# Effect of Visual Cues on Pointing Tasks in Co-located Augmented Reality
Collaboration

Lei Chen<EMAIL_ADDRESS>Xi’an Jiaotong-Liverpool
UniversitySuzhouChina ,  Yilin Liu<EMAIL_ADDRESS>Xi’an Jiaotong-
Liverpool UniversitySuzhouChina ,  Yue Li<EMAIL_ADDRESS>Xi’an Jiaotong-
Liverpool UniversitySuzhouChina ,  Lingyun Yu<EMAIL_ADDRESS>Xi’an
Jiaotong-Liverpool UniversitySuzhouChina ,  BoYu Gao<EMAIL_ADDRESS>Jinan
UniversityGuangzhouChina ,  Maurizio Caon<EMAIL_ADDRESS>University
of Applied Sciences and Arts Western Switzerland (HES-SO)FribourgSwitzerland ,
Yong Yue<EMAIL_ADDRESS>Xi’an Jiaotong-Liverpool UniversitySuzhouChina
and  Hai-Ning Liang<EMAIL_ADDRESS>0000-0003-3600-8955 Xi’an
Jiaotong-Liverpool UniversitySuzhouChina

(2021)

###### Abstract.

Visual cues are essential in computer-mediated communication. It is especially
important when communication happens in a collaboration scenario that requires
focusing several users’ attention on a specific object among other similar
ones. This paper explores the effect of visual cues on pointing tasks in co-
located Augmented Reality (AR) collaboration. A user study (N = 32, 16 pairs)
was conducted to compare two types of visual cues: Pointing Line (PL) and
Moving Track (MT). Both are head-based visual techniques. Through a series of
collaborative pointing tasks on objects with different states (static and
dynamic) and density levels (low, medium and high), the results showed that PL
was better on task performance and usability, but MT was rated higher on
social presence and user preference. Based on our results, some design
implications are provided for pointing tasks in co-located AR collaboration.

Augmented Reality, Co-located collaboration, Visual cues, Pointing Tasks

††copyright: acmcopyright††journalyear: 2021††doi:
10.1145/1122445.1122456††conference: SUI ’21: ACM Symposium on Spatial User
Interaction; November 09–10; 2021††booktitle: SUI ’21: ACM Symposium on
Spatial User Interaction, November 09–10, 2021††price: 15.00††isbn:
978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Mixed / augmented
reality††ccs: Human-centered computing Empirical studies in collaborative and
social computing††ccs: Human-centered computing User studies Figure 1. (a) A
picture of the experimental setup; Two users are locating a target using (b)
Pointing Line (PL) cues and (c) Moving Track (MT) cues; The view of User A who
is locating a target using (f) PL and (g) MT; The view of User B who is now
looking the target that User A has selected using (d) PL and (e) MT.

##  1\. Introduction

Augmented Reality (AR) has been used for collaborative experiences. It allows
users to interact with shared virtual content while having a view of the real
world (Lukosch et al., 2015; Billinghurst and Kato, 2002; Yamazoe and
Yonezawa, 2014). For these collaborative experiences to be efficient and
positive, there needs to be fluid communication between the collaborators. In
addition, knowing what the collaborator is doing or which objects are being
looked at can improve the sense of awareness and social presence (Kim et al.,
2018; Kim et al., 2019; Gerbaud and Arnaldi, 2008; Kraut et al., 1996). One
advantage of using AR head-mounted displays (HMDs) is their ability to capture
users’ head and gaze movements and use the data to provide visual augmentation
cues (Erickson et al., 2020; Huang et al., 2018b; Xu et al., 2019). Such cues
can enhance situational awareness and social presence (Bai et al., 2020) and
improve user performance and usability in collaborative AR (Piumsomboon et
al., 2019).

Previous research on AR primarily focused on remote collaboration (e.g.,
(Piumsomboon et al., 2017; Higuch et al., 2016; Huang et al., 2018a, b)). It
is not clear how visual cues could be helpful in enhancing user collaboration
when they are in the same physical environment. For pointing tasks
specifically, many applications involve such pointing tasks during co-located
collaboration (e.g., games, education, training). It is therefore important to
understand the appropriate techniques for such pointing tasks with different
object states and density levels. Users could be interacting with objects that
are not easily touchable (e.g., moving objects) and clustered in dense regions
with other objects with similar properties. Attempting to describe or pinpoint
an object of interest using verbal and hand gestures may not be practical and
efficient. In this research, we explore the use of visual cues to enhance the
identification of target objects in collaborative AR where these objects can
be static or dynamic and be in various levels of density.

Pointers (Erickson et al., 2020; Kim et al., 2013a; Yu et al., 2019) and
annotations (Huang et al., 2019; LaViola et al., 1998) are two main visual
cues that have been explored. The majority of prior research focused on hand-
based techniques, e.g., to carry a pointer and to draw an annotation in a
virtual environment. However, these hand-based techniques may not be efficient
or ideal for AR systems (Lu et al., 2021, 2020). For one, it can pose severe
occlusion because the field-of-view of AR HMDs is usually narrow. Also, AR
systems, with the exception of the Magic Leap, do not come with a pointing
device or controllers.

Therefore, in this research, we explore two hands-free, head-based techniques
to simulate visual cues provided by pointers and annotations. Pointing Line
(PL) is used to simulate pointers. It indicates a user’s line of sight and
focus of attention. Moving Track (MT) simulates the use of annotations. It
records a continuous moving track to help identify a target. Based on the two
techniques, we investigate the effect of visual cues on task performance,
usability, and social presence when interacting with virtual objects in a co-
located AR. To do this, we ran a user study with two visual cues (PL and MT)
to allow paired users to share information about the object of interest and
identify it. The experiment involved both static and dynamic objects in three
levels of density (low, medium, and high). Figure 1 (above) shows an overview
of the experiment setup.

In short, the paper makes the following two contributions:

  * •

We report the results of a user study comparing the two types of visual cues
based on hands-free, head-based techniques in co-located AR scenarios for
pointing and selection tasks;

  * •

Based on the results, we propose implications on the design and use of these
visual cues for co-located AR collaboration.

##  2\. Related Work

###  2.1. Collaborative AR Systems and Social Presence

Collaboration is the “mutual engagement of participants in a coordinated
effort to solve a problem together” (Roschelle and Teasley, 1995).
Collaborative AR systems allow users to interact with shared AR content as
naturally as with physical objects, and to complete a task or achieve a common
goal (Billinghurst and Kato, 1999; Li et al., 2017; Chen et al., 2020).
Researchers have shown that AR systems can effectively support a group of
users to perform collaborative activities (Tait and Billinghurst, 2015;
Kaufmann, 2003; Szalavári et al., 1998; Billinghurst et al., 2002b).

During collaboration, it is key to be aware of where and which object(s) the
collaborator is interested in or interacting with (Lacoche et al., 2017;
Antunes et al., 2001; Dourish and Bellotti, 1992). Providing visual, nonverbal
cues is beneficial for improving the awareness of users’ actions and the sense
of being together, namely social presence (Buxton, 2009; Gergle et al., 2013).
In the early days, telepointers and cursors (Greenberg et al., 1996) were
explored to support user awareness of others’ actions on a shared workspace in
traditional platforms (e.g., desktop displays). Recently, multimodal cues are
used in collaborative environments, typically using auditory and visual
elements. For example, virtual avatars have been explored to represent each
collaborator and to provide an increased awareness of others in the shared
environment (Gutwin et al., 1996; Piumsomboon et al., 2018; Jo et al., 2016).
In AR environments, researchers explored the use of virtual arrows to
represent collaborators’ head directions (Chastine et al., 2007) and miniature
virtual avatars to show collaborators’ gaze directions and body gestures
(Piumsomboon et al., 2018). Although using avatars can contribute to users’
perceived social presence, it adds extra visual elements to the limited
display and field-of-view of current AR HMDs. Thus, this solution may not be
ideal when there are multiple objects in the environment. Using pointer and
annotation cues is an uncluttered alternative to support social presence and
collaboration in AR. Specifically, sharing gaze pointing cues has been
explored in remote collaboration with AR devices (Ishii et al., 1993; Lee et
al., 2017; Higuch et al., 2016). For example, Ishii et al. (Ishii et al.,
1993) and Higuchi et al. (Higuch et al., 2016) reported that when users shared
their workspace, gaze pointing cues provided better understanding where their
partner was looking at. Lee et al. (Lee et al., 2017) and Higuchi et al.
(Higuch et al., 2016) found that sharing gaze pointing cues significantly
improved users’ awareness of each other’s focus and joint attention.

In addition to gaze pointing cues, augmenting hand pointing cues via gestures
was shown to be able to facilitate users’ perception of others’ actions. For
example, Piumsomboon et al. (Piumsomboon et al., 2018) reported that
redirected gaze and gestures in their Mini-Me system improved users’ awareness
of the partner in a collaborative AR interface. Yang et al. (Yang et al.,
2020) stated that visual head frustum and hand gestures intuitively
demonstrated the remote user’s movements and target positions. Kim et al. (Kim
et al., 2019) added sketch cues in addition to gestures and demonstrated an
improved task efficiency with the enhanced visual cues.

Although previous research has proven the positive effects of visual cues on
supporting social presence in collaborative activities, most of these studies
focused on remote AR systems. The effect of visual cues in co-located AR
collaboration is still largely underexplored. It is not clear how different
visual cues may enable and enhance users’ perceived social presence when they
are co-located in the same physical space. Therefore, we put our focus on the
study of visual cues in co-located AR environments.

###  2.2. Visual Cues in Collaboration

Two noticeable visual cues were presented in previous work on collaboration:
pointer and annotation (Kim et al., 2013a; Huang et al., 2019; Teo et al.,
2018; Erickson et al., 2020). Pointer cues provide a pointing line, indicating
a user’s line of sight and focus of attention. Annotation cues record a moving
track to identify a target, such as a track of hand or head positions. Here,
we discuss these two types of visual cues in detail.

##### Pointing Line Cues

Previous work showed that pointing line cues can effectively support
communication (Greenberg et al., 1996; Duval et al., 2014; Oda et al., 2015;
Sousa et al., 2019; Sakata et al., 2003; Gupta et al., 2016). For example,
Gupta (Gupta et al., 2016) reported that presenting users’ gaze directions
using a pointer significantly improved the sense of co-presence between users
in remote collaboration. Piumsomboon et al. (Piumsomboon et al., 2019)
visualized three types of pointing line cues: the field-of-view frustum, eye-
gaze ray, and head-gaze ray. They reported that these cues significantly
improved user performance, usability, and subjective preferences. They also
found that head-gaze ray was significantly less confusing to use than field-
of-view.

Most of the previous research explored the effect of pointing line cues with
hand gestures or eye gaze (Bai et al., 2020; Sousa et al., 2019). For hand
gestures, if users’ hands are occupied, it requires users to deliberately
release the object in their hand before pointing to a target. Some research
used eye gaze to provide pointing line cues (Špakov et al., 2019; Blattgerste
et al., 2018). They stated that eye gaze can lead to better performance and
better teamwork than head gaze, i.e., the direction that a user is facing
towards but not necessarily looking at. However, people’s eye gaze has a lot
of micro movements. Having to keep their eyes stable when pointing at an
object may lead to fatigue. Head-based techniques, on the other hand, could
mitigate this issue. By providing a gaze ray from a user’s head to a target
object, this can support observers’ awareness of their collaborator’s
attention and allow them to view the same object (Anthes and Volkert, 2005).
However, studies on head-based pointing line cues are very limited, especially
in co-located AR collaboration.

##### Moving Track Cues

Moving track is one of the mostly studied visual communication cues (Tang and
Minneman, 1991; Rekimoto and Nagao, 1995). It has been found to be more
effective than pointing line cues for communicating spatial information (Kim
et al., 2013a; Fussell et al., 2004). While the pointing line cues present a
point around the target object, moving track cues provide users with a track,
namely a series of points leading to the target, and can help guide users’
focus to key locations (Lu et al., 2021). Early research has studied the use
of moving track cues on a shared video view (Tang and Minneman, 1991; Ishii et
al., 1994). Researchers have explored extensively the use of moving track cues
in remote collaborative physical tasks. For example, Billinghurst et al.
(Billinghurst et al., 2002a) presented a method to record stabilized moving
tracks in an annotation-based AR system. Teo et al. (Teo et al., 2018)
reported that local users in remote collaboration felt the task was easier and
more enjoyable with moving track cues, and the cues helped them understand the
attention and focus of the remote user. A recent study (Kim et al., 2019)
compared four combinations of visual cues provided by hand gesture, pointer
and sketch. They reported that participants completed tasks faster and felt
higher level of usability if sketch (moving track) cues were provided.

Although there have been studies on moving track cues, such as annotations and
drawings, this research mainly used hand-based techniques and focused on
remote collaboration (Huang et al., 2018b; Teo et al., 2018). Besides,
previous research on moving track cues also demonstrated some limitations.
Much of this research explored these cues as an annotation tool. In this case,
the track traces needed to be erased after completing each step of a task (Kim
et al., 2013a). Huang et al. (Huang et al., 2018b) also mentioned that moving
track cues that blocked users’ view could have a negative impact on user
experience and task performance. Based on these findings, we have made the
moving track cues to gradually fade away and disappear in our study. This
solution neither requires users to actively erase any virtual element, nor
prevents users from viewing the workspace with additional visual elements.

In summary, our research is framed on previous work and compares two head-
based visual techniques, pointing line cues and moving track cues, to explore
their effect on pointing tasks in co-located AR collaboration.

##  3\. System Setup

To conduct this research, we created a multi-user AR collaborative system (see
Figure 1a). The system was developed with Unity (version 2019.2.0f1) and was
run on the Windows platform. Each of the two users used a Meta2 connected to a
laptop and with each other via a private network and Photon Unity Networking.
Either laptop could be the host server. After they have been connected, one
would assume the host role and the two laptops’ timers were synchronized to
begin the experiment.

The equipment set up in this study consisted of (1) two Meta2 HMDs, an AR
device with two optical see-through displays with a 2550 x 1440 pixel
resolution, 90° field of view, and frame rate of 60 Hz. (see Figure 1a), and
(2) two Windows 10 laptops each with an Intel Core i9-8950HK at 2.9 GHz, 32 GB
RAM, and NVIDIA GeForce GTX 1080. This set up allows a pair of users to work
in the same virtual environment using the two Meta2 HMDs and jointly do any
tasks together. The system is able to collect data (e.g., completion time and
accuracy).

###  3.1. Visual Techniques

To determine the techniques selected for this study, we first conducted a
pilot study and explored both head-based and hand-based approaches, each with
and without a visual cue (i.e., baseline). The results showed that head-based
tended to be better in performance and user preference for pointing tasks in
co-located AR (which was in line with other studies (e.g., (Hansen et al.,
2004; Yang et al., 2020)). Given our pilot study results and those from the
literature, we did not include a baseline technique to keep the study focused
and within a reasonable time. Therefore, our study is based on two head-based
visual techniques (i.e., Pointing Line and Moving Track) to compare their
performance and usability under different scenarios.

The two head-gaze techniques receive input from the user’s head movement and
is captured by the tracking function in the Meta2. The cursor endpoint is
based on a ray cast from the head’s center toward the AR interface.

Figure 2. The two visual techniques (a) Pointing Line (PL) and (b) Moving
Track (MT) with an example each.

##### Pointing Line (PL)

PL is shown as a red ray representing the user’s viewing direction. It is
emitted from the center of the user’s head toward the user interface in AR
environment. When it intersects with an object, this object becomes
highlighted and can be selected. As shown in Figure 2a (upper row), when a
user moves his/her head, the ray also moves in synchronicity following the
head’s directional movements.

##### Moving Track (MT)

MT uses a different approach to show directional movement. Instead of showing
the ray, it shows a trail that follows the cursor. To reduce excessive visual
clutter, the technique limits the length of the trail to 10 pixels. That is,
the excess part of the tail fades out and gradually disappears. As shown in
Figure 2b (lower row), as the user moves their head, the trail is displayed,
with end part becoming lighter and fading out gradually.

##  4\. USER STUDY

###  4.1. Participants

16 pairs of participants (17 males) between the ages of 18 to 23 (M = 20.78)
were recruited from a local university for this experiment. Only 3 pairs did
not know each other before participating in the experiment. All participants
had normal or corrected-to-normal vision and had no issues distinguishing the
colors we used in the AR device. They reported an average of 3.69 for
cooperative ability on a scale from 1 (‘so bad’) to 5 (‘good’). 13 of them
(41%) had limited prior experience with AR HMDs before.

###  4.2. Experiment Design

The experiment followed a 2 × 2 × 3 (2 Technique × 2 Object State × 3 Density)
within-subjects design to study the effects of visual cues on pointing tasks
in co-located AR collaboration. In this experiment, we have the following
three independent variables:

  1. (1)

Technique. We tested two visual techniques: Pointing Line (PL) and Moving
Track (MT). As mentioned above, PL is based on the ray-casting approach to
show a line to the object on the direction of the AR HMD (see Figure 3a). MT
provides a continuous trace that follows the pointer (see Figure 3b).

  2. (2)

Object State. We explored two states: Static and Dynamic. Static objects were
immobile while the dynamic ones would move around. As the arrows depicted in
Figure 3c indicate, dynamic objects would be randomly assigned a path without
any particular pattern. In dynamic scenarios, the objects performed uniform
rectilinear motion (i.e., constant velocity) with speeds between 50-120
pixels/second and random directions. The placement and movement of the objects
were predefined to be same for each pair. All objects (target and non-targets)
would move and were programmed to avoid each other. A virtual boundary of 120
degrees was set for the objects. As this is wider than the 90-degree FOV of
the Meta2, users would need to turn their heads during the tasks.

  3. (3)

Density. The size of objects used in our study was 70 pixels in diameter.
There were three levels of object density: Low (6 objects with no occlusion,
Figure 3c), Medium (12 objects with slight occlusion, Figure 3d) and High (18
objects with severe occlusion, Figure 3e).

Figure 3. Two users completing the task with (a) Pointing Line (PL) and (b)
Moving Track (MT) technique. There were (c) Low, (d) Medium and (e) High
density for Static and Dynamic objects. Red indicates the target object; green
indicates the object selected.

As a within-subjects experiment, each participant experienced all 12
conditions. