Determining device position through minimal user input
McNaughton et al. Hum. Cent. Comput. Inf. Sci.
Determining device position through minimal user input
James McNaughton 1
Tom Crick 0
Andrew Hatch 1
0 Department of Computing & Information Systems, Cardiff Metropolitan University , Western Avenue, Cardiff CF5 2YB , UK
1 School of Education, Durham University , South Road, Durham DH1 3LE , UK
In many co-located, collaborative systems there is a need for the constituent devices used to be aware of the physical positions of their networked counterparts. This paper addresses this challenge by presenting a novel method of utilising users' judgement of direction to obtain the location and orientation of a touch interface. The technique requires a user to draw several arrows on an interface which point towards physical landmarks in an environment. This allows for the setup of interface locations in a way which is (i) quick; (ii) inexpensive; (iii) not encumbering; and (iv) capable of being performed despite obstructions in the environment. A user study is presented which investigates what influence a user's accuracy has on the technique's resulting calculated location of an interface. The study reveals that the magnitude of a user's inaccuracies is proportional to the size of the error in the result and that there is no improvement in user accuracy with practice. Finally, we make observations on the future extension and application of this technique.
User input; Positioning; Device location; Touch devices; Collaboration; HCI
Direct-touch interfaces provide an effective digital medium in which people can
collaborate on a broad range of tasks [
]. Previous research regarding collaboration on
direct-touch interfaces has generally focused on the collaboration between multiple
participants across a single interface [
]. However, when multiple interfaces capable of
interacting with each other are used, an opportunity arises for collaboration between
users interacting with different interfaces; this use of multiple interface types in a shared
environment can be beneficial for collaboration [
The physical locations of interfaces can be used to aid tasks involving interaction
between networked devices. One example of this is demonstrated in the SynergyNet
project, a software framework built for use on direct-touch interfaces, specifically
multitouch tabletops, which allows users to perform a flicking gesture to transfer content [
Users flick a content item in the direction of the interface to which they wish to send
content; the item travels off the side of the source interface and appears on the
target interface. When the item arrives on the target interface, the framework can use its
knowledge of the interface locations to ensure the item travels into view from the
direction of the source interface. This is intended to aid users in identifying from where newly
arrived content items were sent.
Projects such as SynergyNet showcase the need for an interface to have knowledge
of its location relative to networked interfaces surrounding it. Another example of the
benefits of an interface knowing its location originates from a system which uses
multiple projectors where projected outputs overlap. The outputs are seamlessly stitched
together by the system to give the appearance of a single projection. In order for this to
be achieved the system must have information regarding the relative positions of each
projected output. The stitching of multiple projector outputs to create large public
displays is becoming much more common [
]; furthermore, each system which utilises the
stitching of visual outputs needs a method of attaining their locations.
With systems requiring knowledge of the locations of their interfaces, a simple and
effective method of obtaining this information is required. It is possible to measure the
location and orientation of an interface using physical tools such as rulers and
protractors; because of the time consuming nature of this manual measurement strategy it is
best suited for an environment in which the interfaces remain in a fixed position for long
periods of time. However, there are scenarios in which the interfaces may be moved on
a regular basis. For example, the SynergyNet project is intended for educational
environments; these classroom environments are physical spaces in which furniture is
frequently moved or rearranged to accommodate different learning activities during the
course of a day [
]. It is therefore likely that any interfaces in the environment used by
the system will not remain in fixed positions. Measuring the locations and angles of each
interface in the environment directly (i.e. with measuring tapes, rulers and/or
protractors), then inputting this information into the system will take time on every
reconfiguration of the environment. In this time the system will not function as intended because
its knowledge of interface positions will be incorrect.
Incorrect knowledge of interface positions is problematic for systems which use the
information to stitch multiple visual outputs [
]. The image displayed by a repositioned
interface would no longer align with the output from other linked interfaces and
therefore would not be appropriately stitched. Therefore, a method of obtaining the position
of an interface quickly, without the need for time consuming measurements, is required.
