MSMechanical SciencesMSMech. Sci.2191-916XCopernicus PublicationsGöttingen, Germany10.5194/ms-10-1-2019Design and hardware selection for a bicycle simulatorDesign and hardware selection for a bicycle simulatorDialynasGeorgiosg.dialynas@tudelft.nlHappeeRienderSchwabArend L.Biomechanical Engineering, Delft University of technology, Mekelweg 2, 2628 CD Delft, the NetherlandsGeorgios Dialynas (g.dialynas@tudelft.nl)7January201910111024September201829November201823December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://ms.copernicus.org/articles/10/1/2019/ms-10-1-2019.htmlThe full text article is available as a PDF file from https://ms.copernicus.org/articles/10/1/2019/ms-10-1-2019.pdf
With the resurgence in bicycle ridership in the last decade and the
continuous increase of electric bicycles in the streets a better
understanding of bicycle rider behaviour is imperative to improve bicycle
safety. Unfortunately, these studies are dangerous for the rider, given that
the bicycle is a laterally unstable vehicle and most of the time in need for
rider balance control. Moreover, the bicycle rider is very vulnerable and not
easily protected against impact injuries. A bicycle simulator, on which the
rider can balance and manoeuvre a bicycle within a simulated environment and
interact with other simulated road users, would solve most of these issues.
In this paper, we present a description of a recently build bicycle simulator
at TU Delft, were mechanical and mechatronics aspects are discussed in
detail.
Introduction
A number of recent studies have used recorded data of riders during
naturalistic driving and . However,
behavioral studies for other vehicles often use simulators
as they afford more reproducible experiments over a range of riders in a safe
environment. There have been a number of groups that have developed bicycle
simulators for a variety of research goals. designed and
built a haptic steering interface for the control input of a bicycle
simulator, a potentiometer was used to estimate the angular position of the
handlebar and not an angular encoder, whereas the output feedback torque
magnitude of the selected motor was insufficient for this application.
and describe the design of a simulator
mounted on a Stewart platform with steering and pedaling subsystems, which
was used to study rider-bicycle models, but use steer angular position
measurements to estimate angular accelerations and consequently the input
torques via the product of angular accelerations and shaft inertia. However,
the estimation of input torques from inertia and angular acceleration data
contains dynamic errors .
, ,
focus on rider behavior at a cognitive level and do not incorporate a
realistic vehicle model. At TU Delft we have designed and built a fixed-base
bicycle simulator with haptic feedback at the handlebars, which can be used
for various applications, for example studying rider behavior in various
infrastructures, studying rider interaction in traffic and performing rider
training. The bicycle simulator includes a haptic steering device which
generates feedback driven by an underlying bicycle computer model, an
incremental encoder to measure handlebar angle and a torque sensor to measure
handlebar applied torque. In this paper we present a step by step guide to
build such a bicycle simulator. First, the design requirements of the
simulator interfaces are examined. Then, the design of the mechanical
structure is described. Afterwards, the hardware components selection and
calibration procedure is analyzed. The article ends with a discussion and
conclusion section presenting other factors that make such a design valid for
rider behaviour studies.
Design requirements of bicycle simulator interfaces
The aim of this section is to examine all the necessary requirements needed
to build a realistic bicycle simulator. This is achieved by understanding the
role of each sensory system on rider control. The primary sensory systems
used during the riding process are, the vestibular sensory system, the visual
sensory system and the proprioceptive sensory system
. Secondary sensory systems such as the tactile
and the auditory sensory system also contribute to the perception of
information during the riding process. For example, found
that additional auditory information and tactile vibratory information
improved the humans' estimation of speed. In the first paragraph, of this
section we describe the relation between the sensory systems and bicycle
states. The second paragraph describes, the necessary requirements needed to
be fulfilled in order to activate these sensory systems and build the
simulator interfaces. We presume that the bicycle rider system is a closed
loop control system, balancing the mostly unstable bicycle and manoeuvring in
the environment using feedback from the vestibular sensory system, the visual
sensory system and the proprioceptive sensory system
. The rider uses bicycle roll angle ϕ as
part of the feedback control loops for the vestibular/visual sensory systems
and handlebar steer angle δ as part of the feedback control loop for
the proprioceptive sensory system. The rider processes each state
individually in order to apply a steer torque Tδ to control the
bicycle. A block diagram of the bicycle-rider system illustrating the
relation between the primary sensory systems and bicycle states
[ϕ,δ] the rider uses to control the bicycle by applying a steer
torque Tδ is presented in Fig. .
