), 605-617.
[15] Kleiven, S., (2007) Predictors for traumatic brain injuries evaluated through accident
reconstruction. Stapp Car Crash J, 51, 81-114.
[16] King, A.I., Yang, K.H., Zhang, L., et al (2003) Is head injury caused by linear or angular
acceleration. In: IRCOBI conference, Bron, France.
[17] Yogandandan, N., Li, J., Zhang, J., et al (2008) Influence of angular accelerationdeceleration pulse shapes on regional brain strains. J Biomech, 41, 2253-2262.
[18] Post, A., Hoshizaki, T.B., Gilchrist, M.D., (2012) Finite element analysis of the effect of
loading curve shape on brain injury predictors. J Biomech, 45, 679-683.
[19] Post, A., Hoshizaki, T.B., Gilchrist, M.D., (2010) Evaluation of American football
helmet performance using finite element modelling. Poster session presented at: The
VIIIth Conference of the International Sports Engineering Association, Vienna, Austria,
[20] Post, A., Oeur, A., Hoshizaki, T.B., et al (2011) Examination of the relationship of peak
linear and angular acceleration to brain deformation metrics in hockey helmet impacts.
Comput Meth Biomech Biomed Eng, in press. Epub ahead of print.
[21] Wright, R.M., Ramesh, K.T., (2011) An axonal strain injury criterion for traumatic brain
injury. Biomech Model Mechanobio, 11, 245-260.
[22] Seemann, M.R., Muzzy III, W.H., Lustick, L.S., (1986) Comparison of human and
Hybrid III head and neck dynamic response. Stapp Car Crash J, 30, 291–310.
[23] Padgaonkar, A.J., Kreiger, K.W., King, A.I., (1975) Measurement of angular
acceleration of a rigid body using linear accelerometers. J App Mech, 42, 552-556.
[24] Ashrafiuon, H., Colbert, R., Obergefell, L., et al (1996) Modeling of a deformable
manikin neck for multibody dynamic simulation. Math Comput Model, 24(2), 45-56.
[25] Walsh, E.S., Rousseau, P., Hoshizaki, T.B., (2010) The influence of impact location and
angle on the dynamic response of a hybrid III headform. Sport Eng, 13(3), 135-143.
[26] Horgan, T.J., Gilchrist, M.D., (2003) The creation of three-dimensional finite element
models for simulating head impact biomechanics. Int J Crash, 8(4), 353-366.
[27] Horgan, T.J., Gilchrist, M.D., (2004) Influence of FE model variability in predicting
brain motion and intracranial pressure changes in head impact simulations. Int J Crash,
9(4), 401-418.
[28] Nahum, A.M., Smith, R., Ward, C.C., (1977) Intracranial pressure dynamics during head
impact. SAE paper 1977-0922.
[29] Hardy, W.N., Foster, C.D., Mason, M.J., et al (2001) Investigation of head injury
mechanisms using neutral density technology and high-speed biplanar x-ray. Stapp Car
Crash J, 45, 337-368.
[30] Kleiven, S., v Holst, H., (2002) Consequences of brain size following impact in
prediction of subdural hematoma evaluated with numerical techniques. In: Proceedings
2001 International IRCOBI Conf. on the Biomechanics of Impacts, Bron, France, 161172.
[31] Ruan, J., (1994) Impact Biomechanics of head injury by mathematical modelling (PhD
thesis). Wayne State University.
[32] Zhou, C., Khalil, T., King, A., (1995) A new model comparing impact responses of the
homogeneous and inhomogeneous human brain. In: Proceedings 39th Stapp Car Crash
Conference, Society of Automotive Engineers, 121-137.
[33] Willinger, R., Taleb, L., Kopp, C., (1995) Modal and temporal analysis of head
mathematical models. J Neurotrauma, 12, 743-754.
[34] Zhang, L., Yang, K., Dwarampudi, R., et al (2001) Recent advances in brain injury
research: A new human head model development and validation. Stapp Car Crash J, 45,
[35] Horgan, T., (2005) A finite element model of the human head for use in the study of
pedestrian impacts (PhD thesis). University College Dublin.
[36] Miller, R., Margulies, S., Leoni, M., et al (1998) Finite element modelling approaches
for predicting injury in an experimental model of severe diffuse axonal injury. SAE
paper 1998-3154.
[37] Thomas, L.M., Roberts, V.L., Gurdjian, E.S., (1966) Experimental intracranial pressure
gradients in the human skull. J Neurol Neurosurg Psych, 29, 404-411.
[38] Bain, A.C., Meaney, D.F., (2000) Tissue-level thresholds for axonal damage in an
experimental model of central nervous system white matter injury. J Biomech Eng, 16,
[39] Forero Rueda, M.A., Cui, L., Gilchrist, M.D., (2011) Finite element modeling of
equestrian helmet impacts exposes the need to address rotational kinematics in future
helmet designs. Comput Meth Biomech Biomed Eng, 14(12), 1021-1031.
[40] Foreman, S., Hoshizaki, T.B., (2010) The influence of headform geometry and neckform
orientation on the dynamic impact response of a Hybrid III head- and neckform. In: The
Ice Hockey at The Ice Hockey Summit: Action on Concussion, Mayo Clinic, Rochester,
Minnesota, USA, October.
[41] Walsh, E.S., Post, A., Rousseau P., Kendall, M., Karton, C. Oeur, A., Foreman, S. &
Hoshizaki, T.B. Dynamic impact response characteristics of a helmeted Hybrid III
headform using a centric and non-centric impact protocol. J. Sports Engineering and
Technology, 0 (0) 1-6, 2012, DOI 10.1177/1754337112442299.
Table 1. The centric and non-centric impact conditions.
Table 2. Hockey helmet characteristics
Table 3. Finite element model material properties
Cortical Bone
Trabecular Bone
Young's modulus (Mpa)
Poisson's ratio
Density (kg/m³)
15 000
Grey Matter
White Matter
Bridging veins
Table 4. Finite element model characteristics for the different regions of brain tissue
Shear Modulus (kPa)
Decay Constant Bulk Modulus
Brain Stem
Table 5. Dynamic response and brain deformation results for impacts to an unhelmeted and
helmeted (VN/EPP) headform for the three impact sites at 2 m/s. Brackets denote standard
Table 6. Dynamic response and brain deformation results for impacts to an unhelmeted and
helmeted (VN/EPP) headform for the three impact sites at 4 m/s. Brackets denote standard
Table 7. Dynamic response and brain deformation results for impacts to an unhelmeted and
helmeted (VN/EPP) headform for the three impact sites at 6 m/s. Brackets denote standard
Fig 1. Monorail drop rig.
Fig 2. 90° Jig Attachment for Hybrid III head and neckform
Fig 3. MEP Nylon Cap on an anvil extension
Fig 4. Hybrid III neck and headform.
Fig 5. Images depicting the impact locations on an ice hockey helmet.
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