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The Use and Usage of Virtual Reality
Technologies in Planning and
Implementing New Workstations
René REINHARD a,b,1, Peter MÅRDBERG c, Francisco GARCÍA RIVERA d, Tobias
FORSBERG e, Anton BERCE f, Mingji FANG f and Dan HÖGBERG d
a Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM,
67663 Kaiserslautern, Germany
b University of Kaiserslautern, Center for Cognitive Science, 67663 Kaiserslautern,
Germany
c Fraunhofer-Chalmers Centre, 412 88 Gothenburg, Sweden
d University of Skövde, School of Engineering Science, 541 28 Skövde, Sweden
e Industrial Path Solutions AB, 412 58 Gothenburg, Sweden
f China-Europe Vehicle Technology CEVT, 417 55 Gothenburg, Sweden
Abstract. Virtual reality (VR) technologi es can support the planning and implemen-
tation of new workstations in various industry sectors, including in automotive as-
sembly. Starting in the early planning stages, VR can help in identifying potential
problems of new design ideas, e.g. through ergonomics analyses. Designers can then
quickly change the virtual representations of new workstations to test solutions for
the emerging difficulties. For this purpose, the actions and motions of prospective
workers can be captured while they perform the work tasks in VR. The information
can also be used as input for digital human modelling (DHM) tools, to instruct bio-
mechanical human models. The DHM tools can then construct families of manikins
that differ on anthropometric characteristics, like height, to simulate work processes.
This paper addresses both existing technologies for gathering data on human actions
and motions during VR usage and ways in which these data can be used to assist in
designing new workstations. Here, a novel approach to translate a VR user’s actions
into instructions for DHM tools through an event-based instruction sampling
method is presented. Further, the challenges for utilizing VR are discussed through
an industrial use case of the manual assembly of flexible cables in an automotive
context.
Keywords. Digital Human Modelling, Virtual Reality, Motion Tracking, Ergonom-
ics, Assembly Path Generation, Automated Manikins, Flexible Cables, Automotive
Assembly
1. Introduction
Virtual reality (VR) technologies find increasing application in many industrial settings,
including automotive assembly, construction, or energy technologies [1]. This does not
only include the assistance of product design through virtual prototypes [2] and VR-
1 Corresponding Author, Email: rene.reinhard@itwm.fraunhofer.de.
DHM2020
L. Hanson et al. (Eds.)
© 2020 The authors and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
doi:10.3233/ATDE200047
388
based personnel training [3], but also the planning, evaluation, and optimization of as-
sembly processes [4, 5]. For these purposes, VR technologies allow for the interaction
with digital prototypes and with virtual representations of planned workstations even
during the early planning stages [6]. Industrial case studies indicate the usefulness of VR
assisted design of manufacturing workstations compared to design work with only a
desktop computer set-up [7]. Thus, in the planning and design of workstations, VR can
be used to generate feedback from relevant groups quickly and early in the process, lead-
ing to more rapid iterations without the need for costly physical prototypes.
On one hand, experts in subjects like ergonomics can experience and interact with
the proposed workstations. Here, the virtual setups can further be used to visualize the
results from path planning and digital human modelling (DHM) tools. Thus, the experts
can assess the results of assembly simulations and the predicted human motions in an
intuitive manner and help evaluate the feasibility, accessibility, and visibility during the
installation of digital prototypes, and provide impressions for ergonomic analyses. The
gained insights can then be used to adjust the digital work site, to further refine the as-
sembly path and to add to or alter the constraints for the prediction of human motion in
DHM tools.
On the other hand, members of the prospective workforce can perform the planned
actions in VR while their motions are being tracked [8]. This allows for ergonomics
evaluations that take individual characteristics of the workers, their specific restraints
and abilities into account. But it also allows for the extraction of data from their tracked
movements, which can in turn be used as input for DHM tools.
In this paper, we review currently available VR and tracking technology options and
explore their usage in planning new and evaluating existing workstations. In this context,
we present a novel event-based instruction sampling approach, in which the actions of
VR users are tracked while they are interacting with the digital workstation. This is then
used to create a simulation of the manufacturing task, where a digital manikin is in-
structed based on the actions the VR user performed. This makes it possible to evaluate
ergonomics and to repeat the simulation using manikins with different anthropometrics
than the original VR user. Moreover, this approach reduces the time needed to setup a
DHM simulation and offers a more intuitive approach to construct simulations for non-
expert users.
2. Capturing human motions and actions in VR
In order for a VR user to experience a virtual environment and interact with the virtual
objects therein, both visualization and tracking technologies are required. In an industrial
context, the most commonly utilized VR visualization approaches are projection-based
systems and head-mounted displays (HMD) [1, 9]. Projection VR systems include single
or multiple projector-based powerwalls, as well as surrounding, walk-in setups, based on
multiple projection screens (e.g. Cave Automatic Virtual Environment (CAVE) or
CAVE-like systems). However, the current paper focuses on HMD solutions, i.e. display
devices which are affixed to the VR user’s head and typically include one or two displays
as the image source, as well as collimating optics between the eyes and the display.
These systems also typically include on-board inertial measurement units (IMUs) to
track rotational movements of the head which are then translated into corresponding ori-
entation changes in the virtual environment [10]. This can further be combined with
methods that track the position of the HMD to allow for full 6-DoF movement. This
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies 389
positional tracking usually uses accelerometer dead reckoning as its basis, which is com-
bined with various additional tracking methods to correct the inertial measurement drift
[11]. These methods may make use of external hardware, e.g. an external camera that
detects infrared signals send from the HMD [12], or lighthouse tracking, where external
base stations with stationary LED arrays and active spinning laser emitters send out LED
flashes followed by laser sweeps, that are registered by photodiodes on the headset [11,
12]. The positional tracking can also be based on inside-out tracking methods, where
cameras on the device estimate the motions of the camera itself relative to the environ-
ment they model based on the recorded input [13].
Besides head tracking, modern VR devices often integrate motion tracked handheld
controllers, that are tracked and visualized in 3D space and also allow for abstract inputs
via button presses [14]. Hereby, they also track the approximate spatial position of the
hand holding the controller. Newer VR controller concepts also expand these capabilities
with, as of yet, limited finger tracking based on sensors in the controller [15]. This al-
ready allows for the usage of some natural motions to interact with virtual objects, like
grasping a virtual object by gripping the controller. Some motions, like a pinch grip, can,
however, be hindered by the geometry of the controller in the user’s hand. Finger track-
ing can be further extended by other hand and finger tracking technologies, like data
gloves or optical tracking, which can translate more natural hand and finger motions into
the virtual world. Some data gloves can also expand the feeling of touching virtual ob-
jects through tactile feedback, such as actuators on the glove that touch the hand across
its surface, or through force feedback, i.e. mechanical forces applied to the finger tips to
provide a resistance consistent with touching the object [16].
Lastly, body movements of workers at workstations can be captured. This can be
relevant for a range of questions, focusing on topics from posture for ergonomics evalu-
ations, upper body movements for capturing workers performing a task, to gait analyses
for logistics analyses on a factory floor. The motion capture can be achieved by a wide
variety of approaches: optoelectronic measurements, image processing systems, ultra-
sonic localization systems, and electromagnetic- or IMU-based systems [17]. Different
motion capture systems may be more or less appropriate for certain use cases. As an
example, optoelectronic measurements, i.e. active or passive marker-based tracking with
usually fixed cameras, offer the most accurate tracking, but can be negatively impacted
by obstructions to the line-of-sight or large distances from the cameras [17]. By contrast,
IMU systems do not need additional external apparatus, are useful in a mobile context,
and are capable of capturing highly dynamic motions, but also need additional infor-
mation, e.g. from human rigid-body models, to actually offer positional data [17].
In industrial use cases, more elaborate tracking options have been established in the
assessment of ergonomics. For example, Daria et al. [18] combined an IMU-based mo-
tion capture system connected to Siemens Jack with ErgoLog to perform ergonomics
evaluations for workstation simulations. Similarly, Caputo et al. [19] also used an IMU-
based motion capture system with Siemens Process Simulate to track posture, which,
together with risk screening methods, was used as the basis for ergonomics evaluations.
But the tracked movements of VR users can also be useful for other use cases, e.g.
VR-based training. Also, the usage of tracked motions as inputs for DHM tools should
be mentioned. For example, Peruzzini et al. [20] used a Vicon optical motion capture
system for posture tracking with a Delmia V5-6 for workstation digitalization. Here, they
used Catia manikin digitalization and Haption RTI Delmia to connect the VR users’ real
movements to the virtual manikin’s movements. Similarly, Garcia et al. [21] used IPS
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies390
VR with IPS IMMA manikins and Smart Textiles to track user movement. These appli-
cations in ergonomics evaluations and manikin instruction will be the focus of the fol-
lowing sections.
2.1. VR assisted ergonomics evaluations
The cost of work-related musculoskeletal disorders is considerable both for companies
and for the afflicted workers [22]. This includes both direct costs, such as healthcare [23],
and indirect losses, e.g. through reduced quality in the production [24]. Such disorders
are also psychologically taxing for those who suffer them [25]. The combination of mo-
tion capture and VR can help in developing new methods to address these problems.
As the health risks are closely related to the posture of the workers while they per-
form their jobs, corrective approaches can target posture either via active or passive
measures. In active corrections, the operators are informed that their posture is poten-
tially harmful [26], while in passive correction, the workstation’s design is improved to
facilitate better posture [27]. In order to design workstations that minimize the workers’
health risks, standardized ergonomics evaluation methods such as rapid upper limb as-
sessment (RULA), rapid entire body assessment (REBA), or the Ovako working posture
analysis system (OWAS) are being utilized [28]. To apply these standards, experts have
to either simulate the workers’ movements at the workstation using DHM tools, or to run
tests by observing real life motions. Traditionally, this design process makes use of 2D
screens and physical prototypes. Both approaches have advantages and disadvantages.
DHM tools can be used to economically test design ideas even during early phases
of the process and thus spot and address potential problems early on, since they allow
for rapid design changes to the workstation, to the performed task and to the anthropo-
metric characteristics of the simulated workers. Yet, the DHMs need to be instructed,
which requires expertise, time, and effort to come to representative results. Tests with
physical prototypes make direct and easy observations of workers performing the tasks
possible, but are also more costly and make changes to the workstation design more
complicated. Physical tests will therefore often be used later in the design process.
By including VR in the design process, the designer can conduct studies of ergo-
nomics earlier, without costly physical prototypes, and with the possibility of rapid
changes. For this purpose, VR has often been combined with motion capturing technol-
ogies [18, 19]. The virtual environment also offers a high degree of control over the
situation, including over factors like lighting and noise. However, the use of VR can also
have drawbacks. The heavy emphasis on the sense of touch during assembly processes
and the expectation of a physical resistance when interacting with virtual structures can
often not be adequately simulated. Further, some people may feel less present in the
virtual environment, or may even react adversely to VR usage, by developing motion
sickness-like symptoms [29]. These individual reactions to VR can in turn impact task
performance [29]. Consequently, the face validity of motions tracked in VR may not be
as clearly established as it is for real life tests on prototypes. Still, VR can be especially
useful for early tests of design ideas and to support the creation of simulations in DHM
tools. In order for VR to assist in working with DHM tools, the information about the
VR user’s actions and motions have to be made usable within the tools. In the following,
a new event-based instruction sampling approach is presented, to show how this can be
achieved and what requirements it entails.
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies 391
2.2. VR assisted ergonomics evaluations in DHM tools: An event-based instruction
sampling approach
For VR to be of assistance in working with DHM tools, the relevant information from
the VR session has to first be recorded and then translated into a form that can be utilized
in later simulations. Here, we present a method that approaches this issue through the
analysis of a VR user’s actions at the virtual workstation, which results in instructions
for a DHM tool that uses IPS IMMA [30]. This approach has several requirements, both
for the used VR technology and for the DHM tool.
On the side of the adopted VR technology, information about the VR user’s move-
ments and interactions in the virtual space have to be captured. This can be realized even
with the minimal tracking equipment provided by most current HMD-based VR options,
namely the HMD itself and standard handheld VR controllers. While additional tracking
information, e.g. from finger or full body tracking, could be gainfully employed, the fol-
lowing will not presuppose access to any additional VR or tracking technology beyond
this typical setup, i.e. an HMD that is capable of both rotational and positional tracking
and motion tracked VR controllers. On the side of the DHM tool, two functionalities are
fundamental to this new method:
x An automated manikin that can interpret and automatically perform instructions
with ergonomically sound postures and motions.
x An instruction language that can be mapped against the events in the VR session
and that can be interpreted by digital manikins and other objects in the scene.
2.2.1. Automated manikins
A manikin in a DHM system can be said to be automated, if it is able to automatically
perform an assembly operation. Thus, if instructed, it will perform a task automatically,
without any additional help from the user of the DHM tool. Moreover, the task needs to
be performed with ergonomically sound postures and motions which, for instance, need
to consider the balance and weight of the manikin’s body parts and of any carried objects
[31, 32]. The simulation should also consider external forces and torques, while ensuring
that the postures and motions are collision free with respect to both the manikin’s body
parts and the objects in the environment [31, 32].
2.2.2. Instruction language
The instruction language should not be limited to only manikins, since the manikin may
interact with other objects in the simulation. All objects used in the simulation can be
seen as actors that are performing a set of instructions. Such actors may include geome-
tries, manikins, or mechanical structures, with each actor having its own set of instruc-
tions to execute. Thus, simulations with both manikins and other objects in the simulation
can be created from the same instruction language [33, 34].
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies392
a)
b)
Figure 1. Example of events in the IPS instruction language. a) Interactions between the manikin and an object
in the scene with which a task is performed. b) Examples of possible actions for a manikin and object.
The set of instructions that the actors may perform during a simulation should de-
pend on the current state of the actor and on the objects in the simulation. For instance,
if the manikin grasps an object with the right hand, then it is not possible for the manikin
to grasp another object with the same hand unless the first object has been released [33,
34]. An example of such instructions in the IPS software is illustrated in Figure 1.
Moreover, each instruction must have a corresponding action event in the simulation.
For instance, a grasp instruction may only be used if there is an object that is available
for the manikin to take a hold of. Hence, properties of an object, such as grip, view, and
attach points, also define the set of possible instructions for the actors.
Depending on the state of the manikin and on the objects in the environment, work
tasks are translated to a sequence of low-level instructions in a controller structure [35].
By following the instruction sequence, a DHM tool like IPS automatically generates col-
lision-free and ergonomically sound manikin motions that accomplish the assembly tasks
[35].
2.2.3. Instruction of DHM manikins through event sampling actions in VR
To instruct the manikin, the presented approach uses a sampling procedure that considers
all the events that occur while the VR user manipulates virtual objects with the control-
lers. Each manipulation corresponds to at least one sampled event. Such events may for
example include the interaction when the user takes the virtual object, followed by the
motion of the object while the VR user holds and moves it to another location. During
the VR session, this interaction would correspond to the user pressing a button on the
controller to hold the object, to moving it with the motion tracked controllers, and then
to releasing the button to let go of the object.
In the presented method, the interaction events in the VR session are translated to
the instruction language by mapping them to a fixed set of manikin actions. As an exam-
ple, when the user is taking and placing an object, this is translated into corresponding
actions of the instruction language, such as grasp and release. The IMMA manikin uses
grip, view, and attach points to interact with objects in the environment. Predefined grip
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies 393
points are automatically created when a VR controller is used to manipulate an object.
Currently, a new predefined grip is created where the object is grasped as soon as the VR
user takes hold of it.
Moreover, the movements of all objects that the VR user manipulates are also doc-
umented in IPS. As an example, during the VR session, the motions of a tracked control-
ler are translated onto a gripped object. The resulting motions are logged and then move-
ment trajectories are created accordingly. These trajectories can then be included in the
simulation and correspond to a follow instruction in the instructional language. Since all
events and trajectories are time stamped, it is possible to construct an instruction se-
quence for the manikin and all other objects used in the VR session. By following the
resulting instruction sequence, a digital simulation of the VR session is created, where
the user is represented by the manikin.
2.2.4. Discussion of an event-based instruction sampling approach
In the presented event-based instruction sampling approach, it is possible to capture the
actions of VR users to quickly create simulations in a DHM tool, thereby lowering the
expertise requirements of using these software options while allowing for their effective
usage. In the resulting simulations, it is possible to change the anthropometric character-
istics of the manikin and then repeat the simulation for different workers. IPS IMMA
contains a built-in functionality to simulate an entire manikin family [36, 37]. Thus, this
approach offers a straight forward and cost effective path to ergonomics evaluations of
new workstation designs that consider workforce diversity through limited motion cap-
ture efforts in VR.
Still, there remain challenges to this approach. When a user picks up an object, a
grip point is created. The currently automatically chosen grip type is similar to the way
that one grasps the handheld controller, but this might not reflect the VR user’s intentions.
While grip types can be adjusted later in the DHM tool, this is an additional time demand.
One approach to overcome this issue could be to let the user select a grip from a list of
predefined grip types which is then automatically aligned, adjusted, and attached to the
object. This would reduce the time that is later spend on adjusting the grips in the DHM
software, but it also constitutes an action besides naturally interacting with the objects in
VR and necessitates a certain degree of expertise to select the correct grip type. Another
approach would be to use information from finger tracking technologies to automatically
select a fitting grip type. With classical VR controllers, even with the newer models that
try to estimate finger positions based on sensors on the controller, this can be a problem.
Currently, these controllers can e.g. let VR users naturally grasp a spherical object in the
palm of their hand, which corresponds to gripping the controller, but other grip types are
not as intuitively translated into VR. In these cases, other finger tracking methods, like
data gloves [16], could be useful. However, these options are more costly and come with
higher initial time demands for equipping and calibrating the devices. New optical track-
ing options based on cameras in the HMD could also be useful, but they require a clear
line-of-sight to the hand while performing the task, which may not always be possible.
The presented implementation could also be extended by motion tracking for body
movements to include complex postural data and actions like squatting or kneeling in the
instruction sequence. Future implementations will also focus on capturing interactions
with complex objects, including collaborative robots and flexible cables.
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies394
Figure 2. An IPS VR user fastening a flexible cable with clips during an assembly task. Courtesy of CEVT.
2.3. Challenges for using VR in planning and implementing workstations exemplified
by a use case from the automotive industry
The manual assembly of flexible cables is a common task in the automotive industry
today. A corresponding industrial use case from the China Euro Vehicle Technology AB
(CEVT) is illustrated in Figure 2. In this use case, the worker should assemble a cable
from the floor to the roof of a car along its b-pillar. Here, the cables were fastened into
particular places with clips, which are specifically designed for these circumstances. To
assemble the cable, it needed to be unfolded, routed, and fastened in a certain order. All
of this had to occur while the internal torques and forces of the cable, as well as the forces
of the clips needed to be considered. An assembly of this type may also be performed in
narrow regions and it may lead to uncomfortable postures for the assembly worker.
As shown in Figure 2, the assembly can be performed by a VR user who guides a
manikin to assemble each of the clips along the pillar. The cable can be realistically
fastened to the car by stepwise changing the boundary conditions of the cable during the
simulation, i.e. by adding constrains to the clips on the cable. This example showcases
many of the challenges that VR can encounter when it is applied to complex industrial
use cases. To work with the clips, the manikin was instructed to utilize a pinch grip,
which did not correspond to the VR users grip on the controller. Further, the assembly
occurred at the b-pillar of a car and the VR users would have both expected its resistance
when affixing the clips and may have wished to lean on the structure during the assembly.
While VR users can be shown a visual impression of their avatar leaning on a virtual
object, they themselves are not provided with its physical support. Even current haptic
feedback options cannot accurately let the VR users touch and interact with such virtual
structures, especially for demanding actions like bodily leaning against them. In addition,
the work task required physically correct behavior from the flexible cable in real time at
a high frame rate, which, while possible in IPS’s VR implementation, can become per-
formance intensive. While some of these challenges can be conquered with new advances
in DHM and VR software, as well as with tracking-related hardware, complex bodily
interactions with virtual structures can likely only be approached by the introduction of
real life elements, like a b-pillar replica in a mixed-reality setup.
R. Reinhard et al. / The Use and Usage of Virtual Reality Technologies 395
3. Conclusion
VR offers many potential benefits for the planning, design, and implementation of new
workstations. It can allow for ergonomic assessments even early in the planning phases
and, through new methods like the presented event-based instruction sampling approach,
VR can also support the work in DHM-related software. The usage of VR does, however,
also come with challenges that should be considered. VR can especially be of help in
rooting out potential problems early in the design process, while during later stages real
life prototype tests and ergonomics assessments may still have clear advantages in some
complex use cases.
Acknowledgements
This work has been made possible with support from the Swedish Governmental Agency
for Innovation Systems (VINNOVA) within the VIVA and SUMMIT projects, and the
Knowledge Foundation within the Synergy Virtual Ergonomics (SVE) project, and the
Eurostar project ED-VIMA (E!113330) supported by VINNOVA under the grant num-
ber (2019-03534), as well as by the participating organizations. It is also part of the Sus-
tainable Production Initiative and the Production Area of Advance at Chalmers Univer-
sity of Technology. This support is gratefully acknowledged.
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