Block diagram of the bicycle-rider system illustrating the relation
between the primary sensory systems and bicycle states [ϕ,δ] the
rider uses to control the bicycle by applying a steer torque Tδ.
Due to the aforementioned it is necessary to design and build user interfaces
able to activate at least the primary sensory systems of the rider. To
activate the visual sensory system a virtual environment is built using
Unity® software. Projection of the virtual
environment is achieved with a PC screen or with a head-mounted virtual
reality display. To stimulate the proprioceptive sensory system a haptic
steering device is designed and built. The steering device is able to
generate torque feedback based on the equations of motion of a three degree
of freedom bicycle model, the so-called Carvallo-Whipple bicycle model
. The absence of a hexapod in the current implementation
of the bike simulator does not allow the user to experience physical roll and
thus, the vestibular sensory system remains inactive. Although, in
naturalistic bicycle riding the rider uses both the vestibular and visual
sensory systems to estimate roll angle. We think that visual roll of the
horizon in the virtual environment might be an effective tool to compensate
the vestibular loss. However, it should be noticed that the absence of
vestibular input might have a negative effect on rider behavior in certain
tasks, such as braking and lateral trajectory control. For this reason, the
usage of such a simulator to study these tasks may be inappropriate
. The implementation of the equations of motions of the
Carvallo-Whipple bicycle model used to drive the haptic steering device and
Unity® environment will be detailed in
future publications.
TU Delft fixed base bicycle simulator.
Description of the mechanical structure
Several structural design considerations should be taken into account in case
of building a bicycle simulator. Structural strength and required geometry
are some of the most important aspects of the building process. The bicycle
frame must be able to support the load of the rider during all operational
conditions while having adjustable dimensions. Adjustable reach and stack
dimensions are considered important mainly because it is suspected that body
posture also influences the amount of applied torque. As stated in previous
bicycle experiments conducted by the interaction of muscle
length with muscle lever moment arm length is one of the factors which will
dictate the amount of force or torque that can be produced by the rider
during cycling. It should be noted that this statement does not describe the
relationship between muscle length, muscle activity, and torque of the
brachialis muscles however, it shows the influence of body posture to applied
torque. On the other hand, the bicycle frame must be able to support all the
functional subsystems used in bicycles. For instance, the rear wheel and
derailleur, the seatpost and seat, the bottom bracket and pedal subsystem
etc. In addition to the above, the simulator must be able to simulate the
steering forces acting at the bicycle during the riding process. In the first
section of this chapter we describe the design and building process of the
main structure of the bicycle frame, next we present the design of the haptic
steering device and overload protection mechanism.
The mechanical portion of the simulator consists of three main structural
parts. A bicycle roller training base (600×400 mm), a square tube
(40×40×1000 mm) used as a steering column, and a rear half
of a step-through bicycle frame (54 cm), see Fig. . To mount
all the structural parts together the following modification are made. The
front roller of the base is removed and a rectangular tube (40×20×500 mm) is welded as a replacement. In addition, six metal foot pegs
(40×20×500 mm) two at the front, middle and rear are also
mounted. The foot pegs are mainly used to increase the vertical distance of
the base in respect to ground and also to distribute the load equally to
specific areas of the frame. At the steering column a (25×500 mm)
tube is welded at a 25∘ angle and at a 40 mm distance from the end
of the square tube. At the bicycle frame the headtube is removed two custom
made clamps (AL 7075) are mounted to the upper and lower tubes respectively.
The upper and lower clamps are connected with two metal straps one per each
side. The combination of the upper frame clamp design together with the (25×500 mm) welded tube of the steering column create the first
prismatic joint of the assembly, see Fig. a. This prismatic
joint is used to mount the steering column to the bicycle frame. Steer and
saddle height can be adjusted over a large range, and the steering assembly
can be moved horizontally to accommodate a large range of body sizes and bike
geometries.
Connection of the bicycle frame to the roller base using prismatic
joints and mechanical arms.
To mount the steering column and bicycle frame to the roller base a
combination of different types of adjustable blocks are used (blocks are
provided from RK Rose Krieger). A hinge clamp block and a t-shape block
(Gwv 40). First, the hinge clamp block is mounted to the column and tilted
to create a bicycle headtube angle of 72∘, since this is a common
angle also adopted in the Carvallo-Whipple bicycle model
. Afterwards, the hinge block is mounted to the t-shape
block which is next mounted to the racks of the base with two addition square
tubes (40×40×380 mm) and two custom made c-shape clamps.
Because the upper joint of the column is prismatic a second prismatic joint
is also constructed at the base level. To construct the second joint an
additional t-shape block is mounted to the base oppose to the first one. A
square tube (40×40 mm) is mounted to the first clamp and sliding
freely through the square hole of the second one, see Fig. b.
These two prismatic joints together with the c shape clamps are used to
adjust the reach dimensions of the bicycle frame. The mounting of the bicycle
frame from the front end is now completed. To mount the bicycle frame from
the rear end to the base two mechanical arms and a trapezoidal shape
structure are combined. The mechanical arms are constructed from L-shape
stripes and are used to mount the bike from the rear wheel axis to the base.
The trapezoidal structure is constructed from aluminium tubes and a
combination of hinge clamps (Gp 25, Kvr 25, W 25). This structure is used
to mount the bicycle frame from the seatpost to the base, see
Fig. c.
Description of haptic steering device
To allow the rider to interact with a virtual environment and receive
realistic handlebar torque feedback from the simulation model a haptic
steering device is required. The device must be able to generate realistic
torque feedback in order to enhance rider control and prevent excessive
rotation of the handlebars. The importance of haptic steering feedback on
rider control is stated in previous bicycle experiments conducted by
. In this subsection we describe the components used to build
such a device.
The haptic steering device consists of two sub-assemblies. The steering shaft
assembly and the column mount assembly. The steering shaft assembly includes
the components used to build the steering shaft, whereas the steering column
assembly includes the components used to mount the steering shaft to the
column. The steering shaft assembly consists mainly of eight components (not
including the handlebar assembly and adaptors). Five of these components are
mechanical and three of them are electromechanical. More specific, an
overload protection mechanism is used for safety, a steer range limiter is
used to restrict the rotational range to ±35∘, a shaft is used to
intersect with the actuators of the limit switches (limit switches are shown
in Fig. ) and turn of the electric motor when the maximum
rotational range is reached. Two pillow block bearings are used to mount the
telescopic shaft to the column. An electric motor is used to generate
steering feedback, an incremental encoder is used to measure the steering
angle and a torque sensor is used to measure the applied handlebar torque.
The steer shaft assembly is mounted to the column with 3 additional custom
made clamps, the electric motor clamp and two bearing clamps. An extra clamp
is used to mount the reading head of the encoder. Material used for the shaft
components is AL7075 excluding the telescopic shaft which is made from Steel
304. In Fig. the steering shaft and column assembly is
presented.
Steering column and shaft assembly exported from solidworks 2016.
Description of overload protection mechanism
Different methods can be used to protect a rotating shaft from torsional
stress. On a software level torque and rotational range limits can be set in
the parameter programming of the motor drive. However, in case of a sensor
malfunction the software might be unable to recognize the torsional overload
condition. For this reason, a mechanical solution is recommended as a second
measure of protection. There are typically two mechanical mechanisms used to
protect a rotation shaft, a torque limiter and a shear pin. From the above
two mechanisms the usage of a torque limiter is not recommended for this
application mainly because there is no clear indication of the operational
speed the rated torques are measured at, as first noticed by
. Most of the available torque limiters list the rated
torques but with no indication of the operating speed the torques are
measured at. It turns out they are with respect to an 1800 rpm operating
speed. The absence of this vital information together with the low steer
rates make the selection of a limiter inappropriate, since it might lead to
further adjustments and modifications to make the limiter operate properly.
For this reason, a custom shear pin mechanism is designed and mounted inline
with the steering shaft. The shear pin mechanism functions are to protect the
steering shaft and the user by mechanically disengaging the feedback motor
from the handlebars when the maximum torsional strength is reached. For the
geometric design of the shaft-hub mechanism and selection of the proper pin
size the following equations are used. For the shaft-hub combination the
desired geometric relationship between the two diameters is D1=1.5D, where
D1 is the hub, and D the shaft diameter, respectively. The diameter of
the shear pin d, is calculated based on the shear strength τ, of the
material, the service factor κ, the maximum breaking torque T, and
the hub diameter D, as seen in Eq. () conforming to the
requirements of ISO 8730-40 standards.
τ=κ4Tπd2D
The shear strength of the selected pin is also tested in practice. The pin
shears between 25–26 Nm which is 30 % lower from the steering shaft
overload condition. The selected shear limit is considered adequate according
to the aforementioned ISO standards.
Sensor and motor selection
There are three sensors and one actuator motor in the existing bicycle
simulator. Two of the three sensors are located at the steering assembly.
More specifically, an angular encoder, a torque sensor and an electric motor
are mounted inline with the steering shaft, see Fig. ,
whereas a gearwheel encoder is mounted at the rear roller of the powertrain
assembly as seen at Fig. .
Steering shaft assembly.
To select the proper motor and sensors a number of technical specifications
need to be determined in advance. For the encoders, the type and resolution,
for the torque sensor, the range and resolution, and for the electric motor
the maximum and continuous torque. In this section we describe the procedure
followed to determine these requirements. In the first two paragraphs, the
encoders and torque sensor requirements are determined, whereas in the last
paragraph the electric motor requirements.
Two types of encoders are found in literature, incremental and absolute.
Incremental rotary encoders output the pulse corresponding to the rotation
angle only while rotating, and the counting measurement method that adds up
the pulse from the measurement beginning point. On the other hand, absolute
rotary encoders output the signal of position corresponding to the rotation
angle by coded elements. The incremental encoder does not output an absolute
position and for this reason typically the internal structure is simpler and
the cost lower. For both the steer angle and wheel speed measurements
incremental encoders are selected. The resolution of the steer encoder in
counts per turn (cpt), is determined based on the smallest steer angle
increment Δδ, as seen at equation below :
N=360∘4Δδ
A steer encoder with 152 000 (cpt), and an additional index channel for
accurate homing was selected (RLM2HDA001). For the wheel speed encoder the
resolution is selected based on the resolution range of encoders used for
similar systems, such as the anti-lock braking systems of motorcycles. A
gearwheel encoder with 192 (cpt), is selected (reading head is from rls type
is gts35, gearwheel from Ktm). The gearwheel and reading head are mounted
directly to the rear roller and base respectively as seen at
Fig. .
Gearwheel sensor mounted at base roller.
For the selection of the torque sensor the steer torque profile must be
determined, not only for normal bicycle riding but also during perturbation
tests. Measuring the steer torque profile can be achieved with modern torque
sensors although the problem of crosstalk disturbance must be taken into
account. Crosstalk occurs due to the large forces and moments applied to the
fork and handlebars by the ground and the rider during bicycling. These
forces and moments corrupt the relatively small torque measurements as they
can be hundreds of times larger in magnitude. Few published studies attempt
to estimate or directly measure steer torque.
instrumented a bicycle which could measure pedal, handlebar, and hub forces
to characterize the in-plane structural loads during downhill mountain
biking. The handlebar forces acting forward and parallel to the ground were
used to estimate the steering torque. A maximum torque of about 7 Nm is
shown in this study although crosstalk disturbance was not taken into
account. attempted to estimate the torques
acting on the front frame based on orientation, rate and acceleration data
taken while riding a bicycle with no-hands. They estimated a steer torque
under ±2.5 Nm. attached a torque wrench to a
bicycle and made left and right turns at speeds from 0–13 m s-1. The
torques were found to be under 5 Nm except for the 13 m s-1 trial
which read about 20 Nm. However, the torque wrench calibration range
(0–84 Nm) was too large for the obtained torque measurements reducing the
accuracy of his results. attached a steer motor and
controller to the handlebars and tried to estimate steer torque from the
motor current and handlebar moment of inertia. They are one of the few
studies that takes into account some of the inertial effects of the
handlebar. designed and fitted a custom torque
sensor in the bicycle steer tube. They tried to remove crosstalk effects by
applying an axial load on the sensor but they did not account for the dynamic
inertial affects of the components above and below the sensor. Their measured
steer torques during cornering were under ±2.4 Nm. From the above
mention studies, none succeed to measure the actual applied rider torque
since very few accounted crosstalk disturbance and even fewer the dynamic
inertial effects of the components above or below the sensor.
was the first who developed an experimental bicycle that
can accurately extract rider applied torque. In his design a torque sensor
(Futek 150, TFF350, ±15 Nm) is mounted inline with the handlebar and
fork using a double universal joint isolating the handlebar and fork loads
during bicycling. The instrumented bicycle he developed was used to measure
steer torque responses during lateral force perturbation experiments
. A rider torque range between 0–10 Nm, is
realized in these experiments. For that reason, a torque sensor with a range
of ±25 Nm, and a resolution of 4 µNm, is selected (Kistler
9349A).
Estimated haptic steer feedback torque based on angular acceleration
output δ¨, of the benchmark bicycle model for a steer torque
input of 5 Nm.
For the selection of the haptic steer feedback motor the Carvallo-Whipple
bicycle model developed by is used to predict the
maximum torque required within the stable and unstable speed. To obtain an
estimation of the feedback motor torque a steer torque impulse of 5 Nm is
given as an input to the model. The selected input torque is based on
previous bicycle experiments conducted from and is
considered the maximum steering torque measured for controlling a bicycle in
normal maneuvers. To calculate the output feedback torque the steer angular
acceleration δ¨, and the moment of inertia I, of the steer
axis of the bicycle simulator is used, see Eq. (). The
steer angular acceleration δ¨, is given as an output of the
Carvallo-Whipple bicycle model, whereas the steer shaft moment of inertia
I, of the bicycle simulator is measured experimentally.
T=Iδ¨(t)
As shown in Fig. a maximum torque of almost 5.4 Nm is
noticed at the unstable speed region, whereas at the stable speed region a
torque of maximum 0.02 Nm is found. Combining a reduction gearhead with an
electric motor to reduce its physical size and increase torque output is not
optimal for the existing design. Backlash of the gearhead can distort torque
sensor measurements and for this reason is excluded from the existing
steering design. For this reason, an electric motor of 1410 W and drive
combination able to deliver a stall torque of approximately 4 Nm, and a
max.torque 11.5 Nm are selected for the steering actuation (Kollmorgen
AKM42G and AKDP00606).
Block diagram of motor and motor drive configuration.
Ampere/torque analogy.
Calibration and testing of hardware components
To ensure that the selected components fulfill their specifications every
component is tested and calibrated. It is important that all of the sensors
and actuator behave in a consistent and predictable manner. For example,
motor performance before and after tuning is compared not only with its own
feedback but also with the torque sensor output. This way performance
mismatch is analyzed in an early development stage and is avoided by either
re-calibrating specific sensors or by re-tuning the motor drive control
parameters.
To calibrate the torque sensor a table wrench, a digital torque wrench, an
amplifier (Kistler 5030A2) and an oscilloscope are used. The torque sensor is
mounted from one end to a table wrench and from the other end to a digital
torque wrench. Next, the torque sensor is connected to an amplifier and an
oscilloscope. For different torque magnitudes and amplification ranges the
voltage output is measured. Amplification range 1 can measure torque
magnitudes up to ±25 Nm, whereas range 2 only up to ±2.5 Nm.
Expected rider torque is assumed within 0–10 Nm range and thus amplifier
range 1 is selected as a configuration for the bicycle simulator.
Motor performance before and after tuning.
Experimental set-up to measure moment of inertia.
An analog signal is used to supply a reference command torque to the haptic
feedback motor. There are three command modes that the motor drive can be set
with the analog mode, position control, velocity control and torque control.
Position and velocity control are typically used when precise tasks are
required, for instance a welding task. On the other hand, torque control is
used when compliant control is needed. Compliant here means that the rider is
able to rotate the shaft at any angle required while receiving torque
feedback from the motor. Since compliance is required the operation mode is
set to analog torque mode.
The analog torque control loop of the motor drive unit can be seen at
Fig. , where (Vr) is the reference voltage, (ir) is the
current reference, (ic) is the output current of the motor controller and
(if) the feedback current of the electric motor. To convert the input
reference voltage (Vr) to an input current reference (ir) a scaling constant
must be set in Amp V-1. This scaling constant is set based on the peak
current of the drive and the maximum voltage range of the controller. For a
peak motor current of 18 Amp and a maximum input voltage of 12 V the
constant is set to 1.5 Amp V-1.
After the current scaling constant is set, the torque constant is determined
by measuring the motor response for a given input command. For an input
voltage command of 1.06 (Vr), which corresponds to a current of 1.6 (ir) a
torque of 1.75 Nm is produced as an output from the motor, see
Fig. . A linear torque/current relationship of
1.09 Nm Amp-1 is set in the software for controlling the motor in
torque mode. A maximum motor torque output of 10.92 Nm can be reached for
this specific motor-drive configuration.
Once the motor is connected to the steering shaft the system is tested for
given control tasks. A sinusoidal input signal with a frequency of 2 Hz and
an amplitude of 1.6 Amp equivalent to 1.75 Nm is given as an input to
capture the response of the system. The performance of the motor is tested
before and after tuning. The methodology used to estimate the shaft inertia
and tune the motor drive is described at next subsection. As it can be seen
at Fig. the torque commanded to the motor matches to the
output feedback torque when the motor is tuned. On the other hand,
overshooting and phase shift occurs when the motor is not properly tuned.
Estimation of steering shaft moment of inertia
The damping and inertia properties of the steering shaft must be estimated in
case the torque that the rider applies is estimated from acceleration data
and also in case position and velocity modes are used to control the motor
and auto tuning can not be used for safety reasons. To estimate the moment of
inertia different methods can be used. A first approach could be to extract
the moment of inertia from the CAD model. A second approach could be to use
system identification. A third approach could be to add two springs and
calculate the moment of inertia from the oscillation period and the equation
of motion of the system. The first method can not be used for the existing
CAD model. In the CAD model of the steering shaft there are parts exported
from suppliers which are represented as solid entities and are not modeled
correctly. The assembly parts of these entities could not be separated and
the moment of inertia of these components can not be calculated separately.
For instance, the motor shaft moment of inertia can not be measured as a
separate body of the motor. On the other hand, system identification can be
used to estimate the moment of inertia of the steering shaft, however a more
straightforward approach is the spring method. For the above reasons, the
last approach is used to estimate the moment of inertia of the steering
shaft. To oscillate the steering shaft two extension springs with a rate of
k=317 N m-1 and a length of 0.05 m are added perpendicular to the
handlebars as shown in Fig. .
Oscillation of steering shaft using spring method.
The springs are pretensioned proportionally and set to idle around
0∘. The springs are excited and the oscillation period is measured
through the steering encoder. The oscillatory motion of the shaft can be seen
in Fig. . The equation of motion of the steering shaft after
attaching the springs is:
Iδ¨(t)+bδ˙(t)+2ka2δ(t)=0
The steering shaft equation of motion consists of the inertia I, viscous
friction b, and spring stiffness k, and moment arm a. δ denotes
the desired angle of the system, δ˙ and δ¨ the
desired angular velocity and acceleration respectively. Friction caused
between bearings and electric motor components is neglected, thus b is
considered zero. Thus the mass moment of inertia equals:
I=2ka2ω2
The mass moments of inertia of the steering shaft assembly using the spring
method and auto-tuner of the motor drive are I=0.0828 kg m2 and
I=0.0912 kg m2 respectively. The comparison of the two methods is used
to validate the spring method. The inertia difference between the two methods
might be due to the fact that viscous friction b, is neglected in the
spring method, whereas this is not the case when auto-tuning is used.
Discussions and conclusions
The objective of this study was to realize all the necessary steps needed to
design and build a realistic bicycle simulator. The simulator prototype is
the result of multiple design choices and constraints. Constraints, primarily
time-wise, have resulted in a system wherein a mountain bike is placed on top
of rollers and later fitted with a haptic steering device. There is still
space for improvement regarding the mechanical structure and haptic steering
device. For example, the prismatic joints of the mechanical structure can be
equipped with linear bearings to allow friction-less motion when adjusting
the stack and reach dimensions. The steering shaft assembly can be machined
as a single part and from a light material in order to reduce both inertia
weight and misalignment.
All the data used in this manuscript can be obtained by
requesting from the corresponding author. The supplementary data related to
this article is available online at 10.5281/zenodo.2525685
().
GD designed and build the existing simulator and selected
and tested the performance of the hardware components under the supervison of
ALS and RH. All authors discussed the results and contributed to the final
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to thank Oliver Lee for designing the printed circuit boards
and giving rigorous feedback regarding the structure of this paper. We
gratefully acknowledge the European Commission for their support and the
Marie Curie Initial Training Network (ITN) project Nr. 608092 “MOTORIST”
(Motorcycle Rider Integrated Safety; http://www.motorist-ptw.eu, last
access: 31 January 2018). Edited by: Marek
Wojtyra Reviewed by: two anonymous referees
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