Traffic Injury Prevention
ISSN: 1538-9588 (Print) 1538-957X (Online) Journal homepage: https://www.tandfonline.com/loi/gcpi20
Frontal crash simulations using parametric human
models representing a diverse population
Jingwen Hu, Kai Zhang, Matthew P. Reed, Jenne-Tai Wang, Mark Neal & ChinHsu Lin
To cite this article: Jingwen Hu, Kai Zhang, Matthew P. Reed, Jenne-Tai Wang, Mark
Neal & Chin-Hsu Lin (2019) Frontal crash simulations using parametric human models
representing a diverse population, Traffic Injury Prevention, 20:sup1, S97-S105, DOI:
10.1080/15389588.2019.1581926
To link to this article: https://doi.org/10.1080/15389588.2019.1581926
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TRAFFIC INJURY PREVENTION
2019, VOL. 20, NO. S1, S97–S105
https://doi.org/10.1080/15389588.2019.1581926
Frontal crash simulations using parametric human models representing a
diverse population
Jingwen Hua
, Kai Zhanga, Matthew P. Reeda, Jenne-Tai Wangb, Mark Nealb, and Chin-Hsu Linb
a
University of Michigan Transportation Research Institute, Ann Arbor, Michigan; bGeneral Motors Research & Development, Warren, Michigan
ABSTRACT
ARTICLE HISTORY
Objective: Analyses of crash data have shown that older, obese, and/or female occupants have a
higher risk of injury in frontal crashes compared to the rest of the population. The objective of
this study was to use parametric finite element (FE) human models to assess the increased injury
risks and identify safety concerns for these vulnerable populations.
Methods: We sampled 100 occupants based on age, sex, stature, and body mass index (BMI) to
span a wide range of the U.S. adult population. The target anatomical geometry for each of the
100 models was predicted by the statistical geometry models for the rib cage, pelvis, femur, tibia,
and external body surface developed previously. A regional landmark-based mesh morphing
method was used to morph the Global Human Body Models Consortium (GHBMC) M50-OS model
into the target geometries. The morphed human models were then positioned in a validated generic vehicle driver compartment model using a statistical driving posture model. Frontal crash simulations based on U.S. New Car Assessment Program (U.S. NCAP) were conducted. Body region
injury risks were calculated based on the risk curves used in the US NCAP, except that scaling was
used for the neck, chest, and knee–thigh–hip injury risk curves based on the sizes of the bony
structures in the corresponding body regions. Age effects were also considered for predicting
chest injury risk.
Results: The simulations demonstrated that driver stature and body shape affect occupant interactions with the restraints and consequently affect occupant kinematics and injury risks in severe
frontal crashes. U-shaped relations between occupant stature/weight and head injury risk were
observed. Chest injury risk was strongly affected by age and sex, with older female occupants having the highest risk. A strong correlation was also observed between BMI and knee–thigh–hip
injury risk, whereas none of the occupant parameters meaningfully affected neck injury risks.
Conclusions: This study is the first to use a large set of diverse FE human models to investigate
the combined effects of age, sex, stature, and BMI on injury risks in frontal crashes. The study
demonstrated that parametric human models can effectively predict the injury trends for the
population and may now be used to optimize restraint systems for people who are not similar in
size and shape to the available anthropomorphic test devices (ATDs). New restraints that adapt to
occupant age, sex, stature, and body shape may improve crash safety for all occupants.