Obtaining the position of an interface can be achieved through a variety of technological
means. The use of RFID chips [
] is one such technological approach. These devices are
inexpensive and can be used to obtain the positional information of the object to which
they are attached. However, the accuracy of locations given by RFID chips is
dependent on the number of sensors in an environment—despite the relatively low cost of the
chips, the large number of sensors needed for accurate readings can be expensive. Also,
the addition of more sensors requires the system to spend more time compiling the
positional information for each RFID chip detected [
]. The same trade-off between
expense and accuracy is also present for similar technologies using electromagnetic
frequencies, such as wi-fi [
Infra-red sensors can be used to obtain the locations of interfaces [
]. By detecting
the relative location and strength of known infra-red light sources, a device with
infrared sensors can determine its position. However, the technology is extremely sensitive
and small changes in the ambient light level can result in the calculated location varying
from the sensors’ actual locations to a significant degree [
]. This technology can also
be affected by obstructions; an object blocking an infra-red sensor’s view of one or more
infra-red light sources can result in the system obtaining incorrect positional
information. Therefore, for this technology to be used in a system, the environment must be
clear of obstructions and have a consistent ambient light level. These two restraints
make this technology unsuitable for a number of potential usage scenarios.
Visible light can also be used for detecting the location of an interface [
]. Using light
sensing technology, an interface can detect its location by using patterns projected from
a light source. However, similar to infra-red sensors, these visible light sensors require a
clear view of as much of the projected light pattern as possible; if obstructed, a sensor’s
reading would also result an inaccurate calculation of its location.
Visual markers called fiducials [
], which are often used in augmented reality
systems, could also be used for obtaining the position of an interface. A camera is used
to identify and locate the markers which each carry a unique pattern recognisable by
machine vision. Several markers positioned on or around an interface could be located
by a fiducial recognition system. However, this technology requires a clear line of sight
between the camera and the fiducials. Like other location sensing technologies [
], obstructions around the interface can cause inaccurate results.
When attempting to obtain the location of an interface, an alternative to using sensing
technologies is to utilise a technique driven by user input. An example of this approach
is where users measure the locations of the interface directly and input the information
into the system through text entry. User input approaches have the disadvantage of
relying on the accuracy of the human input, whereas sensing technologies can be relied on
to be precise within certain parameters. The accuracy of users can be influenced by a
range of factors which would not affect the accuracy of sensing technologies, such as the
magnitude of the distances being measured [
]. An inaccurate user-generated
measurement, input as part of a location determining technique, would result in the calculated
position of the interface being incorrect. Therefore, it is important to take into
consideration the accuracy of human-generated input when utilising techniques which rely on
Observations on each technique discussed in this section demonstrates that each has
strengths and weaknesses. For example, a number of technology-based location
sensing techniques discussed [
] will produce inaccurate positional information if
obstruction of their sensing components occurs. This is undesirable in any scenarios
where obstruction may frequently occur. For example, environments in which many
users may be present around the interfaces would be unsuited to these location-inferring
technologies; for example, a user may stand between the sensors used by the
technologies to calculate positional information.
Table 1 compares the attributes of the position obtaining techniques discussed in this
section. Each attribute listed in the comparison is derived from observations relating to
the strengths and weaknesses of the techniques;
• If a technique is able to give reliable results despite obstructions surrounding an
interface it is deemed Obstruction Tolerant;
• If the time taken to perform a technique is less than the time taken to measure the
locations directly by hand it is deemed Quick;
• If a technique produces usable positional information it is deemed Accurate;
• If a technique does not require additional hardware to be purchased it is deemed
• If a technique does not require an additional physical device to be attached to the
interface it is deemed to be Not Encumbering.
For each approach discussed in this section a mark is given under each heading to which
it conforms. The significance of the list required attributes of each position obtaining
technique is strengthened through their similarity to requirements often outlined for
human–computer interaction [
], user-centered modelling [
] and natural user
] design. These are both domains which often encompass investigations into, and
usage of, position obtaining techniques.
