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In 2009 approximately half of the French population owned a bicycle. However, the cyclist’s accident rate is the highest of all road users. Hence, it is necessary to set up a protection system for cyclists, especially for the cephalic segment. Currently, relatively little literature has dealt with the head impact condition for this kind of accident, especially for cases of cyclist falls. Therefore, the objective of this work was to identify the initial conditions for head impact in cases of cycling fall accidents. The present paper proposes a parametric study by simulating a lot of accident scenarios. A total of 1024 simulations have been automatically carried out using Madymo®’s software and a specially designed program. Two situations of cycling falls have been investigated according to real accident configuration: cyclist falls due to skidding and after hitting a curb. The TNO (Dutch Organization for Applied Scientific Research, Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek) pedestrian 50th percentile human model was coupled to a city bicycle model. The analysed outputs are head impact area and head velocity before impact. The work has provided a solid information base for future work on cyclist accidents. The parametric analysis was also used to study the effects of poorly known environmental parameters, such as speed or torso inclination. The results provided estimates of the impact area and speed of the head, which helps to improve the design of safer helmets and of helmet certification standards tests
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Special Issue Article
Proc IMechE Part P:
J Sports Engineering and Technology
226(3/4) 282–289
ÓIMechE 2012
Reprints and permissions:
DOI: 10.1177/1754337112442326
Head impact conditions in the case of
cyclist falls
Nicolas Bourdet
, Caroline Deck
, Rui P Carreira
and Re
´my Willinger
In 2009 approximately half of the French population owned a bicycle. However, the cyclist’s accident rate is the highest
of all road users. Hence, it is necessary to set up a protection system for cyclists, especially for the cephalic segment.
Currently, relatively little literature has dealt with the head impact condition for this kind of accident, especially for cases
of cyclist falls. Therefore, the objective of this work was to identify the initial conditions for head impact in cases of
cycling fall accidents. The present paper proposes a parametric study by simulating a lot of accident scenarios. A total of
1024 simulations have been automatically carried out using Madymo
s software and a specially designed program. Two
situations of cycling falls have been investigated according to real accident configuration: cyclist falls due to skidding and
after hitting a curb. The TNO (Dutch Organization for Applied Scientific Research, Nederlandse Organisatie voor
Toegepast Natuurwetenschappelijk Onderzoek) pedestrian 50th percentile human model was coupled to a city bicycle
model. The analysed outputs are head impact area and head velocity before impact. The work has provided a solid infor-
mation base for future work on cyclist accidents. The parametric analysis was also used to study the effects of poorly
known environmental parameters, such as speed or torso inclination. The results provided estimates of the impact area
and speed of the head, which helps to improve the design of safer helmets and of helmet certification standards tests.
Cycling fall, head impact, multibody simulation, parametric study
Date received: 15 December 2011; accepted: 24 February 2012
The cyclist accident rate remains high among all road
users. According to Linn et al.’s study in 1998,
three segments that are most often hit during cycling
accident are the arms, the legs and the head, with 50%
of the cases. Moreover, Otte
showed that 70% of fatal
cyclist accident cases are due to head injuries.
Maimaris et al.
and Eilert-Petersson and Schelp
reported that most of the cycling accident configura-
tions were single falls. In contrast, Yang and Otte
Got and Got
have shown that most of the cyclist acci-
dents happened against antagonist vehicles. Finally,
Depreitere et al.
showed that cycling accidents resulted
from falls and from collisions with vehicles in the same
proportion. According to Ricard and Thelot
from cycling falls make up the majority of the injured
population received in emergency (89%).
Currently, there is little information available on the
head impact conditions for this kind of accident, which
mainly concerns windscreen impact. In 2000 and 2003,
Maki et al.
and Maki and Kajzer
studied the pedes-
trian and cyclist kinematics according to the vehicle
geometry. It appeared that the impact zones, the
impact velocity and angle were different between
cycling accidents and pedestrian accidents. Hence, the
authors concluded that the existing pedestrian tests
were not applicable to cyclists. In 2001, Werner et al.
studied the human–bicycle decoupling. He showed that
for off-centre impact, or a bicycle velocity over 5 m/s,
the cyclist was often thrown to the ground without
touching the car. Several authors, such as Serre et al.,
have reconstructed many accidents between car and
cyclists in order to identify the cyclist kinematics during
impact. However, cyclist falls that occur on their own
have not been studied because of the wide range of pos-
sible scenarios and the difficulty of categorising them.
Therefore, the objective of the present work is to
Strasbourg University, France
Oxylane Research Villeneuve d’Ascq, France
Corresponding author:
Nicolas Bourdet, Strasbourg University, 2 rue Boussingault, Strasbourg
F67000, France.
evaluate the initial conditions of head impact for cases
of cycling falls by simulating a great number of possible
accident situations.
Materials and methods
The present paper proposes a methodology based on
an automatic parametric study to evaluate the head
impact area and velocity for single cyclist falls. A total
of 1024 simulations have been automatically carried
out using MadymoÒsoftware coupled with a special
designed program allowing to compute and to analyse
the simulated scenarios. The analysed outputs are, suc-
cessively, head initial orientation and velocity just
before head contact. The cyclist’s kinematics were
reconstructed based on multi-body computing with
Madymo software. The principle of solving a multi-
body system is to define a set of rigid bodies repre-
sented by ellipsoids and connected by joints. Unlike
finite elements, contact between two bodies is not com-
puted by deformable surfaces but by a penetration
force defined by a function. The computational time of
this multi-body approach is strongly reduced compared
to finite element simulation. The models are developed
using ellipsoids in such a way that the geometry, mass
and inertia are respected. Hence, the human model
used was the TNO pedestrian model implemented in
the Madymo package and validated against post mor-
tem human tests, as depicted by van Hoof et al.