Received 8 November 2018
Accepted 8 February 2019
KEYWORDS
Parametric human model;
diverse population; frontal
crashes; mesh morphing;
injury risk; older occupant;
obese occupant
Introduction
The current design process for vehicle restraint systems relies
extensively on crash tests with a few anthropomorphic test
devices (ATDs). For example, the midsize male and small
female ATDs are the only adult ATDs used in FMVSS and
New Car Assessment Programs (NCAP) in the United States,
Europe, China, and many other countries. However, obese,
older, and female occupants are at increased risk of death
and serious injury compared to midsize, young, and male
occupants (Bose et al. 2011; Boulanger et al. 1992; Kent,
Henary, and Matsuoka 2005; Morris et al. 2002, 2003; Rupp
et al. 2013; Zhu et al. 2010). These vulnerable populations
may not be sufficiently represented by the two widely used
ATD sizes. From an injury biomechanics point of view, these
higher injury risks are due in part to differences in geometric, compositional, and material characteristics of bones and
soft tissues in the human body (Hu et al. 2012; Kent, Lee,
et al. 2005). Therefore, injury assessment tools, such as computational human models, used for optimizing vehicle safety
designs for those vulnerable populations should incorporate
these characteristic differences. Current finite element (FE)
human models for adults, such as models from the Global
Human Body Models Consortium (GHBMC) and the Total
Human Model for Safety (THUMS) from Toyota, have
approximately the same adult body sizes (large male, midsize
male, and small female) as current physical ATDs and do
not consider variations in skeleton geometry and external
CONTACT Jingwen Hu
jwhu@umich.edu
University of Michigan Transportation Research Institute, 2901 Baxter Rd., Ann Arbor, MI 48109.
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/gcpi.
Associate Editor Alessandro Calvi oversaw the review of this article.
ß 2019 The Author(s). Published with license by Taylor & Francis Group, LLC
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
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J. HU ET AL.
body shape outside of those anthropometric categories. As a
result, they cannot be used to evaluate the injury risks for
elderly and obese occupants or others who differ markedly
in size and shape from the ATDs.
Over the past few years, mesh morphing methods have
been applied to morph midsize male human models into
other body sizes or ages (Jolivet et al. 2015; Schoell et al.
2015; Vavalle et al. 2014). Our research group has developed
a parametric human FE modeling approach that allows the
size and shape of an FE human model to be rapidly varied
based on age, sex, stature, and body mass index (BMI;
Hwang et al. 2016a; Zhang et al. 2017a). The parametric
approach eliminates the time-consuming process of building
entirely new human models for each desired occupant size
and shape and enables population-based simulations with a
large set of human models representing a diverse population. This approach has been applied to morph the THUMS
v4 midsize male model into occupants with a wide range of
stature and BMI (Hwang, Hallman, et al. 2016; Shi et al.
2015). The impact responses of those models were compared
to cadaver tests through subject-specific validations (Hwang,
Hu, et al. 2016; Zhang, Cao, Wang, et al. 2017). The same
approach has also been applied to morph the GHBMC midsize male model into 100 human models with a wide range
of age, stature, and BMI for both men and women (Zhang,
Ca, Fanta, et al. 2017). A small set of these morphed models
(n ¼ 6) was used in U.S. NCAP frontal crash simulations to
investigate the human size and shape effects on occupant
impact responses (Hu, Zhang, Fanta, et al. 2017). However,
due to the small sample size, quantitative relationships
between occupant characteristics and injury risks could not
be analyzed.
The objective of the current study was to use a large set
of FE human models representing a diverse population to
examine the increased injury risks for older, obese, and/or
female occupants in frontal crashes and to identify safety
concerns associated with these vulnerable populations.
Methods
Baseline FE human model
In this study, the midsize male simplified occupant model
from the GHBMC (M50-OS V1.8.4) was used as the baseline
model to be morphed into a large set of diverse human
models. The GHBMC M50-OS model has been validated
extensively against cadaver tests that are relevant to frontal
crashes, including a 23-kg hub impact to the thorax with an
initial velocity of 6.7 m/s, a 48-kg bar impact to the abdomen with an initial velocity of 6 m/s, and a frontal sled test
condition with an 11.1-m/s crash pulse. In the frontal sled
condition similar to the simulation condition in the current
study, the correlation and analysis ratings for the body
excursions as well as reaction forces on the knee and seat
ranged from 0.55 to 0.83. More details of the model validation can be found in Schwartz et al. (2015) and the
GHBMC (2016).