Table 1 shows that none of the position-obtaining techniques discussed are
without a weakness. The majority of the technology-based techniques have issues
regarding obstruction which makes them unsuitable for use in scenarios where the interfaces
may have obstructions between them, such as users. However, the techniques which
do not have the issue of obstructions that utilise RFID chips and direct measurements,
also have weaknesses that make them unsuitable for this scenario. RFID tags may be too
inaccurate or expensive to use and direct measurement would require a comparatively
significant amount of time for re-measurement whenever the interfaces are moved.
A new approach is thus needed for scenarios such as the classroom example given in
“Introduction” section, where accurate measurements of the interface positions must be
performed quickly with many users populating the environment. As we have
demonstrated, the technique is required to be obstruction tolerant, quick, accurate,
inexpensive and not encumbering. Our approach, which potentially fulfils these requirements, is
presented in the following section.
It is clear that the presence of users can prove to be a major disruption to many location
sensing technologies; we thus seek a technique which can not only continue to work as
intended with users present but can utilise their presence. In accordance with this
observation, we propose a novel technique utilising user input that can determine an
interface’s location and orientation in a physical environment. This technique can employ
users’ mobility and sense of direction to overcome any obstructions which may have
caused technological alternatives to produce inaccurate results.
The technique utilises two physical landmarks which share the same environment as
the interfaces being located. The distances between these two landmarks must be made
known to the system. Any of the resulting calculated positions and orientations from
the technique are relative to the landmarks. The technique requires users to draw three
arrows which relate to the locations of the landmarks.
The first of the three arrows used in the technique determines the orientation of the
interface on which it is drawn. The user draws the arrow parallel to the imaginary line
between the two landmarks. The angle between this arrow and the local y-axis of the
interface, θ, represents the orientation of the display as shown in Fig. 1. The user should
draw this orientation arrow in the same direction on all the interfaces being located. θ can
then be used to create a vector representing the real-world environment’s y-axis locally
on the interface; this can then be used in calculating the interface’s physical position.
After drawing the orientation arrow, the user is required to draw two further arrows
which point directly to the landmarks (see Fig. 2); each of the arrows must point towards
a separate landmark. The angles of the user drawn arrows from the world’s y-axis, α and
β, are used to determine the values used in calculating the location of the interface. For
each of these two arrows, the angle between the local y-axis of the interface and the
arrow is summed with θ to derive α and β.
The angle between the two arrows, S, is used in conjunction with the angle between
one of the arrows and the local representation of the real world environment’s y-axis, T,
to determine the location of the interface. Algorithm 1 outlines the calculations involved
in determining the x and y values of the interface’s centre point in the physical
environment. The resulting values are relative to Landmark A’s position as shown in Fig. 2.
This technique has a number of benefits over the alternatives of sensing technologies
and direct measurements. The technique only needs one known value before use; this
is the distance between the landmarks. Since it is a requirement of the technique that
these landmarks are not moved, this value will not need to be re-measured. Therefore,
the technique can be performed relatively quickly once this measurement is obtained in
comparison to measuring the locations of the interfaces directly. This ability to be
performed quickly without the need for repeated, time consuming measurements makes
the technique suitable for any scenarios where the interfaces may be moved regularly,
such as educational environments. Because of this the technique can be deemed Quick.
Furthermore, the technique does not rely on any additional technologies, so there is
no extra cost for its implementation into a system. This independence from additional
hardware ensures that the technique is Inexpensive and Not Encumbering. Also, due to
the technique’s dependence on user inputs, rather than technology, it is made suitable
for use in environments where there may be numerous obstructions, such as the users
themselves, around the interfaces. If any immovable obstructions are present in the
physical space between the interface and the reference points, a user can utilise their
knowledge of the environment to make an informed placement of an arrow. Therefore,
the technique can be deemed Obstruction Tolerant.
