ETSI GR ARF 002 V1.1.1 (2019-07)

Augmented Reality Framework (ARF) Industrial use cases for AR applications and services

DGR/ARF-002

General Information

• Status: Published
• Publication Date: 25-Jul-2019
• Technical Committee: ARF - Augmented Reality Framework
• Current Stage: 12 - Completion
• Due Date: 02-Aug-2019
• Completion Date: 26-Jul-2019

Standards Content (Sample)

GROUP REPORT
Augmented Reality Framework (ARF)
Industrial use cases for AR applications and services
Disclaimer
The present document has been produced and approved by the Augmented Reality Framework (ARF) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ARF 002 V1.1.1 (2019-07)

Reference
DGR/ARF-002
Keywords
augmented reality, interaction, interoperability,
teleservice
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Contents
Intellectual Property Rights . 5
Foreword. . 5
Modal verbs terminology . 5
Executive summary . 5
Introduction . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definition of terms, symbols and abbreviations . 7
3.1 Terms . 7
3.2 Symbols . 7
3.3 Abbreviations . 7
4 Overview of industrial Use Case analysis . 8
5 Usage conditions . 10
5.1 Overview . 10
5.2 Usage environments . 10
5.3 Operating conditions . 11
5.4 Augmentation data sources. 11
6 Detailed description of the 4 dominant Use Cases . 11
7 Typical industrial Use Cases . 15
7.1 Overview . 15
7.2 Use Case 1: Wireless network and IoT installation . 15
7.2.1 Context . 15
7.2.2 Scenario . 16
7.2.2.1 Objectives. 16
7.2.2.2 Building the map of the environment to be equipped . 16
7.2.2.3 Checking the radio coverage . 16
7.2.2.4 Placing connected objects in the environment . 17
7.2.2.5 Searching and selecting objects . 17
7.2.3 Advantages . 18
7.2.4 Needed characteristics . 18
7.3 Use Case 2: Service Remote Support . 18
7.3.1 Introduction. 18
7.3.2 Supporting functionalities in an AR environment . 18
7.4 Use case 3: Training . 19
7.4.1 Context . 19
7.4.2 Application scenario . 20
7.4.3 System requirements . 20
7.4.3.1 Overview . 20
7.4.3.2 Requirements analyses . 21
7.4.3.3 Spreading Contamination . 22
7.4.3.4 Radiation Field . 22
7.4.3.5 Decontamination process . 22
7.4.4 Conclusions. 23
7.5 Use Case 4: Manufacturing . 23
7.5.1 Context . 23
7.5.2 Challenges. 23
7.5.3 Application scenario . 24
7.5.4 System requirements . 24
7.5.5 Benefits of AR usage . 26
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8 Conclusions . 27
Annex A: Augmented Reality (AR) in the Industry survey . 28
Annex B: Review of presentations at ISG ARF workshops in Berlin and Paris . 38
Annex C: Authors & contributors . 42
History . 43

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Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The information
pertaining to these essential IPRs, if any, is publicly available for ETSI members and non-members, and can be found
in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to ETSI in
respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the ETSI Web
server (https://ipr.etsi.org/).
Pursuant to the ETSI IPR Policy, no investigation, including IPR searches, has been carried out by ETSI. No guarantee
can be given as to the existence of other IPRs not referenced in ETSI SR 000 314 (or the updates on the ETSI Web
server) which are, or may be, or may become, essential to the present document.
Trademarks
The present document may include trademarks and/or tradenames which are asserted and/or registered by their owners.
ETSI claims no ownership of these except for any which are indicated as being the property of ETSI, and conveys no
right to use or reproduce any trademark and/or tradename. Mention of those trademarks in the present document does
not constitute an endorsement by ETSI of products, services or organizations associated with those trademarks.
Foreword.
This Group Report (GR) has been produced by ETSI Industry Specification Group Augmented Reality Framework (ISG
ARF).
The ISG ARF shares the following understanding for Augmented Reality: Augmented Reality (AR) is the ability to mix
in real-time spatially-registered digital content with the real world. The present document describes the most relevant
use cases identified via a survey conducted with the help of an online questionnaire.
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Executive summary
The present document summarizes the results of a questionnaire issued by the Industry Specification Group Augmented
Reality Framework (ISG ARF) on industrial use cases and reviews of two workshops held by the ISG ARF, where a
number of use cases were presented. These results are presented in categories such as:
• most relevant use cases;
• challenges of AR;
• scale of operation;
• accuracy for the positioning of augmentation;
• data sources for augmentation data;
• data security;
• data sharing;
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• mode of operation;
• environmental conditions; etc.
Based on this analysis it is possible to identify the most relevant parameters and operational conditions for Augmented
Reality in the industry and thus elaborate a requirements document for industrial use cases.
Introduction
The Industry Specification Group Augmented Reality Framework (ISG ARF) has been established to synchronize
efforts and identify key use cases and scenarios for developing an Augmented Reality (AR) framework with relevant
components and interfaces and to provide technical requirements for AR specifications in order to ensure interoperable
implementations that will benefit both technology providers and end-users. The first step of the work of the ISG ARF
was to collect the most relevant use cases in the industrial sector and to identify the required operational conditions for
these use cases.
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1 Scope
The present document presents and classifies industrial use cases for AR applications and services. It forms the basis for
the requirements document to be drafted ETSI GS ARF 004 [i.2].
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GR ARF 001: "Augmented Reality Framework (ARF); AR standards landscape".
[i.2] ETSI GS ARF 004: "Augmented Reality Framework (ARF) Interoperability Requirements for AR
components, systems and services".
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the following terms apply:
digital twin: virtual representation of a physical product or process, used to understand and predict the physical
counterpart's performance characteristics
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
2D 2-dimensional
3D 3-dimensional
AG Aktien Gesellschaft
AR Augmented Reality
ARF Augmented Reality Framework
ATEX ATmosphères EXplosibles
CAD Computer Aided Design
CBRN Chemical, Biological, Radiological and Nuclear
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DCC Digital Content Creation
FoV Field of View
HAZMAT HAZardous MATerials
HHI Fraunhofer Heinrich Hertz Institute
HMD Head Mounted Display
IEM Fraunhofer-Institut für Entwurfstechnik Mechatronik
IFF Fraunhofer-Institut für Fabrikbetrieb und -automatisierung
IoT Internet of Things
ISG Industry Specification Group
IT Information Technology
LIST Luxembourg Institute of Science and Technology
OS Operating System
QR Quick Response
SLAM Simultaneous Localization And Mapping
TM Trade Mark
TV Television
WIFI™ Wireless Ethernet
4 Overview of industrial Use Case analysis
The following overview is the result of a survey based on an online questionnaire (see Annex A) carried out in the
th st
period between February 28 and May 1 2018 and a review of contributions to the two ARF workshops in Berlin and
Paris (see Annex B).
Altogether 77 persons from 16 countries responded to the questionnaire. The distribution of countries was as follows:
• 43 % from Germany
• 24 % from France
• 5 % from Canada
• 5 % from USA
• 5 % from Spain
• 5 % from Italy
• 13 % from other countries
Most responses came from the general "Technology" sector (21 %) followed by Academic Research (18 %) and IT
(15 %). Other distinct sectors were automotive (6 %), basic industries (6 %), consumer services (4,5 %), education
(4,5 %). The remainder (around 25 %) of the responses was distributed over other sectors (e.g. energy, finances,
aerospace, telecommunication, media). Among the occupations of the participants "Research" clearly dominate (32 %),
followed by "Architecture and Engineering" (17 %), "Computer and Mathematical" (12 %) and "Management" (12 %).
57 % of the participants defined themselves as "Technology Providers" and 35 % as "Technology Users". The expected
benefits of AR technologies showed a rather homogenous distribution over the different areas, with a slightly higher
ranking of "Better Training Methods" (50 %).
The following benefits of AR technologies are expected:
• Better training methods (50 %)
• Increasing sales (31 %)
• Better productivity (31 %)
• Better quality of products (27 %)
• Better traceability of operations (23 %)
• Better security for workers (19 %)
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• Other (31 %)
Other specifically mentioned benefits were:
• better information delivery;
• visualization of sensor data through digital twins and IoT sensors;
• new research possibilities;
• remote diagnostics;
• help in diagnostics;
• process acceleration;
• higher level of information for decision making;
• easier documentation;
• faster reaction for remote assistance;
• buying decision information;
• faster and better service;
which however in most cases could also be assigned to the categories listed above. Answers illustrate the holistic nature
of AR.
The participants were also asked, what their level of maturity with respect to AR usage is. The answers showed a rather
homogeneous distribution with values between 9 % and 18 % over the seven categories ranging from "we never heard
from AR" to "we already deployed an operational solution". Almost 60 % already work on AR solutions, either by
conducting some pilot studies (14 %), proof-of-concept studies (17 %), deploying operational solutions (9 %) or already
running operational solutions (18 %).
The most relevant Use Cases are shown in Figure 1.

Figure 1: Most relevant Use Cases mentioned in the questionnaire
Surprisingly, sales & marketing does not hit the top three use-cases among the participants of the survey and logistics,
worker safety and factory layout planning were not identified among the priorities. This may be attributed to the limited
number of answers to the questionnaire and imbalanced profiles of the participants over all business sectors.
TM
As far as operating systems for AR devices are concerned, Android is the dominant OS (43 %) followed by
TM TM
(25 %) and iOS (23 %).
Windows
The main challenges identified in the questionnaire are summarized in Figure 2.
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Figure 2: Today's challenges for AR
Tracking accuracy and robustness, initial positioning (34 %) and ergonomics of AR devices resulting in limited user
acceptance (29 %) are the dominating challenges. Availability or adaptation of data and authoring time/costs are other
important challenges as well as battery life time.
In spite of these challenges participants declared a stronger AR demand and a higher acceptance of AR during the last
two years.
Additional information on industrial use cases could be gathered during the two workshops held by ISG ARF in Berlin
(1.2.2018) and in Paris (23.5.2018). There were a number of presentations on use cases from various fields. An
overview of these presentations can be found in Annex B. The structure of Annex B is the same as in the questionnaire,
however there is not always information available for every category, therefore it is difficult to get quantitative results
from this overview. However, this overview supports the general outcome of the questionnaire.
5 Usage conditions
5.1 Overview
This clause describes the usage conditions of AR technologies regardless of the use cases as expressed in the responses
to the online questionnaire (see Annex A). It is subdivided into "Usage environments", "Operating conditions" and
"Augmentation data sources".
5.2 Usage environments
The scales of usage vary from "Letter Scale (A4 letter size)" to "World Scale (~1 000 m )", while "Room Scale
(~10 m )" is dominating (39 %). The given values are depicted in Figure 3.
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Figure 3: Room scales for AR applications
In addition, in 43 % of the responses there are user friendly or office conditions, in 47 % the conditions are medium
difficult, which may include dust or water projection, mid temperatures and small vibrations. Only 10 % are extremely
hard conditions, e.g. direct rain exposure, high temperatures, a lot of dust and high vibrations.
5.3 Operating conditions
65 % of the participants want the AR user to have his hands-free while using the solution. 85 % of the respondents want
the augmentations to be precisely located relatively to a real equipment or object. 73 % of contributors expect an
accuracy of a few millimetres or under. In 88 % of the cases a viewing distance of less than 5 meters to the object (44 %
close to hand) is required. 41 % expect the augmentation to be shared among several users, which has significant
implications for the use of head-mounted displays.
In 57 % of the cases there is a model evolving over time (e.g. step by step assembly of a mechanical structure), in 43 %
it is static.
In 63 % of the cases the scenes to be augmented are mostly static, whereas in 37 % there are moving objects or persons.
5.4 Augmentation data sources
78 % of the participants identified CAD models as source of information for AR application. 52 % of these data have a
high level of confidentiality. These data have to be stored on sovereign clouds, internal servers or on the AR devices
with an extremely high secure access control.
6 Detailed description of the 4 dominant Use Cases
Besides "Innovation" the most dominant Use Cases identified in the survey are:
• Inspection/quality
• Maintenance
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• Training
• Manufacturing
Therefore, these four Use Cases are taken as the basis for a more detailed analysis. They will also be used to identify
relevant standards or lacks of and to define the further activities of the ISG. "Innovation" has been disregarded, although
it has the highest score in Figure 1, because "Innovation" is not really an industrial use case but rather reflects that many
participants in the questionnaire came from universities or research institutes. Therefore, the answers from academia
have also been disregarded from the subsequent analysis, although comparison shows that results with or without
academic institutions are not significantly different.
Disregarding the answers of academic institutions 55 % of the answers to the questionnaire came from technology
providers whereas 38 % came from technology users (see Figure 4).
None
AR
7%
technology
user
AR
38%
technology
provider
55%
AR technology user AR technology provider None

Figure 4: Participant's profile
With respect to augmentation precision, there are different requirements concerning the accuracy in the four main use
cases. While inspection and training require accuracies of a few millimetres or even below, maintenance and
manufacturing partially accept higher tolerances (see Figure 5).

Figure 5: Accuracy of augmentation
73 % of the respondents identify CAD models as a possible source of information for augmentations.
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As far as security is concerned, 52 % of the participants declare their data need a high level of confidentiality. For them
data have to be stored on sovereign cloud, internal server or on the device with an extremely high secure access control.
Sharing the viewing of augmentations between several users is expected by 60 % of the participants. This has
significant implications for the use of head-mounted displays. However, this is also depending on the use case (see
Figure 6).
Figure 6: Sharing the viewing of augmentations
For 65 % of participants it is required to have their hands free while using the AR application. However, this is also
depending on the use case (see Figure 7).

Figure 7: Percentage of hands-free operation
In about 2/3 of the use cases the environment or area of interest is evolving over time (e.g. step-by-step assembly of a
mechanical structure) as depicted in Figure 8.
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Figure 8: Percentage of evolving augmentations
Room-Scale (area of ~10 m²) is the predominant size of AR usage for the participants. However, extreme values (Letter
scale or World scale) are also mentioned several times (see Figure 9).

Figure 9: Size of the area of interest, where augmentations may occur
With respect to dynamics, in about 40 % of the use cases there are moving objects or people, around or inside the area
where augmentations are located; in 60 % of the use cases the objects are static.
With respect to environmental conditions more than 70 % of the users operate under medium to hard conditions (see
Figure 10). 19 % of participants declare the working environments are even subject to explosive atmospheres (ATEX
certified devices required).
Figure 10: Environmental conditions of AR applications
TM TM
As shown in Figure 11, Android is the dominant operating system in AR application (46 %), followed by Windows
TM
(28 %) and iOS (19 %).
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Figure 11: Share of operating systems in AR applications
The remainder of this section is dedicated to benefits and challenges of AR. Figure 12 describes the expected benefits of
AR in the four main use cases.

Figure 12: Expected benefits from AR usage
A rather homogeneous distribution of responses has been noted, which illustrates the holistic nature of the uses of
augmented reality. Training is identified as the main outcome of Augmented Reality.
7 Typical industrial Use Cases
7.1 Overview
In the following clauses, four typical use cases are described, which belong to the four categories mentioned in clause 6.
7.2 Use Case 1: Wireless network and IoT installation
7.2.1 Context
In the industry, but also at home, wireless networks are backbones that support numerous devices and objects from the
Internet of Things.
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The deployment of that kind of infrastructure in factories or at home is sometimes complex due to the intangible nature
of wave propagation. An interesting challenge is how to place emitters and repeaters to optimize the wireless coverage.
Once the coverage is well tailored and established, the installation of the connected objects is another interesting
challenge. The objects have to be identified and integrated safely in the network, but also localized with their position
and orientation in the real world. Indeed, this location information is highly valuable to provide smart contextual
services to the end user.
A typical use of the position of the object is to provide access to information or to an interface on the objects that are in
the user Field of View (FoV). It can also be used to ease the construction of home automation scenarios involving
different objects; making a switch command different sets of lamps or blinds for example.
AR can greatly facilitate the wireless network deployment, the installation of objects, and afterwards, it could also help
provide rich contextual services to use or to service them.
7.2.2 Scenario
7.2.2.1 Objectives
The present scenario describes how AR can help to build a map of the environment to forecast the wireless coverage,
and to place radio emitters and repeaters.
Then AR will be used to ease the installation of sensors, actuators and all kinds of smart devices.
7.2.2.2 Building the map of the environment to be equipped
The installation technician takes an AR enabled device. Thanks to a SLAM algorithm, the device is constantly
geolocalized. The technician can point the camera of the device successively towards each wall to get their normal
vectors. From the sequence of normals, the position of each wall can be deduced. Then the shape of the room can be
modelled by intersecting the identified walls.
The technician can then point to the borders of each aperture, i.e. doors and windows, to add them to the map.

Figure 13: Resulting room map
Figure 13 presents walls, whose colours indicate their material, and voids indicating where the different apertures are.
7.2.2.3 Checking the radio coverage
Once the environment map is built, a place for the WIFI emitter to be installed may be chosen. The emitter position and
the map information will now feed a prediction tool to compute the WIFI coverage as shown in Figure 14.
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Figure 14: Resulting WIFI coverage prediction
The same map could be used to evaluate the coverage for other wave length and technologies like those generally used
by IoT standards.
7.2.2.4 Placing connected objects in the environment
Once the coverage has been checked, IoTs may be deployed and sensors, actuators and other smart devices may be
placed throughout the house.
Each object is associated with a digital twin, or "digital avatar". This virtual representation of a real object is made of
two parts.
A visual representation can be seen through an AR enabled device as shown in Figure 15. It is collocated with the real
object, and indicates that the user can get some information from it, by pointing at it for example.
A hidden part contains the information about the object and could also provide a handle to access it.

Figure 15: Digital avatars placed in the real world
The augmented reality device will allow to anchor the avatar near or over the real object and to establish a link between
them.
7.2.2.5 Searching and selecting objects
As said before, by using an AR enabled device, the user will be able to see the avatars associated with each object and
to directly interact or service it.
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7.2.3 Advantages
The scanning of the environment to build a map can be performed quickly by anyone without the need of dedicated
measuring equipment.
The positions of the objects are stored with a simple point and click operation.
Interacting with the objects is performed in the same way.
7.2.4 Needed characteristics
In every step of the scenario, the user's smartphone has to be rigorously and robustly registered with the real
environment, and be able to quickly relocate in case of position loss.
The system has to be able to:
• Get the normal of a wall even if it is not textured.
• Extract a valid plane even from a small point cloud area.
• Position anchors in the real world to hook digital twins associated with each IoT, and share them with all the
users who need to interact with them. A centimetre scale precision is enough.
7.3 Use Case 2: Service Remote Support
7.3.1 Introduction
In today's competitive working environment cost and time pressure are business-relevant issues for service and
maintenance and in a world of more and more connected work flows there are rising demands on availability and
service quality. Additionally, the technical complexity of solutions is increasing as well as the amount of data that needs
to be processed and evaluated as part of the work process.
The transfer of knowledge is essential for guaranteeing a certain level of service quality and experience and skills need
to be transferred fast and efficiently to employees and subcontractors. The well-known paper-based documentation is
upgradable in an extensive way but there are no feedback functions foreseen and its usage can become impractical,
especially for outdoor activities. Documentation has to be kept up-to-date and it should be centrally provided without
any media disruption. The level of detail of such a documentation needs to be adaptable to the problem that needs to be
solved and to the knowledge of the employee. Newcomers may get a step-by-step description whereas experts only
have to check items provisioned by a check list.
7.3.2 Supporting functionalities in an AR environment
Figure 16 demonstrates an example for working in an AR environment. It shows the AR-supported wiring inside a
switching unit.
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Figure 16: Wiring inside a switching unit
In order to make such a scenario happen as shown in Figure 16, new functionalities have to be developed and included
into the current working process. These functionalities are as follows:
• Display of documentation in the AR device
• Easy handling of documentation by voice and gesture control
• Sharing of documentation with remote support
• Display of information for step-by-step instructions via augmentation directly into the working area
• Adaptation of information display depending on the actual field of view
• Level of supporting details can be selected by the worker
• Remote support for the connection to experts for video based guidance
7.4 Use case 3: Training
7.4.1 Context
NOTE: This work has been carried out within the framework of the EU H2020 TARGET project and the system
presented was developed by the Luxembourg Institute of Science and Technology (LIST). At the time of
writing the system is still being improved.
At present, law enforcement and rescue agencies train their staff for radiological incidents using three distinct
modalities:
• physical simulations (using live sources, simple mock-ups or simulants);
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• multimedia simulations;
• desktop-based simulations.
Physical simulations provide a level of training which is the closest to real conditions, and can be used as mission
rehearsal exercises and to improve readiness. Using real resources, physical simulations are expensive, mobilising
resources that could be spent elsewhere for the time of the training, and may put these resources at risk. Multimedia and
desktop-based simulations do not have these drawbacks but instead lack fidelity, in particular equipment fidelity. This
kind of training (e.g. Command Post Exercises, Tactical Exercises without troops, etc.) can be well suited for strategic,
and tactical-level operator training but clearly lacks simulation fidelity for operational-level operators.
TM
Firearm training simulators (for instance VirTra V-ST Pro ) address this problem by providing operators with real
equipment and allowing them to shoot munitions on videos beamed on a screen. While these simulations are less
expensive than physical simulations, are photo-realistic and offer hands-on training, they do not allow for real operator
movements.
7.4.2 Application scenario
Chemical, Biological, Radiological and Nuclear (CBRN). This scenario consists of using Augmented Reality and real
world objects to simulate a clandestine radiological laboratory using simulated detection devices and radiological
sources. The aim is to let teams train in the detection, identification and ultimately the extraction of dangerous materials
while guaranteeing the involved operators' safety. Augmented reality allows for a safer training (than using simulants or
live sources) and importantly can be used to display contamination or dispersal of chemicals, etc. which cannot
normally be seen.
The primary points for the training are as follows:
• The first responders (2 person team) enter into a suspicious room such as a clandestine laboratory. They have
to check the area for any dangerous substances and measure the dose level.
• The trainees use virtual devices to simulate the detection equipment that such a team would be able to use.
• Radiological substances are simulated and can be displayed in the augmented reality.
• The first responders have to work in personal protective suits.
• A minimum of two trainers is required to arrange and support the training from professional personal
development perspective.
7.4.3 System requirements
7.4.3.1 Overview
For the training scenario environment, augmented reality is used. This implies that some objects are real, namely there
is one bed, one table, simple laboratory set, possibly other accessories (e.g. bottles or boxes) and some objects are
virtual, including selected radiological substances and simulated casualties (see Figure 17). Training scenario is focused
on the operational/tactical level. The primary objective is to train first responders on site in radiological procedures.
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Figure 17: AR HAZMAT training scenario
7.4.3.2 Requirements analyses
The requirement was to have the modelling of the dispersion of dangerous radiological elements in an Augmented
Reality Scene while optionally giving visual and aural clues to the user. Typical CBRN dangerous elements include:
fluids, gases or solids. In this case, it was particularly interesting to model radiation contaminating elements that might
be spread into a scene while performing tasks, for example if the first responders touch multiple surfaces and therefore
spread some radiation.
Augmented reality has a significant advantage over virtual reality in that the trainees can visit a real environment (e.g.
an office) and add to this some virtual elements. This allows them to see, touch and interact with a mix of the real world
surrounding environment and the augmented reality aspects, therefore potentially improving the experience.
In order to extend the richness of the real elements of the scene, the possibility to designate as many of them as needed
as contaminable objects was required. That is, such designated objects should become contaminated if they come into
contact with contamination sources. Furthermore, virtual objects and elements of the scene should also be
contaminable. In addition to detecting contamination, visualisations may be added so that the trainees can see which
areas have become contaminated.
At the end of the exercise, all CBRN personnel should pass through a decontamination process. In real cases, this
decontamination process consists of cleaning the agent with the CBRN suit on, immediately after leaving the dangerous
area. For this procedure, it is irrelevant whether the agent has been contaminated or not, as every part of the suit has to
be cleaned with a high-pressure cleaning liquid jet. The requirement was to model this decontamination procedure.
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7.4.3.3 Spreading Contamination
The primary element of this model is the "contamination spot" or "contamination source". This atomic element has a
remaining amount of radiation, a minimum amount of radiation and a location in the scene. When enabled, the
visualization of a contamination spot consists of a green/yellow, sphere with a degree of transparency that has a cyclic
animation of contraction and expansion. Audio is used to indicate when contamination occurs.
7.4.3.4 Radiation Field
For the visualisation of radiation fields, initially a directional particle system with a gradient of colours was designed in
order to provide a visual cue to the user about the danger zones of a radiation source. The current version uses a dual
particle system, which models each radiation source as a sphere. The size of these spherical zones can be adapted to
different radiation field intensities without significantly affecting the visual aspect of the particle system and they more
accurately describe, visually, the radiation situation modelled underneath.

Figure 18: Contamination fields are represented by the red and yellow zones
7.4.3.5 Decontamination process
An important requirement is that the trainees are made aware of the importance of decontamination and how to
undertake this step (see Figure 18). For this to take place the trainee stands on a designated location within the
environment, the other trainee (decontaminator) see holograms superimposed over the other trainees body (see
Figure 19). Using positioning trackers the decontaminator has a simulated liquid spray which turns the holograms blue
as that part of the other trainees body is cleaned. This approach allows for a clear indication as to how effectiveness of
the decontamination step.
Figure 19: An example of the augmentation that the decontaminator sees over the trainee's body
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7.4.4 Conclusions
The primary benefit of augmented reality is that it is possible to simulate radiation sources without the inherent risk of
using live materials. Furthermore, augmented reality can make invisible items visible for example radiation. These two
aspects coupled with the ability to see how contamination spreads and the decontamination step demonstrate some of
the unique benefits that augmented reality can bring to radiological incident training which go above and beyond
existing approaches.
7.5 Use Case 4: Manufacturing
7.5.1 Context
The manufacturing and training processes have been op
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