Diverse FE human models through mesh morphing
Anthropometric targets were generated for a total of 103
human models, including 3 models corresponding to the 3
sizes of adult ATDs (small female, midsize male, and large
male) and 50 men and 50 women selected by uniform Latin
hypercube sampling (ULHS) with the age (20 to 80 years
old), stature (5th to 95th percentile), and BMI (5th to 95th
percentile) distributions based on National Health and
Nutrition Examination Survey data for the years 2011–2014
for the U.S. population (Fryar et al. 2016). A weighting factor for each ULHS-sampled occupant was calculated based
on the distribution of age, stature, and BMI in men and
women of the U.S. adult population. The weighting factor
indicates the percentage of the U.S. population represented
by each model. For each model, this means the percentage
of the population who are closer to the model in scaled size
and age than to any other model of the same sex. Weighting
factors ranged from 0.2 to 2.4% for the 100 models
through ULHS.
A detailed method for morphing the GHBMC model into
any given age, sex, stature, and BMI has been presented by
Zhang, Cao, Fanta, et al. (2017) and Hu, Zhang, Fanta,
Hwang, and Reed (2017). For the sake of completeness, the
whole-body mesh morphing method is briefly presented
here. As shown in Figure 1, the process begins with statistical models of skeletal components, including rib cage, pelvis, femur, and tibia, along with external body shape models
of human geometry that describe morphological variations
within the population as functions of age, sex, stature, and
BMI. Mesh morphing methods were then used to morph a
baseline human model into target geometries while maintaining high geometric accuracy and good mesh quality.
Given a target sex, age, stature, and BMI, the statistical
human geometry models developed previously predict thousands of points that define the body posture (Park et al.
2016; Reed et al. 2000, 2002), the size and shape of the
external body surface (Reed and Parkinson 2008), and rib
cage (Shi et al. 2014; Wang et al. 2016) and lower extremity
(Klein 2015; Klein et al. 2015) bone geometries. The skeleton
and external body shape geometries were integrated based
on the landmarks and joint locations shared in both skeleton and external body shape models. Once the target geometries were developed, the baseline model was morphed to
match the target geometries using a landmark-based 3D
nonlinear interpolation technique based on radial basis functions. The entire morphing process is automated and
requires less than 10 min per model using a typical desktop
computer. Because the target geometry was based on the
statistical human geometry models developed independent
of the GHBMC models, the geometries of the morphed
small female, midsize male, and large male models are
slightly different from the corresponding GHBMC models.
Vehicle model for crash simulations
In this study, an FE model of a midsize sedan was used for
all crash simulations. This vehicle was equipped with a
driver airbag, a crushable steering column (3 kN), a constant
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Figure 1. Rapid development of human FE models for a diverse population by mesh morphing.
load limiter (2.85 kN), a retractor pretensioner (2 kN), and
an anchor pretensioner (2 kN) but no knee airbag. This
model has undergone extensive validation against vehicle
crash test data, including both midsize male and small
female Hybrid III ATDs in both driver and front seat passenger locations under U.S. NCAP frontal crash (35 mph)
and FMVSS 208 unbelted crash (25 mph) conditions. The
average difference in joint injury probability (using absolute
values) is 3.2% for belted ATDs in the U.S. NCAP frontal
crash condition and 2.3% for unbelted ATDs in FMVSS 208
conditions. Validation results can be found in Hu, Klinich,
et al. (2017). All of the main injury measures predicted by
the ATD model were highly correlated with the test data.
Occupant positioning procedure
For each crash simulation, the morphed human model was
positioned as a driver according to a driving posture model
developed based on measurements from 68 volunteers (Reed
et al. 2002). The driving posture model predicts occupant
posture and position variables as a function of occupant
body dimensions and vehicle package factors, as shown in
Figure 2. In this study, a constant ratio of sitting height to
stature (¼ 0.52) was assumed for all 103 morphed models;
thus, only stature and BMI were used as the input parameters to define the driver dimensions. Note that the 5th and
95th percentile sitting height–to-stature values are 0.50 to
0.54 for the U.S. population. The vehicle package factors,
including seat height (H30 ¼ 294 mm), steering wheel X
(distance from the center of steering wheel to the ball of
foot reference point) (L6 ¼ 534 mm), and seat track angle
(A27 ¼ 4.5 ), were all set as constant for predicting the driving postures. Based on the regression model shown in
Figure 2, the model-predicted driver hip and eye locations
(only in the X direction) were used to position the morphed
human models, and the predicted driver-selected seat Hpoint location was used to position the seat before each
simulation. For each simulation, the driver’s hands were
positioned on the steering wheel by adjusting the shoulder
and elbow angles, and the right and left feet/shoes were
positioned onto the gas pedal and the floor, respectively, by
adjusting the hip, knee, and ankle angles. After the morphed
human models and the vehicle seat were repositioned, the
seat belt was fitted onto the occupant with mid-sternum
point (shoulder belt) and mid-abdomen point (lap belt) as
the guiding points. In this study, the occupant positioning
and seat belt fitting were conducted in a semi-automatic
manner using Matlab and Ls-PrePost.
For small and obese female models, the driver positioning
procedure occasionally resulted in initial penetration
between the steering wheel and the abdomen of the occupant. In these cases, the occupant model and seat model
were moved rearward along the seat track to keep a minimum 10-mm gap between the occupant abdomen and the
steering wheel.
Injury measures and injury risk curve scaling
For each simulation, injury measures for the head (head
injury criterion [HIC] and brain injury criterion [BrIC]),
neck (force and Nij), chest (deflection), and knee–thigh–hip
(femur force) were output. The injury risks were calculated
based on the injury risk curves provided by the U.S. NCAP.
However scaling was used for the neck, chest, and knee–thigh–hip (KTH) injury risk curves based on the sizes of the
bony structures in the corresponding body regions. Age
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J. HU ET AL.
Figure 2. Driving posture model used in this study (Reed et al., 2002).
effects were also considered for predicting the chest
injury risk.
All injury risk curves and scaling methods used in this
study are shown in Figure 3. The neck forces (tension/compression) and the intercepts for calculating the Nij were normalized based on the size of the C1 vertebra. The chest
deflection was normalized by chest depth, which is measured between the spinal process of T8 vertebra to the midsternum at the fourth intercostal space. The femur force was
normalized based on the cross-sectional area of the femur
where a load cell was defined for measuring femur force.
Results
A total of 103 U.S. NCAP frontal crash simulations with the
morphed human models were conducted. Seven examples of
simulated driver kinematics are shown in Figure 4. Both
stature and body shape exhibited significant effects on occupant kinematics. In particular, taller occupants tended to
pitch forward more than shorter occupants, and their heads
and necks tended to wrap around the top of the airbag. The
torsos of obese occupants pushed the airbag upward during
and subsequent to deployment. This pattern is most evident
for small female models because the abdomen was close to
the airbag before the event.
Some interesting trends in the injury risk distributions
with respect to the occupant weight, stature, BMI, and/or
age are shown in Figure 5. The results for the 50 female
models are represented by hollow red circles, the 50 male
models are represented by shaded blue circles, and 3 human
models corresponding to the ATD sizes are represented with
triangles. The area of each circle indicates the magnitude of
the weighting for each occupant.
As shown in Figure 5-A1, using HIC as the head injury
predictor there is a U-shaped relation between body weight
and head injury risk, indicating that both lighter and heavier
occupants experienced higher head injury risks than midsize
occupants. Interestingly, the stature effects on head injury
risk showed a significant difference between HIC and BrIC.
In particular, shorter and taller occupants generated higher
HIC values than midsize occupants, except for short obese
female occupants, whose HIC values were typically low
(Figure 5-A2). A positive correlation was observed between
stature and BrIC (Figure 5-B2), suggesting that taller occupants tend to have higher BrIC. No strong BMI or age
effects on head injury risks were observed, although BrIC
predicted much higher head injury risks than HIC in all
simulations.
The effects of occupant body weight, stature, BMI, and
age on neck injury risks are not strong based on either neck
force or Nij, although Nij predicted much higher neck injury
risks than neck force. It should be noted that the injury risk
is not zero at zero Nij and Nij tends to overestimate the
neck/cervical spine injury risks based on field data analysis
(Digges et al. 2013).
As shown in Figures 5-C1 and 5-C2, a strong sex effect
on chest injury risks is observed regardless of whether the
age effect is considered in the injury risk curve. The simulations predicted that female occupants had higher chest
injury risks than male occupants, although such effects may
be partially due to the average body size difference between
women and men. In addition, when the age effect was considered in the injury risk curve, the simulation results
showed a strong age effect on chest injury risks. The age
effect for female models was larger than that for male models, and the variation in chest injury risks was substantially
greater
among
older
occupants
than
among
younger occupants.
As shown in Figures 5-D1 and 5-D2, a very strong correlation was observed between BMI and KTH injury risk. A
similar correlation was also observed between body weight
and the KTH injury risk. The stature and age effects were
not strong for KTH injury risk.
Discussion
This study is the first to use a large set of human body
models to investigate the combined effects of age, sex, stature, and BMI on injury risks in frontal crashes and is the
first to use a validated driving posture model to rigorously
position a large set of morphed human models into a driving compartment.
TRAFFIC INJURY PREVENTION
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Figure 3. Injury risk curves and scaling method.
Generally speaking, the simulations suggested that age,
sex, stature, and BMI all significantly affect the occupant
injury risks in U.S. NCAP frontal crash condition. In particular, age has a dominant effect on occupant chest injury
risk, because being older will significantly increase the mean
and variation in the chest injury risks among the population.
This larger variation in older occupants may be partially due
to the fact that older occupants tend to have chest injury
risks closer to 50% in the simulated conditions. The simulation results also suggested that female occupants would have
higher chest injury risks than male occupants, but this effect
may be partially due to their relatively small body size. In
this study, a constant shoulder belt load limiter was used for
all simulations. Consequently, similar forces were transferred
through the seat belt to the chest of all occupants.
Regardless of the occupant interaction with the airbag, this
constant seat belt force alone may help to explain why short
female drivers showed higher chest injury risks due to chest
deflection than taller male drivers. Interestingly, the simulations showed different stature effects on head injury risks
based on HIC and BrIC. Based on HIC, shorter and taller
occupants would have higher head injury risks than midstature occupants, except for short obese females, whose
head injury risks were generally low. However, based on
BrIC, being taller would increase the head injury risk,
because the head would rotate more forward for taller occupants than for shorter occupants. The BMI effect is highly
significant for KTH injury risks, because being more obese
will increase mass-induced body excursions and produce
poor lap belt fit due to a large abdomen. All of these trends
in injury risks are broadly consistent with previous analyses
of field crash data (Bose et al. 2011; Boulanger et al. 1992;
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J. HU ET AL.
Figure 4. Examples of occupant kinematics in US-NCAP frontal crashes.
Kent, Henary, and Matsuoka 2005; Morris et al. 2002, 2003;
Rupp et al. 2013; Zhu et al. 2010). These results indicate
that restraint system designs that can adapt to occupant age,
sex, stature, and body shape have a great potential to
improve occupant protection for individuals with body sizes
and shapes different from those of available ATDs.
This study had several limitations. The statistical geometric models only include the rib cage, pelvis, femur, tibia, and
external body shape; therefore, bones in other body regions,
such as the skull, cervical spine, and feet, were morphed by
the external body surface without accurate bone geometry
prediction. Furthermore, the characteristics of the joints in
the hip, knee, and cervical spine were not changed among
the morphed models. None of the morphed human models
was validated against any cadaver tests, and the material
properties were held constant with age, sex, stature, and
body shape. The methods used in the past for human model
validation are in need of improvement, because the results
of parametric human model simulations demonstrate that
the scaling methods based on body size and weight that
have been used for corridor generation are not valid.
Specifically, the parametric human modeling results show
that body mass scaling does not appropriately account for
variations in response due to body shape. To replace these
outdated methods, we have presented preliminary results of
subject-specific model validation, in which the human model
was morphed into the geometry of specific cadavers
(Hwang, Hu, et al. 2016; Zhang, Cao, Wang, et al. 2017).
TRAFFIC INJURY PREVENTION
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Figure 5. Injury risk distributions with respect to occupant characteristics. Note: The 5th and 50th and 95th are corresponding to the sizes of three adult ATDs,
namely small female, midsize male, and large male.
The morphed human models generally produce results with
accuracy similar to that of the baseline model, but the biofidelity of the morphed human models certainly requires further investigation.
This study only used one vehicle model and crash condition; therefore, the findings only represent that particular
vehicle and may not be generalized in the whole vehicle fleet
and the range of crash conditions. However, the injury
trends suggested by this study are generally consistent with
other studies based on field crash data.
The injury measures used in this study are global measures (e.g., HIC, BrIC, Nij, chest deflection, and femur force)
typically used for ATDs. Whether these injury measures are
suitable for the GHBMC model for predicting injury risks
needs further evaluation. Other injury measures, such as
multipoint thoracic injury criterion, spine load, and tibia
load, can be considered in evaluating injury risks in the
future. Tissue-level strain and stress may also be considered,
which might be more accurate in predicting human injury
risks than global measures. However, tissue-level injury
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J. HU ET AL.
criteria and the associated injury risk curves are not currently available and will likely require significant effort
to develop.
The injury risk curves used in this study were based on
simple scaling methods using the bone dimensions, and age
effects were not considered in predicting injuries to the
head, neck, and lower extremities. This approach has 2
major weaknesses. First, the published injury risk curves
result from various scaling and processing methods applied
to cadaver data that our modeling results suggest may not
be valid (for example, mass scaling). Second, our efforts to
adjust these functions originally developed for midsize male
ATDs for use with other body sizes were based on simple
linear functions of geometry that are almost certainly inadequate to capture the true variation in tolerance with body
size. For example, we assume a constant linear relationship
between femur strength and cross-sectional area, but the
true relationship is almost certainly more complex and may
interact with sex, age, and other variables. Further investigations will be needed to define more suitable injury risk
curves for diverse populations. Nevertheless, our further
analysis showed that even if scaling was not considered in
the injury risk curves, the general trends in the injury risks
are still consistent to those with scaling. It indicated that
morphing-produced occupant size, shape, and seating posture dominated the trends in injury risks.
Finally, driving posture and belt position vary widely
even among individuals with the same overall stature and
body weight, though the current driving posture model only
estimates the average among them and the belt fit procedure
can be improved in the future. Furthermore, the driving
posture model was developed based on volunteers with
BMIs between 16.9 and 33.5 kg/m2, but the models were
extrapolated to provide predictions for BMI over 40 kg/m2.
The nonlinear differences in restraint system interaction
observed in the current study suggest that a particular difference in torso recline, fore–aft seat position, or belt placement may have little consequence for some occupants but
large effects for others. Further research is needed to differentiate among the effects of body size, body shape, posture,
and position.
In summary, this study used a large set of FE human
models (n ¼ 103) with a wide range of age, stature, and
body shape for men and women for U.S. NCAP frontal
crash simulations considering driving posture variations due
to occupant attributes. The simulations suggested that driver
stature and body shape affect occupant interactions with the
restraints, occupant kinematics, and injury risks in severe
frontal crashes, whereas driver age affects the human tolerance and in turn affects driver injury risks. In particular, Ushaped relations between occupant stature/weight and head
injury risk were observed. Chest injury risk was strongly
affected by age and sex, with older female occupants having
the highest risk. A strong correlation was also observed
between BMI and KTH injury risk, though none of the
occupant parameters meaningfully affected neck injury risks.
The morphed human models and the crash simulations
demonstrated the feasibility of using a mesh morphing
method to generate a large set of human models to represent a diverse population for evaluation of occupant injury
risks. The simulation results suggest that restraint optimization should include additional consideration of occupants
who differ substantially in size and shape from the ATDs
commonly used for vehicle safety assessment. New restraints
that adapt to occupant age, sex, stature, and body shape
may improve crash safety for all occupants.
Funding
This work was funded by General Motors (GM). The opinions
expressed in this report are those of the authors and do not necessarily
represent GM.
ORCID
Jingwen Hu
http://orcid.org/0000-0001-6477-0360
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