The technique’s design also allows for its use on interfaces which do not utilise
directtouch inputs. However, the ability to directly manipulate the arrows may be
advantageous when trying to achieve an alignment between an on-screen arrow and a physical
landmark. The act of aiming towards reference points allows for a form of direct
feedback where the user can adjust the arrow until they believe they have the correct
alignment. An indirect input device may draw a user’s attention away from the interface,
interrupting their concentration when aligning the arrows with landmarks. The
technique’s suitability for direct-touch interfaces is enhanced by the fact that text entry,
which would be required with other user input based techniques, through touch
interfaces can be problematic due to their lack of tactile feedback [
This technique has been presented as an obstruction tolerant, quick, inexpensive and
not encumbering solution. However, its dependence on the accuracy of a user’s
judgement of direction has the implication that the positional information it produces may
not be accurate. The inaccuracies in a user’s input into the technique will result in the
calculated position of the interface deviating from the interface’s actual location. This
deviation between the calculated and actual interfaces locations could make this
technique too inaccurate for use in some scenarios. Therefore, it is important to discover
how a user’s inaccuracies when performing the technique affect the resulting value.
A study was conducted to determine how a user’s accuracy affected the error of the
presented technique’s calculated interface positions. The study focused on discovering
whether the results given by the technique could be accurate enough for use in a specific
scenario. The technique was implemented into a software framework and deployed on
four touch-screen tablet interfaces.
Implementation of the technique
Software for the study was constructed using the SynergyNet multi-touch software
]. The framework utilises a number of third-party libraries to support a
wide range of functions such as networking, touch gesture recognition and multimedia
support. The technique was implemented as part of an application within the SynergyNet
framework which allowed for it to utilise a touch-based input.
The arrows used in the implemented technique were designed to always originate from
the centre of the interface. The tail of an arrow remained in the centre of the interface
while the participant could drag the arrowhead to any location on display. The arrow
being manipulated would therefore point from the centre of the interface towards the
location of a participant’s last relevant touch (“relevant” being defined as a touch within
a certain distance of the arrowhead); this allowed participants to determine the
direction and length of the arrow. The participant could reposition an arrowhead as much
as they wanted. Once a participant has finished establishing an arrow’s direction they
would then be expected to press a button on the interface to confirm their arrangement.
The technique first asked participants to draw a single arrow—this corresponds
to Arrow 1 in Fig. 1—which is used to establish the interface’s orientation. Once the
participant confirmed their arrangement of this arrow they would then be asked to
position two arrows together; these are Arrow 2 and Arrow 3 as shown in Fig. 2. While each
of the arrows for this stage of the technique is intended to point towards a landmark,
the target for each arrow is not made explicit to the participant. As long as each of the
two arrows points at a different landmark it does not matter to which landmark they
point. The assumption is made in this implementation that all the interfaces are
positioned in the space to the right of the line heading from landmark A to B. Therefore, the
arrow with the smallest clockwise angle from the environment’s y-axis is Arrow 2. With
this known, Arrow 3 is known through the process of elimination. When a participant is
satisfied with the positioning of both these arrows they are then asked to confirm their
placement to complete the approach.
The tablet interfaces used in the study were positioned in the configuration shown in
Fig. 3. This configuration was chosen to maximise the use of the four available interface
devices in a non-symmetrical layout. The orientations were chosen to include (i)
orientations in line with the room’s coordinate system (interfaces 1 and 3), reflection (between
interfaces 1 and 3) and orientations not parallel to the room’s coordinate system
(interfaces 2 and 4).
Video recordings of the participants using the technique were made so that
information regarding the timings and possible mistakes could be observed. The application was
designed to record the local angle of any of the arrows drawn by a participant. Any of the
values used by the technique to calculate an interface’s location were also recorded along
with the resulting positional information.
Before the study took place, multiple direct measurements of the table’s positions and
orientations were made by the study organisers to confirm the true locations. Using
these values and the technique’s calculations the angle of the arrows which would
produce perfectly accurate positional information could be derived. Comparing these
optimal angles with the angles of user drawn arrows allowed for a participant’s inaccuracy to
The four tablet interface used in the study were identical. Each tablet interface had a
resolution of 1024 by 768 displayed on a 247 mm by 185 mm screen.
Two hypotheses were proposed prior to the study:
The error of the technique’s result is proportional to the error of user
To support or disprove this hypothesis, the optimal angles of an interface were
compared with the α, β and θ angles of the arrows drawn by the participants for each
execution of the technique. The deviation of a participant’s arrows from their optimal angles
could then be compared to the difference between the corresponding interface’s
calculated and actual location information (i.e. position and orientation).
As participant’s gain experience with the technique, their accuracy
This hypothesis was derived from the observation that as users gain experience with
an interaction technique their performance improves [
]. If user accuracy is found to
influence the technique’s result it is important to understand changes on it caused by
practice. The order of the tables on which the technique was performed was changed
between participants. This allowed any influences regarding the positioning of the
interfaces to be distinguished from any learning effect the technique may have.
13 participants took part in the study. Each participant performed the technique four
times—once on each interface—resulting in 52 instances of the technique being executed.
All the participants were right-handed males who used computers daily and had at least
some prior experience using the stylus interaction employed by the tablet interfaces.
Figure 4 shows the average deviation of each participant’s arrows from their
optimal equivalents against the resulting positional information’s average deviation from
the actual location and orientation. Arrow 1 in the graph represents the arrow used to
collect a device’s orientation. Arrow 2 and Arrow 3 represent the arrows used to point
towards the two landmarks. The graph in Fig. 4 indicates a general trend that as the
average deviation from the optimum increases, the difference between an interface’s actual
and calculated positions increases. This supports Hypothesis 1 as a proportional
relationship between the participant’s inaccuracy and the error of the technique’s result is
demonstrated by the graph. As noted on the graph there is a set of outlying data which
does not conform to this trend; this is discussed in “Discussion” section.
Figure 5 shows the average deviation of participant drawn arrows from their
equivalent optimal angles over the number of times a participant has performed the technique.
If Hypothesis 2 was correct, the mean of participants’ inaccuracies should decrease as
the participants’ experience with the technique increases. However, the graph shows
there is no discernible improvement for any of the participant drawn arrows over the
number of attempts made. This indicates that there was no learning effect and that
experience with the technique does not improve a participant’s accuracy. The evidence thus
indicates that Hypothesis 2 is incorrect.
As identified in “Results” section, there is a subset of eight results which do not conform
to the general trend of the data. These outlying results are circled in Fig. 4 outside the
grouping of the majority of data. The data outside this subset of results implies that there
is a proportional relationship between a user’s accuracy and the error of the result.
However, these outlying results represent instances where a participant has been relatively
inaccurate in comparison to other executions of the technique but a position with little
deviation from the interface’s actual position has been calculated. This implies that the
relationship between a user’s inaccuracies and the error of the technique’s result is more
complex than Hypothesis 1 states.
It is possible that one of the arrows used by the technique as an input has a greater
influence over the result than the others. The points in the graph shown in Fig. 6
represent deviation of individual participant-drawn arrows from their corresponding
optimum angles for all 52 instances of the technique being performed. The graph
highlights the general trend of participant’s total inaccuracy increasing with the error of the
result (ignoring the outliers previously discussed). If any particular arrow input into the
technique has a greater influence than the others then a relationship between the
inaccuracies of the arrow and the result would be apparent. For example, if Arrow 1 had
a significantly greater influence than Arrow 2 and Arrow 3, then a correlation between
the size of the participant inaccuracies when drawing this arrow and the magnitude of
the error in the technique’s result would be apparent. Arrow 3 does appear to produce
a larger total deviation for smaller deviations in input than the other two arrows. This
could be a result of this arrow having a smaller range of user input deviations as shown
in Fig. 5. Despite this, no clear relation between the inaccuracy for a single arrow and the
error of the technique’s result is apparent in these results.
It is possible that a specific combination of arrows may have the greatest influence on
the result, rather than a single arrow. If this were true it would mean that one of the
arrows would have comparatively little influence on the technique’s result. In
circumstances where the participant inaccuracy for a hypothetical inconsequential arrow is
large, but is small for the other two arrows, the total deviation from the arrow’s optimum
angle would not be proportional to the result’s error. This would account for the outlying
data. However, the data from the study does not support this as no relationship between
the inaccuracy of two arrows and the error of the technique’s result is apparent.
There is a further possibility that different individual arrows, or combination of
arrows, may have the greater influence in specific regions of the environment. This could
be due to the interface’s proximity to the landmarks. If an interface inhabits a region of
the display where one of the landmarks used by the technique is significantly closer than
the other, it is possible that the arrow pointing towards this landmark may hold a greater
or lesser influence than in other regions of the environment. Further study is required to
discover if this theory is correct. If true, knowledge of which arrow or combinations of
arrows are the most influential in specific areas of an environment could be employed to
allow the system to reduce the impact of user inaccuracy.
Because users do not appear to become more accurate with experience, alternative
methods of improving the accuracy of the technique will need to be employed.
Confidence ratings could be used to reduce the potential error of the technique’s results.
Furthermore, factors which influence a user’s accuracy could be used to determine the
initial confidence rating for an interface. These ratings could then be used to assess
whether a result is potentially accurate enough for use. If not, the user could be asked to
repeat the technique at this interface. An average of the results from multiple executions
of the technique on a specific interface could be used as the calculated position for
further use by a system. The deviation between the results could also be used to influence
the confidence rating. Since Hypothesis 1 holds true, there is a proportional relationship
between user accuracy and the size of the result’s error. A small deviation in the multiple
results from a single interface would indicate that the user is being more accurate than a
user producing a large deviation in their results.
As a user repeats the technique on an interface the confidence rating increases. A
greater confidence rating implies a higher probability of a more accurately calculated
position. However, one of the main strengths of this technique is the short amount of
time it requires in comparison to the alternative of measuring the position of an
interface directly. Users were noted to take an average of 26.9 s per performance of the
technique in the study. Repeating the technique increases the amount of time required to
calculate the position on an interface. It is important to consider the trade-off between
the accuracy gained from repeating the technique and the additional time required from
users. Using knowledge of where in an environment users may be less accurate, the
number of times the technique is repeated could be kept to a minimum allowing for the
best trade-off between time taken and accuracy.
In this paper, we have presented a technique that can employ a user’s sense of direction
to determine the location of an interface. The technique offers a method of informing
a system of the location and orientation of its affiliated interfaces without the need for
additional technologies or time consuming measurements. This technique is obstruction
tolerant, quick, inexpensive and not encumbering. However, the accuracy of the
technique is dependant on the accuracy of users.
As the accuracy of participants in the study was determined not to improve with
practice, the technique may be required to be made more accurate for use in some systems.
Therefore, attempts to improve the accuracy of users, and as a result reduce the error
of the resulting calculated positions, cannot rely on users gaining experience with the
technique. Future work involving this technique will require discovering what influences
a user’s accuracy. One such influence, discussed in “Discussion” section, is the region of
the environment relative to the landmarks which the display inhabits. Other potential
influences could include the size of the interfaces in addition to the distance between the
interface and the landmarks.
A possible alteration to the technique which could result in greater accuracy is the use
of more landmarks. By drawing additional lines to other landmarks in the environment
(with known distances between them) the technique’s calculations could be repeated
with each pair of landmarks. This could allow for deviation in the resulting calculated
position to be reduced by finding the convergence between the results of each execution
of the calculations. The more arrows the user draws to different landmarks the more the
impact of their deviations could be minimised. In this study the authors focused on the
use of two landmarks as this is the minimum required for validation of this approach.
This makes this iteration of the technique the fastest in comparison with variations
where there are additional landmarks because the user will need to draw more arrows.
However, if the use of extra landmarks greatly increases the accuracy this could be worth
the additional time taken to draw more arrows. Future studies involving the technique
could focus on finding how much additional landmarks increase accuracy. Once this
is known, it would be possible to find the best trade-off in the additional time to draw
more arrows with accuracy of the technique.
Future iterations of the study could also make other improvements on the technique’s
implementation; one feasible improvement could be to the method used for collecting
the orientation of devices. The instructions to participants described the first arrow as
pointing to the Room’s North, this caused confusion amongst some participants and
often had to be explained a number of times by study organisers. This highlighted how
the difference between this first arrow pointing in a general direction (i.e. parallel to the
line between the landmarks) and the subsequent two arrows pointing to specific
locations (i.e. to the landmarks themselves) was difficult to convey to participants and may
have increased the required cognitive load of the technique. This could have led to errors
in what participants thought they needed to point at and their uncertainty may have had
an impact on their accuracy.
It may also be possible for future studies to collect the device orientation through
different approaches. One such method could be through the use of a rectangle on
the screen which the user can rotate. User should be instructed to rotate the rectangle
so that it aligns with the walls of the room. The rectangle will need to have one side
highlighted that participants must align with a specific side of the room. If aligned
correctly, the additive inverse of the rectangle’s orientation would be the orientation of the
device in the room. This method of collecting the device’s orientation could differentiate
the orientation collection phase of the technique with the location determining phase
enough to reduce confusion and uncertainty amongst study participants.
Another alternative method of collecting device orientation could be through the use
of an in-built compass. If the devices being used in the study have in-built compasses
then magnetic North could be used to derive the Room’s North and therefore the
orientation of devices. For this approach to work, the angle between the line from Landmark
A to Landmark B and magnetic North would need to be known beforehand (much like
how the technique needs to know the distance between the two landmarks beforehand).
The devices could then apply this known difference to their reading of magnetic North
to derive their orientation. The removes a step from the technique reducing the required
user input further; only needing users to draw arrows to the two landmarks. This
reduction in user interpretation and input removes some of the technique’s reliance on user
accuracy. Although the accuracy of in-built compasses can vary between devices, they
are likely to return a more accurate result [
] than that from the user input observed in
this study. This change to the technique is likely to vastly improve it, but would only be
applicable in scenarios where the devices used have in-built compasses. The technique
in its current form, as it is presented in this paper, allows for its use on any device which
provides pointing or touch-based user input without needing any additional features.
The findings of any future studies concerning this technique would allow for
improvements to its accuracy. Knowledge of how accurate an execution of the technique is likely
to be allows a confidence rating to be employed; this confidence rating could be used to
judge whether the result is usable for a specific scenario. Through this confidence rating,
the best trade off could be discovered between the time the technique requires to be
performed and the accuracy of the resulting calculated position.
JM developed the technique discussed in the manuscript, conducted the original study and was an author in both the
initial draft and subsequent redraft. TC was an author in the manuscript’s redraft, as well as providing technical advice
and analysis. AH was an author in the manuscript’s original draft. All authors read and approved the final manuscript.
The authors would like to thank the members of the Durham University Technology Enhanced Learning Special Interest
Group for supporting the redrafting of this manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
The software used in the study discussed in this paper is openly available as part of the SynergyNet 2.5 Software
Consent for publication
All authors fully support the publication of the final manuscript.
Ethics approval and consent to participate
The study presented adhered to the ethical standards of research. Ethics approval to conduct this study was obtained
from the Science Faculty Ethics Committee at Durham University. Participants received full details on the study
beforehand, were asked to sign a consent form and were permitted to withdraw at any time.
This work was partially funded under the UK’s EPSRC/ERSC Teaching and Learning Research Programme (TLRP)
SynergyNet project (RES-139-25-0400).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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