De Lange et al.
In the present study, the model used
to represent the cyclist had a mass of 75 kg and a height
of 1.75 m. The bicycle was modelled in accordance with
the geometry and mass of a city bicycle, as illustrated
in Figure 1(a). Each part of the frame was modelled by
a rigid body including mass and inertia computed as
steel pipes and connected by ‘bracket’ joints. The frame
and the front fork as well as the frame and the pedal-
board were connected by a revolute joint. Figure 1(b)
illustrates the bicycle model.
Two situations of cycling fall have been studied
according to a hypothetical configuration of real
frequent-accident cases. One configuration consists of a
fall alone after skidding, as depicted in Figure 2(a), and
the second consists of a fall after hitting a curb, as illu-
strated in Figure 2(b). For each configuration a set of
parameters was selected to be changed in the model
such as bicycle linear and angular velocity, torso pos-
ture, hip position, pedals position, handlebars angle
and bicycle incline. For pedalboard, four positions
were selected (0°,90°,180°and 270°) while for handle-
bars only two positions were studied (–30°and 30°)
with the final objective to study some extreme positions
without adding too much to the simulations. The out-
put of the simulations focused on three major results:
head velocity, head orientation and head impact area,
in order to define the conditions of head impact just
before head contact.
Table 1 reports the selected parameters considered
for the parametric study carried out for each configura-
tion with the associated values. The design of the simu-
lation table established for the present parametric study
is a full factorial table with 512 combinations. Hence,
the total number of simulations is 1024.
One of the outputs is the head impact area, so this
point had to be further defined. The head impact point
coordinates are extracted from the simulation and
plotted on the EN960 headform geometry by matching
the height and depth of both head geometries. The
choice of partitioning the cyclist’s head into latitude
and longitude zones was made in order to define easily
and quickly the impact point on the head, extracted
from simulation. Figure 3 illustrates the defined head
regions as follows: around the vertical axis, the head
has been divided into 12 separate regions, with a 30°
interval, leading to six symmetric regions called
Longitude 1 to Longitude 6. In the horizontal plan,
above the reference plan AA#definedintheEN960
the headform has been divided into five
equidistant horizontal sections (Latitude 1 to Latitude
5) and a section located below line AA#with an equiv-
alent width (Latitude 6). The last region, called
Latitude 7, describes the remaining part. The metho-
dology applied for the analysis of the computation
results was conducted in two steps. First, a global
Figure 1. Representation of a real city bicycle (a) and its model under MadymoÒsoftware (b).
Bourdet et al. 283
distribution of the contact area according to the previ-
ous head partition, as well as a mean value of velocity
in terms of resultant, normal and tangential compo-
nents, was evaluated. In a second step, the effect of
each parameter on the impact area and velocity
responses was studied. For this purpose, it was neces-
sary to define a response model associated with the
parametric analysis.
Figure 3. Representation of EN960 headform geometry partition: (a) in latitude and (b) in longitude. The reference line AA#
defined in EN960 is also reported.
Figure 2. Representation of the cyclist model coupled to the bicycle model used for the simulations of fall alone: (a) after
‘skidding’, (b) after ‘hitting a curb’.
Table 1. Display of the selected factors for the parametric study and the associated values.
Factors Bicycle parameters Human parameters
F1 F2 F3 F4 F5 F6 F7 F8
rotation (°)
rotation (°)
velocity (m/s)
velocity (rad/s)
roll (°)
inclination (°)Hip position (mm)
Vert. Horiz.
Level 1 0 –30 5.5 0 0 21 0 0
Level 2 90 30 11.1 3.14 30 37 200 200
Level 3 180
Level 4 270
284 Proc IMechE Part P: J Sports Engineering and Technology 226(3/4)
The response model was a linear combination of fac-
tors and their interactions, as described in equation (1).
In this study, only the effect values of factors are evalu-
ated, and the effect values of interactions are neglected.
where mis the total average of the studied response from
all simulations (Nsimulations), F
are the effect val-
ues corresponding to the selected factors and I
the sum of the effect values from interactions between
several factors with order i,asdepictedinequation(2)
k=1 P
I6=F1F2F3F4F5F6F76 ð2Þ
The effect value of a factor/interaction at a given level
was calculated as the mean value between the responses
when the factor/interaction had the level uand the aver-
age m.
A total of 1024 simulations of cycling falls were carried
out with Madymo software. Figure 4(a) and (b) shows
the distribution of head impact area for both the ‘skid-
ding’ and ‘after hitting a curb’ fall configurations in
terms of latitude and longitude. It can be observed that
in the ‘skidding’ configuration the latitude areas most
often impacted are Latitude 6 and Latitude 5, with
90% of the total impacts. However, for the fall config-
uration after hitting a curb, the area is more spread out,
including Latitudes 4 to 6, with 65% of total impacts.
Considering longitude distribution, both fall config-
urations present a large range of impact areas. 80% of
these impacts are located from Longitude 2 to
Longitude 5 for falls after skidding, with an average of
20 61%, and from Longitude 1 to Longitude 4 for falls
after hitting a curb, with an average of 17 62%.
Tables 2 and 3 report the factors influence on the
corresponding response. The significance of the calcu-
lated influence value of the factors according to the
ANOVA test is also calculated. Hence, the non-
significant values are shaded in the tables. It is clear
that the resultant and tangential velocities are strongly
influenced by the bicycle speed in both configurations.
However, the normal component is also influenced by
the bicycle roll angle and the vertical position of the
hip. This can be explained by the fact that these factors
define the vertical position of the head and, hence, the
head drop height.
The previous analysis can further be divided into
two categories according to the bicycle speed, as
For a cyclist moving with a speed of 5.5 m/s (20 km/h)
and falling alone, as simulated in this study, the resultant
head velocity just before ground contact is, respectively,
6.9 61.2 m/s and 6.4 60.9 m/s in the cases of ‘skidding’
and ‘after hitting a curb’ configurations. Considering the
normal components of the velocities, results are
5.7 61.3 m/s and 5.2 61.0 m/s for ‘skidding’ and ‘after
hitting a curb’, respectively. Coming to the tangential
velocity, both fall configurations give about 3.7 m/s, as
reported in Table 4. In terms of impact angle versus the
normal direction a value of about 35°can be established.
Figure 5 represents the head impact area distribution
extracted from both configurations of falls with a bicycle
speed of 5.5 m/s. The latitude distribution is similar for
both configurations, with a majority of impacts located
at Latitude 5 and Latitude 6 (80% and 65% in the cases
of ‘skidding’ and ‘after hitting a curb’, respectively). On
the other hand, and as for the global distribution, the
impacts among the longitude partitions are spread in the
range from Longitude 1 to Longitude 4.
Considering a bicycle speed of 11.1 m/s (40 km/h),
the resultant head velocity just before ground contact is
11.3 61.1 m/s (6.2 61.0 m/s and 9.4 61.0 m/s for nor-
mal and tangential components, respectively) in the
case of falls after ‘skidding’, and 9.1 62.1 m/s
(4.8 61.3 m/s and 7.7 61.9 m/s for normal and tangen-
tial components, respectively) in the case of falls ‘after
hitting a curb’, as reported in Table 5. It can be
Figure 4. Histogram of the head impact area distribution obtained for the complete set of 1024 simulations in terms of: (a)
latitude; and (b) longitude for both falling configurations.
Bourdet et al. 285
observed that in case of this bicycle speed, the tangential
component is higher than the normal one for both fall
configurations. Indeed, the impact angle is shifted from
35°to 57°. Considering the distribution of head impact
area, it was observed that in the case of falls after
‘skidding’, latitude distribution remains identical to the
configuration with a bicycle speed of 5.5 m/s. On the
other hand, in the case of falls ‘after hitting a curb’, the
distribution becomes more spread out, as shown in
Figure 6.
Figure 5. Histogram of the head impact area distribution in terms of: (a) latitude; and (b) longitude for both falling configurations
with a bicycle speed of 5.5 m/s.
Table 2. Influence of factors on simulated responses in case of falls after ‘skidding’. The non significant influences are in bold.
Factors Bicycle parameters Human parameters
F1 F2 F3 F4 F5 F6 F7 F8
rotation (°)
rotation (°)
velocity (m/s)
velocity (rad/s)
roll (°)
inclination (°)Hip position (mm)
Vert. Horiz.
0% 0% 88% 5% 6% 0% 0% 0%
2% 7% 9% 6% 62% 5% 10% 1%
0% 0% 95% 4% 1% 0% 0% 0%
Latitude 2% 3% 1% 26% 51% 6% 1% 9%
Longitude 1% 2% 0% 74% 19% 0% 1% 3%
Table 3. Influence of factors on simulated responses in case of falls after ‘hitting a curb’. The non significant influences are in bold.
Factors Bicycle parameters Human parameters
F1 F2 F3 F4 F5 F6 F7 F8
rotation (°)
rotation (°)
velocity (m/s)
velocity (rad/s)
roll (°)
inclination (°)Hip position (mm)
Vert. Horiz.
0% 2% 62% 3% 16% 1% 16% 0%
2% 7% 7% 0% 31% 5% 47% 0%
0% 0% 85% 3% 5% 0% 5% 0%
Latitude 1% 6% 31% 1% 47% 0% 12% 1%
Longitude 16% 6% 0% 32% 44% 0% 1% 1%
Table 4. Computed head impact velocities and impact angle for a bicycle speed of 5 .5 m/s.
(m/s) V
(m/s) V
(m/s) Impact angle (°)
Skidding fall 6.9 61.2 5.7 61.3 3.7 60.9 33.5 68.7
Curb hitting 6.4 60.9 5.2 61.0 3.7 60.8 36 67.7
286 Proc IMechE Part P: J Sports Engineering and Technology 226(3/4)
Figure 7 represents the head impact points extracted
from all 1024 simulations and plotted on the EN960
headform geometry. In order to superimpose this result
with the impact area prescribed in the standard, the test
line defined in the EN1078 standard was plotted. It can
be observed that an important proportion of impact
points are located under the helmet test line.
Although cycling falls occur often, it is very difficult to
get real statistics about the head injuries caused to the
victim, especially in the case of accidents without any
antagonist. Due to the large number of possible
scenarios, it is unrealistic to consider all accident types.
In the present study, two realistic and classical accident
scenarios have been considered: one caused after ‘skid-
ding’ and the other ‘after hitting a curb’. Moreover, the
same bicycle and the same human model size were used
for both accident configurations. These constitute a
first limit of this work. Indeed, some further bicycles
with different masses and sizes as well as further heights
and weights for the human body model should be con-
sidered in further research. For the parametric study,
four pedalboard positions were selected, while for han-
dlebars only two positions were studied. This choice
was made in order to study some extreme positions
but also in order to keep a reasonable number of
Figure 7. Representation of the head impact points: (a) for falls after skidding; (b) for falls after hitting a curb.
Figure 6. Histogram of the head impact area distribution in terms of: (a) latitude; and (b) longitude for both falling configurations
with a bicycle speed of 11.1 m/s.
Table 5. Computed head impact velocities and impact angle for a bicycle speed of 11.1 m/s.
(m/s) V
(m/s) V
(m/s) Impact angle (°)
Skidding fall 11.3 61.1 6.2 61.0 9.4 61.0 56.6 65.1
Curb hitting 9.1 62.1 4.8 61.3 7.7 61.9 58 66.5
Bourdet et al. 287
simulations. It should also be mentioned that for the
zero degree handlebars rotation, the time for the bicycle
fall was too long and caused simulation instability
problems. Nevertheless, it should be integrated into
future work. However, the results obtained with a
bicycle model coupled to a human model with the
height and weight of a 50th percentile man, provided
reasonable new knowledge about cycling falls, dis-
cussed further below.
For each fall configuration, seven parameters linked
to the bicycle and human characteristics have been con-
sidered for a parametric study with 512 simulations.
The analysis of impact locations at head level showed
that the impacts are very often (42%) close to the hel-
met rim line (Latitude 6) for both fall configurations;
more precisely, 57% in the case of falls after ‘skidding’,
but more spread out in the case of falls after ‘hitting a
curb’ (28%). These results are consistent with other
studies presented in the literature, such as Ching et al.,
who noted that 49.3% of impact locations on 311 hel-
mets are on the helmet rim. More recently, in 2009,
showed that among a collection of 862 helmets,
the majority of impacts occurred at the border line.
For both fall configurations, the impact in longitude
area is fronto-parietal, with more than 50%. Coming
to the discussion concerning the head speed at the time
just before impact, the results showed that for the nor-
mal components, the obtained values are in the velocity
range recommended by testing standards for the certifi-
cation of helmets (5.42 m/s). Moreover, during head
impact against ground surface, a significant tangential
component of the velocity is observed. Indeed, for a
bicycle speed configuration of 5.5 m/s the head resul-
tant velocity presents a 35°incline versus normal axis,
whereas for a bicycle speed configuration of 11.1 m/s
this angle is about 57°. This tangential component
of the head velocity generates a rotational acceleration
of the head and thus increases the brain injury risk,
as demonstrated by Deck et al.
and Deck and
Therefore, the standard test for bicycle hel-
met should include this tangential component in future
improved test procedures.
The present study focuses on cyclist head impact condi-
tion in the case of an accident without an antagonist,
an aspect poorly reported in the literature. A multi-
body bicycle and cyclist model has been developed and
applied for extensive parametric study (1024 simula-
tions). Two fall conditions have been considered (‘skid-
ding’ and ‘hitting a curb’) at two bicycle initial speeds
(5.5 m/s and 11.1 m/s). Results demonstrate that head
impact points are very often located around and under
the helmet rim, the normal head initial speed is close
to 5.5 m/s and the head velocity presents a significant
tangential component. It is considered that this infor-
mation should be taken into consideration both in test
procedure design and helmet development.
Funding statement
This research received no specific grant from any funding
agency in the public, commercial, or not-for-profit sectors.
This work has been developed within the ANR
PREDIT GO4 project BicyTeˆ te (French Department
of Transport).
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... Validation of helmet finite element models). Second, we modelled typical cyclist impact scenarios (section 2.4 Modelling of the cyclist impact Scenarios) following the previous literature (Bourdet et al., 2012). The simulations were conducted using the previously validated coupled finite element (FE) -multibody (MB) full-scale pedestrian model Yu et al., 2020) . ...
... The cyclist model was created by repositioning the coupled FE-MB pedestrian model using the information about the cyclist riding posture in kerb-impact and skidding impact scenarios obtained from the literature (Bourdet et al., 2012). The repositioning was conducted using the Coupling Assistant module of MADYMO V7.7 MB analysis package (TASS, 2013b). ...
... We analyzed two typical impact scenarios that occur in realworld cyclist accidents: (a) Cyclist falls with the head impacting a kerb (referred to as kerb-impact scenario, Fig. 8a), and (b) Cyclist falls with skidding on the road surface and head impacting the road surface (referred to as ''skidding" impact scenario, Fig. 8b) (Bourdet et al., 2012). Following the literature (Bourdet et al., 2012), the initial conditions were defined by applying the initial velocity of 5.5 m/s in the horizontal direction to the main joint of the bicycle (located at the pedal center), the main joint of the cyclist (located at the hip), and all nodes of the cyclist FE head-neck model. ...
Full-text available
Introduction: Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist's head from injury continues. Method: We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist's head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist's body) of the cycling accidents for cyclists wearing a helmet and without a helmet. Results: The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS0.98) for helmeted cyclists was predicted for both impact scenarios. In contrast, wearing the helmet reduced the CSDM only for the skidding impact scenario. For the kerb-impact scenario, no clear influence of the helmet on the predicted CSDM was observed.
... This was consistent with the findings in a recent numerical study which predicted oblique head-ground impacts with a vertical velocity of 6.3 ± 1.4 m/s and an angle of 65 ± 10° during ES falls induced by a pothole (Posirisuk et al., 2022). The head-ground impact velocities predicted in our study were also consistent with those predicted during bicycle fall simulations (5.2 ± 1.0 m/s and 3.7 ±0.8 m/s for vertical and tangential impact velocities) (Bourdet et al., 2012). The similar head-ground impact velocities between bicycle and ES falls might suggest that the This preprint research paper has not been peer reviewed. ...
... helmet adequate to protect from bicycle falls should also be adequate for ES falls. Finally, the main factor influencing the head-ground vertical and tangential impact speed was the ES traveling speed before the crash, which was also reported in other crash scenarios (Bourdet et al., 2012;Posirisuk et al., 2022;Xu et al., 2016). ...
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Objective: Head injuries are common injuries in E-scooter accidents which have dramatically increased in recent years. The head impact conditions and helmet performance during E-scooter accidents are barely investigated. This study aims to characterize the head-ground impact biomechanics and evaluate bicycle helmet protection in typical E-scooter falls. Method: The finite element (FE) model of a hybrid III dummy riding an E-scooter was developed and validated. The FE model with and without a bicycle helmet was used to reproduce twenty-seven E-scooter falls caused by the collision with a curb, in which different riding speeds (10, 20, and 30 km/h), curb orientations (30, 60, and 90°), and E-scooter orientations (-15, 0, and 15°) were simulated. Head-ground impact velocities and locations were evaluated for the unhelmeted configurations while the helmet performance was evaluated with the reduction of head injury metrics. Results: E-scooter falls always resulted in an oblique head-ground impact, with 78 % on the forehead. The mean vertical and tangential head-ground impact velocities were respectively 5.7 ± 1.5 m/s and 3.7 ± 2.0 m/s. The helmet significantly (p < 0.1) reduced the head linear acceleration, angular velocity, HIC_36, and BrIC, but not the angular acceleration. However, even with the helmet, the head injury metrics were mostly above the thresholds of severe head injuries. Conclusion: Typical E-scooter falls might cause severe head injuries. The bicycle helmet was efficient to reduce head injury metrics but not to prevent severe head injuries. Future helmet standard evaluations should involve higher impact energy and the angular acceleration assessment in oblique impacts.
... Then, the stress compression wave moves to the softer EPS foam, which acts as a cushion by plastically deforming at low loads, therefore limiting the transmitted force to the head [8]. A helmet's safety performance is evaluated through certification standards specific to different countries and regions of the world, and the peak linear acceleration is generally used as a metric to assess helmet effectiveness [9][10][11][12][13][14][15]. The test to evaluate the helmet's impact performance is known as impact attenuation test or shock-absorbing test (depending on the standard) and it is performed in a drop tower containing a headform where the helmet is secured tothat is dropped in a guided free fall to impact against an anvil (flat or curbstone) [15]. ...
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Cork is a natural material that presents excellent properties for applications concerning impact resistance, and its performance can be further enhanced by combining it with other materials into composites. This study tests several combinations of hybrid composites in layered structures involving cork laminates, different types of polymers such as polyvinyl chloride (PVC) and polyurethane (PU) containing shear thickening fluid (STF) in their composition, fabric impregnated with STF, and the fluid in bulk used as an interlaying agent. The aim is to evaluate, through a series of drop impact tests, the robustness of the composites applied in head protection devices used in micro-mobility. During the study, various composite structures were subject to impact tests of 20 and 100 J, deliberately lower and higher energy levels than the 69 J established by the European standard for bicycle helmets – EN1078. The authors directly compared composites of the same thickness and dimensions, determining the influence of the material supplementary to cork in the impact performance. Results are promising for two different types of hybrid composites: a) a solution with encapsulated STF, showing a reduction of up to 8.5% in peak acceleration and smoother deacceleration through higher deformation levels for a given amount of energy absorbed per unit volume; b) a solution with impregnated fabric, which is the lightest and less dense amongst all, displaying very good compromise between density and good impact resistance. These aspects are desirable features for helmets used in micro-mobility.
... The simulations of e-scooter falls showed that in the majority of impacts the rider was thrown after impact, with the head often following a trajectory like a projectile. A similar head trajectory has been found in bicycle-curb impacts (Werner et al., 2001;Bourdet et al., 2012). Hence, we compared the travel distance of the centre of mass of the head with the travel distance of a projectile. ...
E-scooters are the fastest growing mode of micro-mobility with important environmental benefits. However, there are serious concerns about injuries caused by e-scooter accidents. Falls due to poor road surface conditions are a common cause of injury in e-scooter riders, and head injuries are one of the most common and concerning injuries in e-scooter falls. However, the head-ground impact biomechanics in e-scooter falls and its relationship with e-scooter speed and design, road surface conditions and wearing helmets remain poorly understood. To address some of these key questions, we predicted the head-ground impact force and velocity of e-scooter riders in different falls caused by potholes. We used multi-body dynamics approach to model a commercially available e-scooter and simulate 180 falls using human body models. We modelled different pothole sizes to test whether the pothole width and depth influences the onset of falls and head-ground impact velocity and force. We also tested whether the e-scooter travelling speed has an influence on the head-ground impact velocity and force. The simulations were carried out with three human body models to ensure that the results of the study are inclusive of a wide range of rider sizes. For our 10 in. diameter e-scooter wheels, we found a sudden increase in the occurrence of falls when the pothole depth was increased from 3 cm (no falls) to 6 cm (41 falls out of 60 cases). When the falls occurred, we found a head-ground impact force of 13.2 ± 3.4kN, which is larger than skull fracture thresholds. The head-ground impact speed was 6.3 ± 1.4 m/s, which is the same as the impact speed prescribed in bicycle helmet standards. All e-scooter falls resulted in oblique head impacts, with an impact angle of 65 ± 10° (measured from the ground). Decreasing the e-scooter speed reduced the head impact speed. For instance, reducing the e-scooter speed from 30 km/h to 20 km/h led to a 14% reduction in the mean impact speed and 12% reduction in the mean impact force, as predicted by the models. The models also showed that the median male riders were sustaining higher head-ground impact force and speed compared with the small female and large male riders. The findings of this study can assist authorities and e-scooter hiring companies to take more informed actions about road surface conditions and speed limits. These results can also help define representative impact test conditions for assessing the performance of helmets used by e-scooter riders in order to reduce head and brain injuries in e-scooter falls.
... As outlined in these mandatory safety standards, the linear acceleration of the headform should not exceed a certain threshold (i.e., 300 g outlined in CPSC, 1998, Snell B95 Cheung et al., 2004, and ASTM F1447 Chang, 2003, as well as 250 g outlined in AS /NZS 2512.1, 2009, and Sandberg et al., 2018, EN, 1078, 1997. However, cyclists often fall off their bicycles and impact their heads at angles that are not always direct and usually varies between 30°and 60° (Bourdet et al., 2012;Bourdet et al., 2014). These impacts not only can cause linear acceleration but can also result in rotational acceleration due to the tangential forces to the head (McIntosh et al., 2013;Willinger et al., 2019). ...
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Cycling accidents are the leading cause of sports-related head injuries in the US. Conventional bicycle helmets typically consist of polycarbonate shell over Expanded Polystyrene (EPS) foam and are tested with drop tests to evaluate a helmet’s ability to reduce head kinematics. Within the last decade, novel helmet technologies have been proposed to mitigate brain injuries during bicycle accidents, which necessitates the evaluation of their effectiveness in impact testing as compared to conventional helmets. In this paper, we reviewed the literature to collect and analyze the kinematic data of drop test experiments carried out on helmets with different technologies. In order to provide a fair comparison across different types of tests, we clustered the datasets with respect to their normal impact velocities, impact angular momentum, and the type of neck apparatus. When we analyzed the data based on impact velocity and angular momentum clusters, we found that the bicycle helmets that used rotation damping based technology, namely MIPS, had significantly lower peak rotational acceleration (PRA) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) as compared to the conventional EPS liner helmets (p < 0.01). SPIN helmets had a superior performance in PRA compared to conventional helmets (p < 0.05) in the impact angular momentum clustered group, but not in the impact-velocity clustered comparisons. We also analyzed other recently developed helmets that primarily use collapsible structures in their liners, such as WaveCel and Koroyd. In both of the impact velocity and angular momentum groups, helmets based on the WaveCel technology had significantly lower peak linear acceleration (PLA), PRA, and GAMBIT at low impact velocities as compared to the conventional helmets, respectively (p < 0.05). The protective gear with the airbag technology, namely Hövding, also performed significantly better compared to the conventional helmets in the analyzed kinematic-based injury metrics (p < 0.001), possibly due to its advantage in helmet size and stiffness. We also observed that the differences in the kinematic datasets strongly depend on the type of neck apparatus. Our findings highlight the importance and benefits of developing new technologies and impact testing standards for bicycle helmet designs for better prevention of traumatic brain injury (TBI).
... 19 Additionally, impacting helmets at an oblique anvil is a better representation of real-world bike helmet impacts than impacting on a flat surface. 4,6,7,20 . A novel study by Bland et al. conducted in-lab reconstructions of damaged bike helmets using CT scanning to quantify helmet damage and an oblique test rig to replicate realworld impacts. ...
The best way to prevent severe head injury when cycling is to wear a bike helmet. To reduce the rate of head injury in cycling, knowing the nature of real-world head impacts is crucial. Reverse engineering real-world bike helmet impacts in a laboratory setting is an alternative to measuring head impacts directly. This study aims to quantify bike helmet damage using computed tomography (CT) and reconstruct real-world damage with a custom, oblique test rig to recreate real-world impacts. Damaged helmets were borrowed from a helmet manufacturer who runs a helmet warranty program. Each helmet was CT-scanned and the damage metrics were quantified. Helmets of the same model and size were used for in-lab reconstructions of the damaged helmets where normal velocity, tangential velocity, peak linear acceleration (PLA) and peak rotational velocity (PRV) could be measured. The damage metrics of the in-lab dropped helmets were quantified using the same CT scanning process. For each case, a multiple linear regression (MLR) equation was created to define a relationship between the quantified damage metrics of the in-lab tested helmets and the associated measured impact velocities and kinematics. These equations were used to predict the impact kinematics and velocities from the corresponding real-world damaged helmet based on the damage metrics from the original damaged helmet. Average normal velocity (3.5 m/s), tangential velocity (2.5 m/s), PLA (108.0 g), PRV (15.7 rad/s) were calculated based on a sample of 23 helmets. Within these head impact cases, five notes reported a concussion. The difference between the average PLA and PRV for concussive cases versus other impacts were not significantly different, although the average impact kinematics for the concussive cases (PLA = 111.4 g, PRV = 18.5 rad/s) were slightly higher than the remaining cases (PLA = 107.1 g, PRV = 15.0 rad/s). The concussive cases were not indicative of high magnitude impact kinematics.
Inconsistent results have been reported for helmeted impact responses between the two most commonly used headforms: The National Operating Committee on Standards for Athletic Equipment (NOCSAE) and the Hybrid III (HIII). There is a need to understand the reasons for the different responses of the headforms so that helmet protection may be discerned independent of the headform. In this study, the kinematic response and brain injury measures of the NOCSAE and HIII headforms at three impact orientations with three helmet models on an inclined anvil were compared. The results showed that the peak linear acceleration from the two headforms were within 6.3% on average for all impacts. However, despite the higher moment of inertia of the HIII headform, it did not have a consistently lower rotational acceleration compared to the NOCSAE headform. The differing headform rotational responses were primarily due to differences in the headforms’ center of gravity location. This led to differences in couples and accelerations, which tended to be most severe in frontal impact orientations. The variation in rotational responses of the headforms seems to be also dependent on the helmet type, with helmet A having greater variation compared to helmets B and C. Differences in the rotational kinematics of the two headforms led to a 47% average difference in their brain injury measures.
In the United States, all bicycle helmets must comply with the standard created by the Consumer Product Safety Commission (CPSC). In this standard, bike helmets are only required to by tested above an established test line. Unregulated helmet performance below the test line could pose an increased risk of head injury to riders. This study quantified the impact locations of damaged bike helmets from real-world accidents and tested the most commonly impacted locations under CPSC bike helmet testing protocol. Ninety-five real-world impact locations were quantified. The most common impact locations were side-middle (31.6%), rear boss-rim (13.7%), front boss-rim (9.5%), front boss-middle (9.5%), and rear boss-middle (9.5%). The side-middle, rear boss-rim, and front boss (front boss-middle and front boss-rim regions combined) were used for testing. Two of the most commonly impacted regions were below the test line (front boss-rim and rear boss-rim). Twelve purchased helmet models were tested under CPSC protocol at each location for a total of 36 impacts. An ANOVA test showed that impact location had a strong influence on the variance of peak linear acceleration (PLA) ( p = 0.002). A Tukey HSD post hoc test determined that PLA at the side-middle (214.9 ± 20.8 g) and front boss (228.0 ± 39.6 g) locations were significantly higher than the PLA at the rear boss-rim (191.5 ± 24.2 g) location. The highest recorded PLA (318.8 g) was at the front boss-rim region. This was the only test that exceeded the 300 g threshold. This study presented a method for quantifying real-world impact locations of damaged bike helmets. Higher variance in helmet performance was found at the regions on or below the test line than at the region above the test line.
Finite element simulation was used to analyse the response of an elastomeric pre-buckled honeycomb structure under impact loading, to establish its suitability for use in helmet liners. A finite element-based optimisation was performed using a search algorithm based on a radial basis function. This approach identified optimisation configurations of a pre-buckled honeycomb structure, based on structural bounds subject to impact loading conditions. Furthermore, the influence of objective function, peak acceleration and head injury criterion was analysed with respect to the resultant mechanical behaviour of the structure. Numerical results demonstrate that this class of structure can exceed the performance threshold of a common helmet design standard and minimise the resultant injury index. Experimental testing, facilitated through laser sintering of thermoplastic polyurethane powder, validated the output of the numerical optimisation. When subject to initial impact loading, the fabricated samples satisfied their objective functions. Successive impact loading was performed to assess the performance and degradation. Samples optimised for peak acceleration demonstrated superior performance after stabilisation, relative to their initial response. The culmination of this study establishes a numerical design pathway for future optimisation of candidate structures for head impact protection. Furthermore, the optimised pre-buckled honeycomb structure represents a new class of energy absorbing structure, which can exceed the thresholds prescribed by the design standard.
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The vehicle traffic accidents have been widely studied in different countries, but the difference of nature of traffic accidents in different countries was not adequately investigated for set suitable protective strategy in different area. This study aimed to identify the occurrence, type and mechanisms of the traumatic injuries of the vulnerable road users (VRUs) in vehicle collisions in China and Germany. The accident data (in the years 2000 to 2005) were collected from traffic police and hospital in Changsha, China as well as from GIDAS database documented in Medical University Hannover, respectively. An in-depth study was carried out based on the collected data by using approaches of statistics analysis and virtual reconstructions. The results from analysis of Chinese data were compared with results from analysis of German data. The injury severities were determined using AIS code and ISS values. The results were presented in terms of cause of injuries, injury distributions, injury patterns, injury severity. The VRUs accidents were identified as vital issue in urban traffic safety and therefore a high priority should be given to this road user group in research of safe urban transportation. It was discussed with regard to accident data collection, accident sampling and injury distributions, the factors influenced the injury outcomes etc. The data sources reflects the real situations of vulnerable road users in traffic accidents in Changsha and Hannover and may not in the whole countries of China and Germany. This study will contribute to the determination of different nature of vehicle traffic accidens between motorized and motorizing areas, which will form a firm background for making safety counter-measures.
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Objectives and methods - Data on 1462 injured bicyclists aged 1-19, obtained over a period of five years from the British Columbia Children's Hospital as part of a national emergency room based program in Canada, were analyzed to describe the epidemiology of injuries, helmet use, and the occurrence of head injuries before the enactment of a new mandatory helmet law. The odds ratio (OR) and 95% confidence interval (CI) were calculated for non-users compared with helmet users. Results - Bicycle injuries comprised 4% of all injuries seen in the five year study period. The proportion of admissions was 12.7% among bicyclists, significantly higher than the 7.9% admissions of all 35 323 non-bicyclist children who were seen during the study period (OR = 1.96, CI = 1.44 to 1.99). Boys were injured more often than girls. The proportion of admissions for boys was 13.8% compared with 10.2% among girls (OR = 1.41, CI = 0.97 to 2.05). More than 70% of injured bicyclists reported no helmet use. The proportion of admissions of injured bicyclists who did not use helmets was always higher than the proportion of admissions of those who used helmets (OR = 2.23, CI = 1.39 to 3.62). Head and face injuries occurred more often among those who did not use helmets (OR = 1.55, CI= 1.18 to 2.04 ). However, there was no excess of minor head injuries among non-users (OR = 1.10, CI = 0.60 to 2.06). Of the 62 concussions, 57 occurred to non-helmet users (OR = 4.04, CI = 1.55 to 11.47). Most injuries occurred in the upper (46.4%) or lower extremities (32.4%). Dental injuries occurred slightly more often among helmet users compared with non-users but this excess was not statistically significant (OR = 1.29, CI = 0.76 to 2.20). Conclusion - The data indicate the need to control injuries by using helmets. A decrease in the number of head injuries and their severity is expected when bicycle helmet use becomes law in British Columbia.
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This paper presents the methodology used to improve knowledge about vulnerable road users accidents and more specifically pedestrians or cyclists. This work is based on a complete analysis of real accidents from three different approaches: in-depth accident investigation, numerical simulation with multibody model and experimental reconstitution with PMHS subject. Accidents chosen from an in-depth multidisciplinary investigation are numerical modelled using a multibody software. Then, a parametric study focused for instance on car velocity and victim position at impact is performed in order to find the best correlations with all indications produced by the in-depth analysis. Finally, the retained configuration close to the presumed real accident conditions is reproduced experimentally by crash-test using cadavers. All results are finally compared in order to validate the real accident reconstruction. This methodology is applied on two real accidents involving one pedestrian or one cyclist.
Our previous report concerned an analysis of bicyclists struck laterally by cars, which is the most fatal type of accident. The current study compares the behavior of bicyclists struck frontally and from the rear by cars. Comparatively few bicyclist fatalities occur in frontal crash accidents. The comparison is based on accident analysis and MADYMO simulations using a humanlike model. Bicyclist impact behavior was made clear on the basis of computer simulations. It was found that the initial impact between a bicyclist's knee and the vehicle front structure is very important in reducing the impact velocity of the bicyclist's head.
To study circumstances of bicycle accidents and nature of injuries sustained and to determine effect of safety helmets on pattern of injuries. Prospective study of patients with cycle related injuries. Accident and emergency department of teaching hospital. 1040 patients with complete data presenting to the department in one year with cycle related injuries, of whom 114 had worn cycle helmets when accident occurred. Type of accident and nature and distribution of injuries among patients with and without safety helmets. There were no significant differences between the two groups with respect to type of accident or nature and distribution of injuries other than those to the head. Head injury was sustained by 4/114 (4%) of helmet wearers compared with 100/928 (11%) of non-wearers (P = 0.023). Significantly more children wore helmets (50/309 (16%)) than did adults (64/731 (9%)) (P < 0.001). The incidence of head injuries sustained in accidents involving motor vehicles (52/288 (18%)) was significantly higher than in those not involving motor vehicles (52/754 (7%)) (chi 2 = 28.9, P < 0.0001). Multiple logistic regression analysis of probability of sustaining a head injury showed that only two variables were significant: helmet use and involvement of a motor vehicle. Mutually adjusted odds ratios showed a risk factor of 2.95 (95% confidence interval 1.95 to 4.47, P < 0.0001) for accidents involving a motor vehicle and a protective factor of 3.25 (1.17 to 9.06, P = 0.024) for wearing a helmet. The findings suggest an increased risk of sustaining head injury in a bicycle accident when a motor vehicle is involved and confirm protective effect of helmet wearing for any bicycle accident.
The objectives of this study were to describe bicycle-related injuries in relation to injury patterns, age, gender and medical treatment in a defined Swedish population and to identify factors contributing to injury. The study group comprised all patients living in the county of Västmanland, Sweden, visiting a physician or dentist because of bicycle-related injury during one year (November 1989-October 1990). Cyclists were mostly injured on pavements, pedestrian malls and cycle tracks. Twenty percent of the events occurred on public roads in urban areas; most frequently, the injured were in the age range 0-24. The most common bicycle injury event involved no other party. The events were often caused by environmental factors, in combination with behaviour such as excessive speed, lack of attention, breach of traffic regulations or a co-ordination problem. Head injuries, including oral injuries, were the most common, in particular among children and adolescents. One in four children in the age range 0-9 sustained an oral injury.
The objective was to evaluate the relationship between helmet damage and head injuries in helmeted bicyclists in a sub-study of a large case-control study of bicycle injuries and helmet effectiveness. The setting consisted of seven hospital emergency departments in Seattle, WA. Hospitalized patients and medical examiners cases were included. The participants in the study were helmeted bicyclists who suffered a head injury or who damaged or hit their helmet in a crash. The Snell Memorial Foundation laboratory evaluated the helmets, blinded to crash circumstance and injury diagnosis. Damage was scored on a five-point scale (0 = none to 4 = destroyed). The damage location for each helmet was coded into regions (six longitudinal and three latitudinal) and mapped onto a three-dimensional CAD (computer-aided design) model of a helmet. The same procedure was also followed for injury location, which was mapped onto a three-dimensional ISO (International Organization for Standardization) headform for visualization of head-injury distribution. 785 helmeted subjects met the criteria for inclusion in the sub-study, and 527 helmets were purchased and evaluated (67%). 316 (60%) of the helmets had no or minimal damage, and 209 (39.7%) had significant damage (score 2, 3 or 4). Helmet types were 49.7% hard shell, 34.2% thin shell and 16.1% no shell. The risk of head and brain injury increased if the helmet was destroyed: OR = 5.3 (95% CI 2.9, 9.9) and OR = 11.2 (95% CI 3.5, 37.9), respectively. A high proportion of helmet impacts were along the front edge of the helmet, with a preponderance of head injuries in the same region. The large number of impacts to the front rim of the helmet, combined with the substantial number of riders with injuries to the forehead, indicate that some helmets, because of poor fit or wearing style, expose the forehead to injury. In addition, the data indicate that for a small proportion of injuries, the energy to the helmet may exceed design limits.
Bicyclist and pedestrian injuries in collisions with vehicles in Japan were investigated based on national and in-depth accident data analyses and mathematical simulations. In an impact with a bonnet-type vehicle, a bicyclist slides over the bonnet of the vehicle, behavior that is not observed for pedestrians. As a result, the bicyclist's head tends to strike a bonnet-type vehicle at a more rearward location in comparison with pedestrians. The first contact position of a bicycle with a vehicle, the vehicle front-end geometry and the bicycle velocity affect whether the bicyclist's head strikes the vehicle or not. Due to the bent-knee posture of a bicyclist's legs, the types of leg injuries sustained by bicyclists and their causes differ from those seen for pedestrians. Component test procedures have been proposed for evaluating pedestrian safety, but some modifications of the head impact area and angle are necessary when applying these methods to bicyclists.