# Comparison of Spatial Visualization Techniques for Radiation in Augmented
Reality

Fintan McGee<EMAIL_ADDRESS>0000-0001-7398-2664 Luxembourg Institute of
Science and TechnologyMaison d’Innovation, 5 Avenue Des Hauts-FourneauxEsch-
sur-AlzetteLuxembourgL-4362 ,  Roderick McCall<EMAIL_ADDRESS>0000-0002-0765-8919 Luxembourg Institute of Science and TechnologyMaison
d’Innovation, 5 Avenue Des Hauts-FourneauxEsch-sur-AlzetteLuxembourgL-4362 and
Joan Baixauli<EMAIL_ADDRESS>0000-0001-7327-0966 Luxembourg Institute
of Science and TechnologyMaison d’Innovation, 5 Avenue Des Hauts-
FourneauxEsch-sur-AlzetteLuxembourgL-4362

(2024; 15 September 2023; 12 December 2023; 22 February 2024)

###### Abstract.

Augmented Reality (AR) provides a safe and low-cost option for hazardous
safety training that allows for the visualization of aspects that may be
invisible, such as radiation. Effectively visually communicating such threats
in the environment around the user is not straightforward. This work describes
visually encoding radiation using the spatial awareness mesh of an AR Head
Mounted Display. We leverage the AR device’s GPUs to develop a real time
solution that accumulates multiple dynamic sources and uses stencils to
prevent an environment being over saturated with a visualization, as well as
supporting the encoding of direction explicitly in the visualization. We
perform a user study (25 participants) of different visualizations and obtain
user feedback. Results show that there are complex interactions and while no
visual representation was statistically superior or inferior, user opinions
vary widely. We also discuss the evaluation approaches and provide
recommendations.

Augmented Reality, Visualization, Spatial Awareness, CBRN Response Training

††isbn: 978-1-4503-XXXX-X/18/06††journalyear: 2024††copyright:
rightsretained††conference: Proceedings of the CHI Conference on Human Factors
in Computing Systems; May 11–16, 2024; Honolulu, HI, USA††booktitle:
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI
’24), May 11–16, 2024, Honolulu, HI, USA††doi: 10.1145/3613904.3642646††isbn:
979-8-4007-0330-0/24/05††ccs: Human-centered computing Mixed / augmented
reality††ccs: Human-centered computing Information visualization††ccs: Human-
centered computing Empirical studies in visualization

Figure 1. Three different approaches to visually encoding radiation in the
physical environment around the user. In this composite image there are three
Augmented Reality radiation sources, the barrel (left), the small suitcase
(middle) and the laptop on the table (right). At the leftmost side there is no
visualization of the radiation, moving right, a banded visualization coloring
the spatial awareness mesh is used, next a hexagon is used as a stencil, to
allow better visibility of the real-world objects. Finally, an arrow-based
visualization is shown illustrating the flow of radiation away from the
sources.

A composite image of a room showing three different augmented reality
visualizations of radiation from virtual sources.

© Fintan McGee, Roderick McCall, and Joan Baixauli. 2024. This is the author’s
version of the work. It is posted here for your personal use. Not for
redistribution. The definitive Version of Record was published in Proceedings
of the CHI Conference on Human Factors in Computing Systems (CHI ’24),
https://doi.org/10.1145/3613904.3642646.

##  1\. Introduction

AR offers the possibility to show people make the invisible, visible. It
allows a user to see the inside of a printer to help with maintenance tasks
(Feiner et al., 1993), or to visualize underground civil infrastructure
(Zollmann et al., 2010), while archaeological AR can allow long disappeared
buildings to be visible once again in their original setting (Vlahakis et al.,
2002). AR can also enable a user to understand other aspects of their
environment that may not be visible to the naked eye such as pollution (White
and Feiner, 2009) or radiation (Leucht et al., 2015). These aspects may be
completely simulated, as part of a training exercise, but visualizing them
helps a user better understand their environment and the results of their
actions. AR has long been used for training, particularly in situations where
training with real material and equipment may be prohibitively costly and
possibly even dangerous, e.g., laboratory safety training. In this paper, we
examine the use of AR for the visualization of environmental threats,
specifically harmful radiation (see Figure 1). Using simulated virtual
radiation sources avoids the risk of exposure of training participants to
actual radiation. While our approach has been motivated by the use of AR for
the training of emergency responders for radiological incidents, we believe
the techniques and results described here can be generalized for other
applications.

It is possible to visualize radiation by highlighting the source object, but
of course the radiation field extends beyond its physical boundaries.
Visualizing a field is also difficult, not least because the extents can span
large areas, and it is difficult to convey the increase of intensity at points
closer to the source. Therefore, we choose to visualize the intensity of
radiation on the surfaces around the sources and the user. To this end we
leverage the spatial awareness mesh, provided by the HoloLens 2 AR Head
Mounted Display (HMD). However, this also presents challenges. AR blends the
virtual with the physical. A simple coloring of the mesh and objects around
the user based on intensity of radiation may obscure objects in the real
world. The safety critical nature of the work means that the visualization
should be easily understood and imply the correct (or safest) movement of the
operative concerned within the space. Also given the life critical time
constraints involved, the visualization should not require time to be
understood, it should be almost implicit. Our approach can be considered as
situated visualization, as the radiation is visualized around the position of
a virtual source in the real world. In the context of the recent work of Lee
_et al._ (Lee et al., 2023) on situated visualization, such an approach can be
considered a _decal_ design pattern, in the sense that it is a texture mapping
applied to the real-world physical objects modeled by the spatial awareness
mesh.

### Objectives and Motivation

The objective of this work is to demonstrate how radiation can be best
visualized in a dynamic real-time manner in the environment around the user.
The purpose of visualization is to help a human user to better understand the
nature of the threat they are facing, allowing them to minimize their
radiation exposure. The scenario that motivates our work is the training of
first responders for radiological incidents. Where the purpose is to find,
identify and retrieve the radiological sources. The purpose of the
visualization in this training scenario is to provide team members with an
understanding of the radiation distribution in the room from multiple virtual
radiation sources. The basic reasoning being that the movement of the user,
and hence their proximity to radiation sources and the time spent near the
sources, will vary based on the visualization approach. The most effective
visualization being the one where the total dose of radiation absorbed is the
lowest. We explore techniques to give a user the best perception of both the
virtual radiation and the real word environment around them (see Figure 1).
The solution must work in real time as a radioactive source may be moved
around the scene. Additionally, there may be multiple sources so the effects
of the radiation must be visualized in a cumulative manner. We want to avoid
obfuscating an entire scene and physical environment while also ensuring that
the intensity of the sources is comprehensible to the user. The visualization
should help the user understand which potential path will result in the lowest
level of exposure. The contribution of this work lies in the technical
demonstration of the approach implemented on commodity hardware, the empirical
evaluation comparing the resulting visualizations in a user study, as well as
the discussion concerning the results and evaluation approach.

##  2\. Background and related work

In this section we describe related visualization work in AR, focusing on CBRN
(Chemical, Biological, Radiological and Nuclear) response and radiation
visualization.

###  2.1. Augmented Reality and Visualization

Spatial integration of virtual objects into the real world, in the context of
visualization in AR, has been recognized as a challenge for some time
(Kalkofen et al., 2011). It can be done as part of an in-situ visualization,
where the data and visualization are co-located in the real world, such as
pollution data in a city (White and Feiner, 2009). In-situ visualization AR
also is used to visualize data that is inherently 3D such as spatio-temporal
interaction data, as done by the MIRIA Toolkit (Buschel et al., 2021).
Recently the topic has received renewed attention in the literature, such as
the work of Lee _et al._ (Lee et al., 2023) describing design patterns for
situated visualizations, and the work of Calepso _et al._ (Calepso et al.,
2023) exploring AR for situated analytics. Other work has also shown that 3D
Data manipulation in AR with tangible markers improves time and accuracy for
tasks that require coordination between perception and interaction (Bach et
al., 2018). The motivating factor for this work is to provide a safe training
environment (e.g., no active radiation sources), where the visualization
techniques can improve future safe behaviors of the end-users.

###  2.2. AR for CBRN Response

AR and VR have been used to support training for a wide range of potentially
dangerous incidents e.g., US Army (Amburn et al., 2018) and CBRN device
training using VR (For, 2023). AR has also been used for managing actual
radioactive incidents, for example (Ismael et al., 2023) to provides live
information on the source, position and spread of radioactive materials. The
primary advantage of using AR is that it makes the invisible, e.g., radiation,
visible. Furthermore, live information from simulated or real equipment can be
provided in real-time to the HMD.

Typically, radioactive incident teams must (1) find sources (2) identify the
type of source (3) extract the source from the scene. The procedures vary in
each country, and for safety reasons the extraction team is not the same one
that is used to find and locate the sources. In our scenario, we assume that
the sources have been found, identified and that the operative assumes the
role of an extraction team member who should remove the source from the scene.
In order to fulfill this task, the operative must have a good level of
situation awareness (Endsley, 1988), which relies on three key aspects (a)
perception (b) comprehension (c) planning. If they possess a good level of
situation awareness, they should be able to plan the best path to the source
and remove it from the scene while minimizing the total amount of radiation
absorbed. The focus in our visualization approaches is predominantly on
egocentric perception. By this we mean how the operative estimates the
distance between themselves and the stimuli (radiation source).

###  2.3. Existing AR Approaches for Visualizing Radiation

Guarese _et al._ (Guarese et al., 2021) visualize electromagnetic fields in AR
for the purpose of compatibility testing. Their approach uses lines in 3D
space to show a 3D field and colored vectors to show emitted electromagnetic
radiation. Electromagnetic signals are also the focus of WaveRider (Rowden et
al., 2022). It visualizes signals of WiFi routers, using the surfaces (walls,
ceilings, and floors) of the environment around the user. The authors use
novel visualization approaches and have developed and evaluated their
techniques in VR using a 3D model of the environment. They have also developed
an initial prototype in AR, although for the AR prototype the 3D model for the
visualization is manually registered with the real world. In contrast in our
work, it is automatically registered. Recent work by Meireles _et al._
(Meireles et al., 2023), which extended the prior work of Carmo _et al._
(Carmo et al., 2017), focused on situated visualization of solar radiation on
building facades using tablet based AR. Building data is retrieved from a
spatial database, based on the user position and search radius, and
parallelepiped glyphs are drawn over the facades, with an artificial color
scale to indicate the underlying values. VIPER (Smith et al., 2022) visualizes
the fields of static radiation sources as semi-transparent isocontours using a
rainbow based colormap to show different intensities of radiation. However, it
is difficult to understand the radiation levels at different points in space,
therefore the isocontour is sliced into on the vertical and horizontal axis.
Additionally, the isocontours may also obscure other aspects of the scene.
VIPER also visualizes a user’s path to see the radiation intensity at each
point. Leucht _et al._ (Leucht et al., 2015) describe an approach to
visualizing X-ray radiation in a surgical context. Their work focuses on
simulating radiation doses from a C-Arm fluoroscope used by surgeons and uses
two depth cameras to create a visual representation of the surgeon augmented
with a color coded radiation map. The spatial mesh is generated from a
stationary depth camera, and the setup process requires calibration. There is
no HMD, and the fluoroscope is the only source of radiation, and it cannot
move around the scene. Most recently Pakari _et al._ (Pakari et al., 2023)
delivered a solution for real time visualization of radiation data output from
a dual particle radiation detector. They use a perceptually uniform colormap
rendered on the spatial awareness mesh around the detector, as well as using a
ray-based approach. Their approach is targeted towards real radiation data
being processed and visualizing the reported radiation levels in the space
around the detector. It is not targeted towards training purposes with
multiple virtual sources.

##  3\. Technical Approach

In this section we describe the requirements for our visualization approaches,
and the techniques we used to realise them.

###  3.1. Requirements

Our technical approach is constrained by the fact that it is integrated as
part of a larger existing AR training platform. This platform uses the
HoloLens application as a client to a central server application and provides
different training scenarios. We did not have the option of using a toolkit
such as RagRug (Fleck et al., 2023) or DXR (Sicat et al., 2019), as our
approach needed to not require significant changes to the existing
architecture and code base and be flexible enough to seamlessly integrate into
other scenarios. Furthermore, trainees need to interact in the real spaces,
unobstructed by AR visualizations. We cannot use a volumetric visualization
approach or 3D isocontour approach as done by VIPER (Smith et al., 2022), as
this will clutter to the scene and obscure real-world items. Our approach of
drawing on the physical environment to reduce clutter has some similarities to
the approach of WaveRider (Rowden et al., 2022), although the environment in
that case is defined by an existing 3D model and our use cases are quite
different. To meet our objectives for supporting the training for radiological
incidents, the following requirements, based on first responder feedback
during a previous project, had to be met by the implementation:

  1. (1)

The visualization should help the user determine the safest path to follow.

  2. (2)

The radiation will be visualized on the surfaces around the sources (i.e.,
using the spatial awareness mesh), to avoid adding extra clutter to the scene.

  3. (3)

The visualization should be responsive and support real-time updates of moving
sources.

  4. (4)

Radiation must accumulate from multiple sources.

  5. (5)

It must be possible to allow the users to see both the physical world and the
virtual radiation.

The key to visualizing the spatial awareness mesh using a texture that
reflects the radiation intensity at that given point in three dimensions.
There are many issues to be overcome such as: (1) accessing the mesh data
points, (2) the lack of texture coordinates for the continuously updated
spatial awareness mesh, (3) the mapping between a texture point on the 2D
mesh, (4) the appropriate level of radiation intensity and, (5) orienting a
texture towards a 3D point, if orientation is being encoded.

###  3.2. Hardware and Using the Spatial Awareness Mesh for Visualization

The HoloLens 2 was chosen as it is one of the more advanced headsets on the
market and offers the spatial awareness functionality required by our
approach. It is an HMD that allows the users nearly full visibility of the
real world, but a limited AR display (it has a diagonal field of view of 52
degrees). This means that a user may be able to view the real-world elements
without looking through the AR display. This work uses the HoloLens’ 2 Optical
See Through (OST) AR, as professional first responders need to not have their
vision limited in any way and they require a compact device that minimizes
interference with Personal Protective Equipment. Additionally, the resolution
of many pass-through AR devices makes it difficult to see small details,
making contemporary pass-through AR devices not suitable. This may change in
future. For development we used the Mixed Reality Toolkit (MRTK) (Microsoft,
2024), with the Unity Game Development Engine, which provides access to the
spatial awareness mesh. The HoloLens 2 spatial awareness mesh is continuously
updated using 3D scans of the surrounding environment. It is essentially a
simple 3D mesh provided without any texture coordinates. This means that if a
standard texture is to be used, it is necessary to map from the 3D position to
two-dimensional texture coordinates onto which the texture can be drawn. The
mesh is typically drawn by applying a Unity material to the mesh to make it
visible. The spatial awareness mesh must be built by the HoloLens 2 using its
depth camera and it has limited extents. With our approach, being able to see
the radiation requires that there is a spatial awareness mesh rendered near
the source. In all practical training scenarios, a radiation source will be on
a surface, with walls and other structures nearby, that can be identified by
the spatial awareness mesh. The only time there may not be a spatial awareness
mesh around a source is when the radiation source is far from the HMD. We
address this by drawing a plane that is bill-boarded towards the user (i.e.,
always perpendicular to the camera), and texturing it with the radiation
texture (but leaving it transparent otherwise), if the source is above a
threshold distance. The plane fades as the trainee approaches, and the spatial
awareness mesh around the source is filled in. This technique has been
implemented but is not part of our visualization evaluation, as the room the
experiment took place in can be fully described by the HoloLens 2 spatial
awareness mesh.

###  3.3. Shader Overview

Modern computer graphics approaches allow for custom programs, known as
shaders, to be written and executed on a device’s GPU. While there are some
standard shaders supplied by the MRTK, the technical requirements for
visualizing radiation levels in the mesh resulted in custom shaders being
developed (more details are available in the supplemental materials). These
shaders had to deliver the following functionality:

  * •

A point in the mesh must be colored based on the intensity of radiation at
that position.

  * •

Multiple sources can contribute to the radiation at any given point.

  * •

To allow the users to see both the physical world and the virtual radiation,
it must be possible to limit colors to a specific stencil shape leaving gaps
in the coloring using a stencil.

  * •

To help the user determine the best path to follow it must be possible to
orient stencils in a specific direction.

###  3.4. Implementation for Source Accumulation

Figure 2. The color palettes sampled by the fragment shaders, the continuous
on the top and the 8-color banded variant on the bottom. The yellow end of the
scale indicates the highest radiation levels.

The full Viridis color palette and a Viridis color palette sampled into 8
bands of color

To calculate the intensity at a given point in the mesh, the shader needs to
consider the position of a source as input as well as the mapping of the
domain of the radiation intensity, defined by a lower and upper bound, to the
output color range. The min and max value of the intensity domain are defined
as shader inputs. The output color range is defined by a texture (essentially
a color bitmap), which contains the color scale. The input texture used for
this work was generated for the Viridis scale. The only time another texture
is used is when the color scale is to be banded, see Figure 2. The fragment
position in world coordinates is interpolated from the world space position of
the vertex. The contribution from each radiation source at the fragment is
calculated, based on the source positions which are defined as inputs. The
intensity is calculated using exponential drop off (dose rate divided by the
distance squared). The mapping between the final intensity and the color is
done based on the maximum and minimum range inputs. This fundamental approach
for coloring a pixel based on radiation intensity does not require any texture
coordinates (that is a mapping to coordinates of an input texture), as the
only factor affecting the pixel is its distance from a source.

###  3.5. Using a Stencil

To avoid drawing every pixel with radiation intensity, we used a stencil-based
approach. The radiation intensity will only be rendered for a pixel if it
falls within a shape specified by the input stencil texture.

Figure 3. The actual textures used for rendering each stencil, but
transparency has been colored black. The patterns were tiled differently so
the hexagons appeared visually similar in scale to the circles when rendered.

A white circle with black background, white hexagons with a black background,
A white arrow on a black background pointing to the left

We began with the simplest texture, a circle, however one potential issue with
such a pattern is that the space between the circle texture is not consistent.
Therefore, for a more regular spacing of the stencil we chose a hexagon, as
the distance between adjacent hexagons is more consistent and hexagons vary
little under rotation (although they are not fully rotationally invariant like
circles). One key issue to be faced was how to apply the stencil shape texture
to the spatial awareness mesh. The spatial awareness mesh is consistently
updated by the HoloLens hardware to reflect the world. It is dynamic messy
data, the does not come with a neat set of texture coordinates that are easily
looked up. Therefore, we adopted the approach of tri-planar texture mapping.
Tri-planar texture mapping is a technique by which textures are rendered
aligned with 3 planes (usually aligned with the coordinate system for
practicality), and then the final pixel is colored based on a blend of the
resulting textures. To only show the radiation intensity within a specific
stencil shape, we used the stencil buffer of the HoloLens graphics
capabilities. The stencil buffer allows for fragments to only be drawn based
on those specified in an initial pass, all fragments not drawn in this initial
pass are not contributed to in subsequent rendering passes. This approach
involves a rendering pass to the stencil buffer where the spatial awareness
mesh is drawn using a stencil texture. Fragments for the final rendering pass
are only considered where the stencil texture was drawn in the initial pass.

###  3.6. Orientation

The arrow stencil needs to be oriented in the correct direction, considering
all 3 of the tri-planar textures. To achieve this effect a rotation must be
applied to each, rotating them to point away from the average source position,
weighted by source intensity.

###  3.7. Resulting Visualizations

Different techniques were being evaluated, however there were some common
characteristics. We have chosen the Viridis color scale (Garnier et al., 2021)
to be the basis of all stimuli. This color scale has been recently evaluated
as effective (Liu and Heer, 2018) and is considered more resistant to red-
green color blindness. The change of color is exponential (as radiation values
drop exponentially with distance). Other palates can easily be applied to our
approach by changing the input texture. The focus of our work is how the
visualization is rendered, not which color scale is chosen. Readers who wish
to further explore the impact of colormap are referred to (Reda and Szafir,
2021) and (Liu and Heer, 2018). The final list of visualizations can be seen
in Table 1, and examples of the visualizations in Figure 4. For details of the
implementation of the visualizations see the supplemental materials.

Table 1. The visualization approaches.

A description of each of the visualization approaches applied.  Name
Description Motivation Experiment Hypothesis Continuous This uses the full
Viridis color scale Radiation visualization using the spatial awareness mesh.
No hypothesis as this was the base condition for comparison. Banded The color
scale is divided into 8 color bands. Allowing experiment participants to
clearly distinguish the changes in gradients. H1: The banded visualization
will outperform the continuous opaque visualization. Transparent Full-scale
color where the opacity of the color is set to 33%. To determine if
transparency can effectively allow a user to perceive the real world along
with the visualization. H2: the transparent visualization will outperform the
continuous opaque visualization. Circle This uses the full color scale but
with a circular stencil. Use of a stencil to allow the user to see through the
visualization. H3: The stencil visualization will outperform the continuous
opaque visualization. Hex Full color scale but with a hexagonal stencil. Use
of a stencil where the gaps between the stencil items are consistently spaced.
H4: the hexagonal visualization will outperform the circular visualization.
Arrow Full colors scale with an arrow stencil that is oriented pointing away
from sources. Providing direct directional information will make the best path
more clear. H5: the oriented arrow visualizations will outperform the
continuous opaque visualization.
H6: the oriented arrow visualizations will outperform the other stencils.

3 Radiation sources visualized using a continuous Viridis scale on a flat
plane

3 Radiation sources visualized using a banded Viridis scale on a flat plane

3 Radiation sources visualized using a continuous semi-transparent Viridis
scale on a flat plane

3 Radiation sources visualized using a circle stencil and a continuous Viridis
scale on a flat plane

3 Radiation sources visualized using a hexagon stencil and a continuous
Viridis scale on a flat plane

3 Radiation sources visualized using an arrow stencil and a continuous Viridis
scale on a flat plane

Figure 4. Illustrative examples of the visualizations on a flat plane, showing
radiation from 3 sources.

##  4\. Visualization Evaluation

We performed user experiments to determine the impact of each approach.
Participants performed a simple task for each trial with a specific visual
encoding, and we measured their exposure during each trial and the time taken
to complete the task. We have also used a questionnaire and free-form
responses to gauge their subjective feelings towards each visual encoding.

###  4.1. Experiment Hypotheses

We consider the continuous opaque visualization as the base technique, and the
others e.g., using transparency, banding, a stencil-based approach and
encoding direction as enhancements. The hypotheses related to each
visualization can be seen in Table 1. We propose an additional hypothesis that
individual personal differences between participants, as measured by Visual
Spatial Image ability (VSI) may impact on the results. VSI is essentially the
ability of a person to imagine a 3D scene based on 2D, auditory or textual
descriptions. We would expect people with higher VSI to more accurately build
a mental model of the risks in the scene, therefore leading them to avoid
risks more thus reducing their overall radiation dose absorption. VSI can be
evaluated using a pre-experiment questionnaire such as the Visual Spatial
Imagery set of Vorderer _et al._ (Vorderer et al., 2004). Our additional
hypothesis is as follows:

  * •

H7: Those who exhibit VSI ability will have a lower cumulative simulated
radiation dose exposure.

###  4.2. Experiment Setup

The Experiment took place in a room of 4.5M * 8.5M, with a partition in the
middle, 4.5M from one end of the room. There was space at either side of the
partition for the user to walk around it. At opposite ends of the room along
one side wall are doors by which the room was entered and exited for the
experiment (see Figure 5). For each trial there were 3 virtual radiation
sources in the room. For a specific trial, the user had to enter the room from
one door wearing the HMD and perform the trial task in a brisk manner (not
running), while keeping their exposure to the virtual radiation to a minimum.
The experiment evaluated each of the visualization approaches described in
section 3 across multiple trials. Each visualization type was used as a
stimulus for a block of trials. In addition to the 6 radiation visualizations,
there were 5 different configurations (position-wise) of the 3 radiation
sources in the room, referred to as scenes. The aim was that the visualization
would influence the user to determine which path to take to the table through
the central partition. A specific trial consisted of a 1 of the 6
visualizations in 1 of the 5 scenes. A sixth scene was used only for user
training. The scene layouts are illustrated in the supplemental materials. All
radiation sources were of equivalent strength and the visualization was
configured to have a radius of 2 meters for a single source. Radiation sources
have dose rate measured in Sieverts (Sv) per hour at a 1 meter distance. This
depends on the source substance and the amount of the material. Each virtual
source used for experiment had a dose rate of 1 millisievert per hour (mS/H)
at a distance of 1 meter. However, the actual dose received was not the focus
of the experiments or analysis, just whether the visualization type impacted
performance. The range of the visualization can be configured for different
values of inputs.

Figure 5. The layout of the experiment room.

A top down schematic of the experiment room layout, including labels for the
entrance, exit and card table location.

###  4.3. Trial Task

The trial task was designed to require that the user was able to understand
both the radiation and the environment around them. The task consists of the
participant entering the experiment room, searching for an item and exiting
the room during which time they must try to minimize their total radiation
absorption. In real-world scenarios, first responders will often search a
scene to find evidence, such as a USB key or mobile phone. We required a
recognizable item of reasonably comparable size, so we chose a playing card
(from a standard 52 card deck). Having to select from a set of similar items
means the participant needs to have some understanding of the physical world
as well as the virtual. The participants had to select the correct card from
amongst 26 other cards (a red suit and a black suit were used, to make them
easier to distinguish) which are lying face up and presented in random order
(See Figure 5 for a photo). For each trial, the user is given a new card and
asked to retrieve the matching one from the table. The purpose of retrieving
the card is to demonstrate to the experimenter that the correct item was
found. After each trial, the card was returned to the table in a different
position from where it was retrieved. During a trial, the experimenter would
remain outside of the room to avoid distracting the participant and to avoid
being registered as part of the spatial awareness mesh. The cards on the table
were fully re-randomized between trial blocks.

Figure 6. The table containing cards as used in the experiment.

A photograph of playing cards laid out on a table.

###  4.4. Experiment Structure

Participants were presented with consent forms and an experiment description
sheet as well as an information sheet describing each of the visualizations
(see supplemental materials). They were also given a verbal description of the
experiment procedure. When ready, the participant began with a training block
of trials to ensure they knew what to expect from the visualizations, ensure
that the experiment task was clear and reduce any potential learning effect.
The training block consisted of 6 trials, one for each visualization, and each
using a special training scene. The participants could explore the experiment
space, and the experimenter was present with them in the room for these trials
to answer any questions. The order of visualizations was randomized for the
training block. The experiment was divided into 6 blocks one for each
visualization. At the end of a block, the participant answered a questionnaire
concerning that specific visualization (described in more detail in Section
5). The order of blocks was randomized for each participant. Each block
consisted of 5 trials, one for each of the five configurations of sources
(referred to as scenes). The order of the scenes in each block were also
randomized. This meant the that in addition to the training trials each
participant performed 30 trials.

##  5\. Evaluation Approach

In this section we describe the materials and metrics used to evaluate the
visualizations.

###  5.1. Pre-Experiment Questionnaire

Table 2. The pre-experiment questionnaire.

The pre-experiment questionnaire with questions from the MEC-SPQ Visual
Spatial Imagery (VSI) 4 item scale [Vorderer et al. 2004]  # Source Question
Text Answer Format 1 MEC SPQ (Vorderer et al., 2004) When someone shows me a
blueprint, I am able to imagine the space easily. 7-point Likert Scale 2 It’s
easy for me to negotiate a space in my mind without actually being there. 3
When I read a text, I can usually easily imagine the arrangement of the
objects described. 4 When someone describes a space to me, it’s usually very
easy for me to imagine it clearly.

Prior to starting the experiment, we asked the user questions from the MEC-SPQ
Visual Spatial Imagery (VSI) 4 item scale (Vorderer et al., 2004) see Table 1.

###  5.2. Quantitative Metrics

For each trial, the time elapsed (seconds) and user position were logged. The
trial timer was controlled by the experimenter using a custom application on
an Android device, clicking on a trial start button when the participant
entered the room and stop when they exited the room having successfully
completed the task. The data was further cleaned by removing data points from
beyond the threshold of the entrance and exit doorways. The following error
metrics were then calculated based on the time and position data.

  1. (1)

Cumulative radiation exposure during the trial (Sieverts)

  2. (2)

Mean dose rate received (Sieverts per Hour)

  3. (3)

Mean distance to the nearest source (Meters)

  4. (4)

Maximum dose rate received (Sieverts per hour)

  5. (5)

Time in proximity (1.5 meters) to the card table (Seconds)

###  5.3. Visualization Questionnaire (Subjective Data)

The goal was to understand which techniques were more effective, in terms of
maintaining awareness of both the real and virtual worlds. We also wanted to
better understand the situational awareness aspect of each visualization
approach. Therefore, we also included questions from existing spatial
awareness evaluation surveys. We took questions from the MEC SPQ (Vorderer et
al., 2004) specifically the Spatial Situation Model (SSM) 4 item scale, and
SART (the situational Awareness Rating Technique) (Taylor, 1990), see Table 2.
SART is mainly drawn from aeronautics and highly dynamic environments, as our
scenes are static these aspects were removed. As such, we assess individual
ratings rather than the overall score provided by SART.

Table 3. Questionnaire taken after each visualization block.

The questionnaire taken after each visualization block, featuring questions
from the MEC Spatial Presence Questionnaire [Vorderer et al. 2004] and SART
(situation Awareness) [Taylor 1990] questionnaires, along with new questions
# Source Question Text Answer Format 1 This Paper I could maintain suitable
awareness of virtual radiation sources. 7-point Likert Scale 2 I could
maintain suitable awareness of my physical environment. 3 The balance of
visibility between the physical and virtual was appropriate. 4 MEC SPQ
(Vorderer et al., 2004) I was able to imagine the arrangement of the spaces
presented in the augmented reality experience very well. 7-point Likert Scale
5 I had a precise idea of the spatial surroundings presented in the augmented
reality experience. 6 I was able to make a good estimate of the size of the
presented space. 7 Even now, I still have a concrete mental image of the
spatial environment. 8 SART (Taylor, 1990) Complexity of the situation: How
complex is the situation, is it complex with many interrelated components
(high) or is it simple and straightforward (low)? Number in range 1 to 7 9
Arousal: How aroused are you in the situation, are you alert and ready for
activity (High) or do you have a low degree of alertness? 10 Concentration of
Attention: How much are concentrating on the situation? Are you concentrating
on many aspects of the situation (High) or are you focused on only one (Low)?
11 Division of Attention: How much is your attention divided in the situation?
Are you concentrating on many aspects of the situation (High) or focused on
only one (Low)? 12 Spare mental Capacity: How much mental capacity do you have
to spare in the situation? Do you have sufficient to attend to many variables
(High) or nothing to spare at all (Low)? 13 Information Quantity: How much
Information have you gained about the situation? Have you received and
understood a great deal of knowledge (High) or very little (Low)?

###  5.4. Visualization ranking

Finally, the participants were asked to rank the visualizations sorting them
from best to worst in their opinion, and to provide free text explaining their
choice, and adding any further information they thought was relevant.

##  6\. Results

Twenty-five adult participants took part, with 19 identifying as male and 6 as
female, and the majority had a computer science research background (see
supplemental materials for further demographic information). One participant
was removed from the quantitative analysis due to missing data for a small
subset of trials, meaning they could not be included in the within-subjects
analysis. However, the survey responses and ranking data were kept as they
were complete with no errors.

###  6.1. Quantitative Evaluation

The scores for each participant can be seen in the boxplots of Figure 7(f).
Each of the 6 metrics was tested for normalcy of distribution across all
trials using a Shapiro-Wilks test. The only metric which was normal was mean
nearest source distance. Therefore, all other metrics were tested for
significance using a Friedman test, while mean nearest source distance was
tested using ANOVA. The tests showed that there is no significant difference
(p ¡ 0.05) between the visualizations in terms of any metric. For all metrics
tested using the non-parametric Friedman test, the effect size was determined
to be small, using Kendall’s W. Therefore, none of our hypotheses can be
accepted with respect to performance.

The color legend for the results charts

(a) Time(s)

A Tukey boxplot showing the experiment results for time

(b) Cumulative radiation (Sv)

A Tukey boxplot showing the experiment results for cumulative radiation

(c) Mean Dose Rate (Sv per hour)

A Tukey boxplot showing the experiment results for mean dose rate

(d) Mean nearest source distance (M)

A Tukey boxplot showing the experiment results for mean nearest source
distance

(e) Max dose rate (Sv per hour)

A Tukey boxplot showing the experiment results for Max dose rate (Sv per hour)

(f) Time (s) in proximity to table

A Tukey boxplot showing the experiment results for Time in proximity to table

Figure 7. Tukey boxplots for each of the metrics used for evaluation, with
visualization approach being indicated by color.

Multiple Charts showing the results for each visualization

###  6.2. Visualization Questionnaire

Due to the ordinal nature of the Likert scale data, we applied a Friedman test
to the results. The only question showing significant differences in answers
was Q01, focusing on awareness for virtual radiation sources (Friedman test: p
¡ 0.05). However, a Wilcox test showed that this potential significant
difference was only between the circle and banded visualizations, and when a
Bonferroni correction was applied the significance was maintained. However,
this significant difference does not relate directly to any of our hypothesis,
therefore none of our hypotheses can be accepted with respect to any of
questions from the evaluation questionnaire.

Figure 8. The results of the post trial block questionnaires (bars indicate
standard error). The questions Q01 to Q07 were all asked on a 7-point Likert
scale ranging from strongly disagree (a value of 1) to strongly agree (a value
of 7). The remaining questions were answered on a numeric scale of 1 to 7.

A bar chart show the rankings of user preference with Arrow and Banded tied
for highest rank

###  6.3. Visualization Ranking

We analyzed the rankings, as can be seen in Figures 9 and 10(f), and there
were no significant differences across the users’ preferences, using a
Friedman Test (p¡0.05).

Figure 9. Average ranking for each visualization with standard error (1 =
bottom ranked, 6 = top ranked)

A chart showing the average rankings of the questionnaire for each
visualization

(a) Arrow

A bar chart showing a binary distribution of rankings for the arrow
visualization

(b) Banded

A bar chart showing a distribution of rankings for the banded visualization

(c) Circle

A bar chart showing a distribution of rankings for the circle visualization.

(d) Continuous

A bar chart showing a binary distribution of rankings for the continuous
visualization.

(e) Hex

A bar chart showing a distribution of rankings for the hex visualization.

(f) Transparent

A bar chart showing a distribution of rankings for the circle visualization.

Figure 10. The count of rankings for each visualization (1 = bottom ranked, 6
= top ranked).

Multiple Charts showing the count of rankings for each visualization (1 =
bottom ranked, 6 = top ranked).

###  6.4. Choice of Path

As discussed in section 4.2 and shown in the supplemental materials, for 4 of
the scenes there was one side on the central room partition that exposed the
user to more radiation. In order to consider if visualization had an impact on
path choice, we counted the number of times the path of highest exposure was
taken. Of the 576 trials logged for a scene with a clear path of higher
exposure, a participant chose it 71 times. There were no significant
differences between the visualizations.

##  7\. Discussion

The lack of significant results in the user evaluation is somewhat surprising,
however the results are interesting in and of themselves, given the
differences shown in the boxplots of Figure 7(f). Some insight can be gained
by examining the distribution of rankings of the visualizations, as well as
looking at the qualitative comments offered by the users. As noted earlier,
the role of visualization here was to influence human movement primarily
through improving their situation awareness (perception, comprehension,
planning), while maintaining a suitable balance between the real and the
virtual. The goal is for the visualizations to encourage the user to take the
safest path. In other words, minimizing radiation exposure, which is a product
of time and proximity to sources. We took a null hypothesis significance
testing approach to analyzing our data. Such an approach is frequently used in
visualization evaluation, in the context of determining whether a
visualization helps you answer a specifies question more correctly or more
quickly. For this experiment we were trying to determine whether the
visualization influenced the participants movement, while also not impeding
them at the card finding task. It is possible that these two goals were
somewhat confounding factors. While it is too early to provide a clear
indication, it could be that further work is required to understand more
clearly how human movement, not just understanding of the data, can be
influenced by visualization techniques. This perhaps requires a greater
analysis of the role of visualization on egocentric understanding e.g., the
understanding of the distance between the observer and the stimulus.

###  7.1. Performance Data

In the Tukey box plots of Figure 7(f), we can see that in terms of cumulative
radiation the arrow visualization performs well relative to other approaches
with a lower median value, a reasonable range of values, and only a single
outlier. This is reflected in the lower median time and median nearest source
distance. The arrows visualization approach resulted in the lowest average
radiation dose (2.97 micro-Sieverts), and lowest average time (12.84 seconds).
The arrow visualization also resulted in a smaller distribution of values for
time in proximity to the table. This is understandable, as the arrow stencil
provides the least amount of spatial awareness coverage of all the
visualizations. Unfortunately, the differences are not large enough for
statistical significance. Looking at the mean nearest distance scores (Figure
10(d)), participants rarely spent time within 1.5 meters of the sources. As
dose rate drops off exponentially with distance, this means that time was the
largest contributing factor. All the visualizations, even the base continuous
one, were effective at keeping the participants away from the sources. While
there were differences in time, they were not enough for the cumulative
radiation value to be affected to a significant degree.

###  7.2. Questionnaire Results Discussion

The results for the qualitative data were somewhat consistent with the results
for the performance data. As can be seen in Figure 8, with respect to the
first 3 questions concerning the balance for the real and virtual, the
responses of the participants do not diverge hugely from the performance
analysis. On average the circle visualization is ranked lowest, with arrow and
banded being ranked higher. Q02, concerning maintaining suitable awareness of
my physical environment, is the only instance of transparent visualization
having the highest average score (5.68). For MEC SPQ, the results generally
follow this pattern, although it is notable that the hexagon based
visualization comes near the top of the pack concerning estimating the size of
the space and maintaining a concrete metal image of the space afterwards. This
may indicate a different preference for a visualization approach depending on
the type of the task, however the lack of a significant result means much
further research and experimentation are needed to tease out the scale and
nature of any such preference.

###  7.3. Ranking Results

The results for quantitative data and ranking data were also somewhat aligned.
The arrows visualization was joint highest rated in terms of preference along
with the banded visualization (tied for first, with an average ranking score
of 3.84, see Figure 9). Once again there were no significant differences,
however we can see in Figure 10(f) that two of the more popular visualizations
(Arrow and Continuous) have a binary distribution, many people giving it a top
(or close) rank and a bottom rank. It appears that these visualizations were
either highly ranked or lowly ranked with little in-between, making it
difficult for the rankings to be differentiated. In the discussion following
their user experiments in data visualization in AR Bach _et al._ (Bach et al.,
2018) found that individual difference (of participants) may play a factor in
results and these rankings also suggest this. Looking at the individual user
comments and feedback, as well as the result of the VSI questionnaire, helps
to provide context.

###  7.4. User Feedback

The users who rated the arrow visualization highest stated it provided “
_additional information of direction_ ” and that it did not “ _hide details of
physical objects_ ”, and the arrows “ _give you more information”_. However,
those who ranked it lowest stated that there was “ _too much information_ ”
and their “ _direction was not clear_ ”. One user who ranked the banded first
and the arrows second stated that the arrows did not make the boundaries
clear, but the banded scenarios made it easier to see the limit of the
radiation. Overall, the arrow visualization was the most commented on in the
user feedback and had the highest ratio of positive comments to negative
comments (8 positive and 5 negative), as determined positive or negative by
the authors. The circle visualization had the most negative comments (9), but
surprisingly 3 users had positive comments (“ _have a sense of the physical
space_ ”, “ _allow me to be more aware of the surroundings_ ”, “ _I like the
cases where only some circles are shown, not too bright colors, and not
covering too much space_ ”). Of the 6 negative comments for the continuous
visualization, 4 of them mentioned difficulty in seeing the correct card on
the table. Despite the quantitative results, examining the users’ free text
responses does indicate some advantages and disadvantages of the various
techniques. A further study may be able to reflect the described advantages of
the arrow stencil, and better capture the disadvantages of the more solid
visualizations.

###  7.5. Consideration of Visual Spatial Imagery Ability Questionnaire

We removed 8 outliers from the data, who ranked themselves as lower than
”somewhat agree”, as we felt this indicates that they possess lower visual
spatial imagery ability. However, this resulted in no significant difference
with respect to results in terms of performance. We also explored the
correlation between the average rating a person gave themselves on the VSI
scale and their results, using a Kendall correlation due the non-normal nature
of the data. Using a range of 0.26 to 0.49 as a definition of moderate (taken
from (wic, 2023) which was derived from the Pearson correlation
classifications of Schober and Schwarte (Schober and Schwarte, 2018)), there
is a moderate correlation between the Average of an individual’s VSI score and
their average score for the SSM questions across all visualization types
(Kendall coefficient of 0.262).

Table 4. Moderate Kendall correlation with the Average VSI score of each
participant.

A table showing moderate Kendall correlation with the Average VSI score of
each participant for 7 metrics  Result Name Kendall Correlation Coefficient
Performance Time - Continuous 0.373173 Average SSM score - Continuous 0.329774
Performance Time - Banded 0.305324 Performance Time - Hexagon 0.305324 Q01
Response - Circle 0.294504 Q01 Response - Continuous 0.265854 Average SSM
score (All visualizations) 0.261895

We considered the results of the SSM questionnaire (average of the answers)
and the results of performance in terms of time for each visualization, as
well as how the participant answered to Q01, Q02 and Q03, concerning the
awareness or the virtual, physical, and balance of both. There were 6 moderate
correlations, the strongest being with performance time. This can be
interpreted as the higher the persons rating in the VSI pre-questionnaire, the
more likely they were to take longer at the trials for the continuous
visualization type. They were also to more likely rate it higher on average in
the questions for the MEC SPQ. Considering Q01 response, again Continuous had
a moderate correlation. The continuous visualization is the one with the most
coverage of the mesh and largest set of colors. The banded and hexagon
visualizations also had a moderate correlation, and these two visualizations
also cover most of the physical world. It appears that those with higher VSI
self-ratings are more likely to take longer, particularly for the
visualizations that have the most coverage of the spatial awareness mesh.
However, the reason behind this is unclear, and further research is required
before drawing any firm conclusions.

###  7.6. Experiment Hardware and Limitations

The HoloLens 2 is an OST headset. However, as devices improve in terms of
resolution, processing power, and ergonomic profile, pass-through AR may be
more viable option, and additionally it has its own benefits. With OST devices
environmental illumination impacts perception and there is no difference
between rendered black and full transparency (Erickson et al., 2020). Using
transparency is equivalent to making a color darker and the device has no
concept of the real-world color underneath the pixel for alpha blending.
Despite the limited effect of the transparency, it still did not make it
significantly worse than any of the other visualizations. However, a pass-
through AR device would more easily allow more complex blending of colors and
better use of transparency, using the alpha blending functionality of the
graphics hardware. This may be useful with respect to concerns about contrast
for the stencil visualizations which were specifically mentioned, by one
participant. However, recent work by Zhang _et al._ (Zhang et al., 2022)
looked at providing improved color contrast between virtual and real objects
using an OST device. They capture background video from the device and blur it
to use as a background color to improve color contrast. Such a technique could
be explored to help resolve any possible contrast issues with stencils, to
improve color blending for transparency, and to improve perceptual consistency
of the radiation visualization across a variety of scene backgrounds. Such an
approach may have a trade off in performance and would need to be evaluated
thoroughly for this use case. Zhang _et al._ do note that that software-based
solutions still have issues overcoming hardware limitations of OST-HMDs, for
example, for drawing black or darker colors. Langlotz _et al._ (Langlotz et
al., 2016) suggest an approach for mitigating the effects of undesired color-
blending for OST-HMDs, however it depends on custom novel prototype HMD, and
is not applicable to commodity HMDs.

###  7.7. Study Design

This system is visualizing data with the primary aim of influencing the
physical movement of users. This differs from the more traditional
visualization techniques where the objective is more concentrated on purely
understanding the data. Here the data must first be understood, then the user
should move around accordingly. In this study there was no interaction with
the underlying data. These factors perhaps shaped the user behavior and hence
the results.

The results were often not significant between each condition. However, we do
note that one of our own visualization questions provided a significant
difference, even if it was not part of a hypotheses. The visual spatial
imagery questionnaire provided some moderate correlations, thus pointing to it
having some relevance. Other results were largely non-significant, but the
approaches were chosen as they are already widely used in the mixed reality
community or in the wider domain of situation awareness. Given these points,
we feel there is a specific need for a situated visualization questionnaire
which can more accurately explore the impact of the visualization not only on
understanding the data, but also the relationship between levels of situation
awareness, and the ultimate effects on human movement behavior.

While the experimental conditions changed (visualizations) the room layout did
not. Therefore, it is not possible to say if the room layout itself was a
factor in the largely non-significant results. Furthermore, the number of test
conditions was relatively large, combined with the fact that except for the
card task, the scenario was largely static. All these combined with a simple
task, and an identical room layout probably influenced user behavior. These
aspects should be examined in more detail through further studies.

The motivation of the card tasks was to force the user to search for a real-
world item, showing they could perceive both the real and the virtual.
However, the 52 degree AR display area of the HoloLens 2 means that a user may
be able to view the real-world elements (such as the card table) without
looking through the AR portion of the display. Participants were instructed to
only look for the card through the AR display, however it was not possible to
strictly enforce this, as it was not possible for the experimenter to be in
the room close to the participant. One participant even made the comment “
_the headset was a bit small and the face of the cards could be seen directly
by looking down_ ”.

In our experiment a single source had a radius of 2 meters. This was to avoid
having visualizations completely dominate the experiment room. Looking at the
mean nearest source distance in Figure 10(d) it can be seen that users in
general kept quite far, with the median mean distances being in the range of
1.8 to 1.9 meters. It may be possible that participants were using the edges
of the visualization as a barrier to guide them. One participant did comment
that the arrow visualization made it “ _difficult to find the boundary_ ” and
that banded visualization allowed then “ _to better see the limit of the
radiation_ ”, which suggests that the visualization boundary was an important
feature for them. Adding clearly defined boundaries is a possible future
design feature for the arrow based visualizations.

###  7.8. Recommendations and Lessons Learned

Based on our experiences of the experiment, we make recommendations in the
areas of visual encoding, study design, and individual differences.

####  7.8.1. Visual Encoding

In terms of visual encoding more information does not necessarily result in
better performance. The inclusion of arrows explicitly specifying direction
did not improve performance significantly. It also had no negative impact
either. Therefore, it may be included as part of a visualization, but the
context of the visualization and the preferences of the specific users of a
system should be considered and balanced against the cost of implementation
and processing time.

####  7.8.2. Study Design

The study was carefully designed to ensure that participants used information
from the visualizations to navigate. However, visualization that affects
behavior may need a different approach to visualization that imparts
knowledge. Our participants typically completed the tasks very quickly,
limiting their exposure time. While this is desirable, it remains to be seen
if the same duration would arise when experts use the system. Furthermore, the
short times resulted in a small effect size for time, and therefore radiation
dose, making statistical difference more difficult to achieve as part of an
evaluation. We recommend exploring tasks where the desired behavior
modification can be identified more explicitly. It may be worth examining if
an effect size can be detected when undertaking a radiation source type
identification scenario, in the context of CBRN response.

Additionally, the high number of trials conducted means that participants may
have desired from a purely personal and non-CBRN level to avoid boredom and
complete the task as quickly as possible. In future, we may explore fewer
variations and iterations of the trial for each user, plus explore a longer
task. However, this is quite challenging, as adding more trials or lengthening
trial duration to improve the effect size may have other negative impacts on
results. The study did not contain a no visualization option, this was a
design choice. However, it means that we are not able to assess the
effectiveness of visualization in general.

Choice of questions plays an important role, asking the wrong questions may
obscure an interesting result. Due to our focus on situation awareness we
decided to adapt questions from established questionnaires in the literature.
In retrospect our choices should have been more focused on our specific
experiment. In Figure 8, it can be seen that, generally, the custom questions
for this experiment have a wider spread of results, and that the MEC and SART
questions were less effective at distinguishing the visualizations.
Additionally, the pre-questionnaire was focused on VSI ability, however other
questions could have been included, for example relating to experience with
video games. One participant mentioned they found the hexagons less intrusive,
but this may have been because of “ _some hexagon based games_ ” they had
played.

####  7.8.3. Individual differences need more exploration.

This study did not go into detail on individual differences, either through
conducting spatial ability tests, or analyzing multiple groups based on the
MEC VSI questionnaire. However, the ranking data points to their being strong
differences in preferences, with some binary distributions occurring.
Therefore, a more thorough study which explores the role of individual
differences is required. The comments from users indicated some designs and
levels of transparency may have had an impact on task behavior. While this was
not noted by all users, it points to the need perhaps to change certain
features or perhaps allow customization depending on user preference. For
example, it is not clear if all users (there may be many) require the same
visualization technique. Instead, they could choose one which they find the
most effective, and/or perhaps have customization options e.g., arrow size,
transparency etc. Some user comments, such as continuous visualization
obscuring the card table, were not surprising. However, other aspects such as
the bimodal distribution of preference rankings for some visualizations, and
the comment about the arrows not making radiation boundaries clear were
surprising and will help steer future work.

##  8\. Conclusions

At the technical level, we have described an approach that allows users in AR
to visualize data in the physical environment around them leveraging the
spatial awareness mesh. We have demonstrated that GPU based shading of the
spatial awareness mesh a technically viable approach to visualizing data in
real time in a training scenario. Our fundamental approach may be applied to
other use cases beyond our scenarios for radiological incident response.

We designed and performed a user experiment to compare visual encodings with
the aim of understanding which approach could help the user maintaining
awareness of both the virtual threat and the physical world in a balanced
fashion. Our results show that enhancements to the fundamental approach of
coloring the spatial awareness mesh to improve user performance and balance
perception of the virtual and physical environment do not make a significant
difference. However, visualizing data in a spatial environment is challenging
and more research is required to understand the confounding aspects that might
be at play. Individual differences, between participant in experiments may
need to be better understood. Finally, we have a detailed discussion around
evaluation approaches for in-situ visualization.

## References

  * (1)
  * For (2023) 2023\.  ForgeFX: Augmented Reality device training simulators for enhanced warfare training.  https://forgefx.com/simulation-projects/augmented-reality-device-training-simulators/ https://forgefx.com/simulation-projects/augmented-reality-device-training-simulators/.
  * wic (2023) 2023\.  Weak or strong? How to interpret a Spearman or Kendall correlation.  https://blogs.sas.com/content/iml/2023/04/05/interpret-spearman-kendall-corr.html https://blogs.sas.com/content/iml/2023/04/05/interpret-spearman-kendall-corr.html.
  * Amburn et al. (2018) Charles Amburn, Christopher Garneau, Michael Boyce, Paul Shorter, and Nathan Vey. 2018\.  The Augmented Reality Sandtable (ARES) research strategy.  _US Army Research Laboratory_ (2018).  Issue ARL-SR-0340.
  * Bach et al. (2018) Benjamin Bach, Ronell Sicat, Johanna Beyer, Maxime Cordeil, and Hanspeter Pfister. 2018.  The hologram in my hand: How effective is interactive exploration of 3D visualizations in immersive tangible Augmented Reality?  _IEEE Transactions on Visualization and Computer Graphics_ 24, 1 (2018).  https://doi.org/10.1109/TVCG.2017.2745941
  * Buschel et al. (2021) Wolfgang Buschel, Anke Lehmann, and Raimund Dachselt. 2021\.  Miria: A mixed reality toolkit for the in-situ visualization and analysis of spatio-temporal interaction data.  _Conference on Human Factors in Computing Systems - Proceedings_.  https://doi.org/10.1145/3411764.3445651
  * Calepso et al. (2023) Aimee Sousa Calepso, Philipp Fleck, Dieter Schmalstieg, and Michael Sedlmair. 2023.  Exploring Augmented Reality for Situated Analytics with Many Movable Physical Referents. In _ACM Symposium on Virtual Reality Software and Technology - Proceedings_ _(VRST ’23)_. Association for Computing Machinery, New York, NY, USA.  https://doi.org/10.1145/3611659.3615700
  * Carmo et al. (2017) Maria Beatriz Carmo, Ana Paula Cláudio, António Ferreira, Ana Paula Afonso, Paula Redweik, Cristina Catita, Miguel Centeno Brito, Silvana Silva, and Carolina Meireles. 2017\.  Augmented reality for support decision on solar radiation harnessing.  _Encontro Portugues de Computacao Grafica e Interacao, EPCGI 2016_.  https://doi.org/10.1109/EPCGI.2016.7851201
  * Endsley (1988) Mica Endsley. 1988\.  Situation Awareness Global Assessment Technique (SAGAT).  _IEEE National Aerospace and Electronics Conference - Proceedings_ , 789–795.  https://doi.org/10.1109/NAECON.1988.195097
  * Erickson et al. (2020) Austin Erickson, Kangsoo Kim, Gerd Bruder, and Gregory Welch. 2020\.  Exploring the limitations of environment lighting on Optical See-Through Head-Mounted Displays.  _ACM Symposium on Spatial User Interaction , SUI - Proceedings_.  https://doi.org/10.1145/3385959.3418445
  * Feiner et al. (1993) Steven Feiner, Blair Macintyre, and Dorée Seligmann. 1993\.  Knowledge-based augmented reality.  _Commun. ACM_ 36 (7 1993), 53–62.  Issue 7. https://doi.org/10.1145/159544.159587
  * Fleck et al. (2023) Philipp Fleck, Aimee Sousa Calepso, Sebastian Hubenschmid, Michael Sedlmair, and Dieter Schmalstieg. 2023\.  RagRug: A toolkit for situated analytics.  _IEEE Transactions on Visualization and Computer Graphics_ 29, 7 (2023).  https://doi.org/10.1109/TVCG.2022.3157058
  * Garnier et al. (2021) Simon Garnier, Noam Ross, Robert Rudis, Antônio Pedro Camargo, Marco Sciaini, and Cédric Scherer. 2021\.  {viridis} - Colorblind-friendly color maps for R.  https://doi.org/10.5281/zenodo.4679424 R package version 0.6.2.
  * Guarese et al. (2021) Renan Guarese, Pererik Andreasson, Emil Nilsson, and Anderson Maciel. 2021.  Augmented situated visualization methods towards electromagnetic compatibility testing.  _Computers & Graphics_ 94 (2021), 1–10.  https://doi.org/10.1016/j.cag.2020.10.001
  * Ismael et al. (2023) Muhannad Ismael, Roderick McCall, Maël Cornil, Mike Griffin, and Joan Baixauli. 2023.  Radiological Incident System using Augmented Reality (RISAR).  _IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW - Proceedings_.  https://doi.org/10.1109/VRW58643.2023.00138
  * Kalkofen et al. (2011) Denis Kalkofen, Christian Sandor, Sean White, and Dieter Schmalstieg. 2011.  Visualization techniques for Augmented Reality.  In _Handbook of Augmented Reality_. 65–98.  https://doi.org/10.1007/978-1-4614-0064-6_3
  * Langlotz et al. (2016) Tobias Langlotz, Matthew Cook, and Holger Regenbrecht. 2016\.  Real-time radiometric compensation for Optical See-Through Head-Mounted Displays.  _IEEE Transactions on Visualization and Computer Graphics_ 22, 11 (2016), 2385–2394.  https://doi.org/10.1109/TVCG.2016.2593781
  * Lee et al. (2023) Benjamin Lee, Michael Sedlmair, and Dieter Schmalstieg. 2023\.  Design patterns for situated visualization in Augmented Reality.  _IEEE Transactions on Visualization and Computer Graphics_ Early Access (2023), 1–12.  https://doi.org/10.1109/TVCG.2023.3327398
  * Leucht et al. (2015) Nicola Leucht, Séverine Habert, Patrick Wucherer, Simon Weidert, Nassir Navab, and Pascal Fallavollita. 2015\.  [POSTER] Augmented Reality for radiation awareness.  , 60-63 pages.  https://doi.org/10.1109/ISMAR.2015.21
  * Liu and Heer (2018) Yang Liu and Jeffrey Heer. 2018.  Somewhere over the rainbow: An empirical assessment of quantitative colormaps.  _Conference on Human Factors in Computing Systems - Proceedings_ 2018-April.  https://doi.org/10.1145/3173574.3174172
  * Meireles et al. (2023) Carolina Meireles, Maria Beatriz Carmo, Ana Paula Cláudio, António Ferreira, Ana Paula Afonso, Paula Redweik, Cristina Catita, Miguel Centeno Brito, and Daniel Soares. 2023.  Mobile Augmented Reality for analysis of solar radiation on facades. In _International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP - Proceedings_. INSTICC, SciTePress, 131–142.  https://doi.org/10.5220/0011886800003417
  * Microsoft (2024) Microsoft. 2024\.  Mixed Reality ToolKit (MRTK).  https://docs.microsoft.com/en-us/windows/mixed-reality/mrtk-unity/mrtk2
  * Pakari et al. (2023) Oskari Pakari, Ricardo Lopez, Ivan Druckman, Emilee Meng, Erik Zhou, Ziang Wang, Shaun D. Clarke, and Sara A. Pozzi. 2023\.  Real-time mixed reality display of dual particle radiation detector data.  _Scientific Reports_ 13 (2023).  Issue 1. https://doi.org/10.1038/s41598-023-27632-1
  * Reda and Szafir (2021) Khairi Reda and Danielle Albers Szafir. 2021.  Rainbows revisited: Modeling effective colormap design for graphical inference.  _IEEE Transactions on Visualization and Computer Graphics_ 27, 2 (2021).  https://doi.org/10.1109/TVCG.2020.3030439
  * Rowden et al. (2022) Alexander Rowden, Süleyman Aslan, Eric Krokos, Kirsten Whitley, and Amitabh Varshney. 2022\.  WaveRider: Immersive visualization of indoor signal propagation.  _ACM Conference on Spatial User Interaction, SUI - Proceedings_.  https://doi.org/10.1145/3565970.3567689
  * Schober and Schwarte (2018) Patrick Schober and Lothar Schwarte. 2018.  Correlation coefficients: Appropriate use and interpretation.  _Anesthesia and Analgesia_ 126 (2018).  Issue 5. https://doi.org/10.1213/ANE.0000000000002864
  * Sicat et al. (2019) Ronell Sicat, Jiabao Li, Junyoung Choi, Maxime Cordeil, Won Ki Jeong, Benjamin Bach, and Hanspeter Pfister. 2019.  DXR: A toolkit for building immersive data visualizations.  _IEEE Transactions on Visualization and Computer Graphics_ 25, 1 (2019).  https://doi.org/10.1109/TVCG.2018.2865152
  * Smith et al. (2022) Michael Smith, Scott Greenwood, Noel Nelson, Nick Thompson, and Douglas Peplow. 2022.  Virtual Interaction with Physics Enhanced Reality (VIPER): Using Augmented Reality to visualize and interact with ionizing radiation data.  _Transactions of the American Nuclear Society_ 126, 1 (June 2022), 805–808.  https://www.osti.gov/biblio/1879962
  * Taylor (1990) Russel M. Taylor. 1990\.  Situation Awareness Rating Technique (SART).  _Advisory Group for Aerospace Research and Development (AGARD) Conference Proceedings - No. 478_ (1990).
  * Vlahakis et al. (2002) Vassilios Vlahakis, Nikolaos Ioannidis, John Karigiannis, Manolis Tsotros, Michael Gounaris, Didier Stricker, Tim Gleue, Patrick Daehne, and Luís Almeida. 2002\.  Archeoguide: an augmented reality guide for archaeological sites.  _IEEE Computer Graphics and Applications_ 22 (2002), 52–60.  Issue 5. https://doi.org/10.1109/MCG.2002.1028726
  * Vorderer et al. (2004) Peter Vorderer, Werner Wirth, Feliz Gouveia, Frank Biocca, Timo Saari, Lutz Jäncke, Saskia Böcking, Holger Schramm, Andre Gysbers, Tilo Hartmann, Christoph Klimmt, Jari Laarni, Niklas Ravaja, Ana Sacau, Thomas Baumgartner, and Petra Jäncke. 2004\.  MEC spatial presence questionnaire (MEC-SPQ, English and German version): Short documentation and instructions for application.  _Report to the European Community, Project Presence: MEC (IST-2001-37661)_ (06 2004).  https://doi.org/10.13140/RG.2.2.26232.42249
  * White and Feiner (2009) Sean White and Steven Feiner. 2009.  Sitelens: Situated visualization techniques for urban site visits.  _Conference on Human Factors in Computing Systems - Proceedings_.  https://doi.org/10.1145/1518701.1518871
  * Zhang et al. (2022) Yunjin Zhang, Rui Wang, Yifan Peng, Wei Hua, and Hujun Bao. 2022.  Color contrast enhanced rendering for Optical See-Through Head-Mounted Displays.  _IEEE Transactions on Visualization and Computer Graphics_ 28, 12 (2022), 4490–4502.  https://doi.org/10.1109/TVCG.2021.3091686
  * Zollmann et al. (2010) Stefanie Zollmann, Denis Kalkofen, Erick Mendez, and Gerhard Reitmayr. 2010.  Image-based ghostings for single layer occlusions in augmented reality.  _IEEE International Symposium on Mixed and Augmented Reality (ISMAR) - Proceedings_.  https://doi.org/10.1109/ISMAR.2010.5643546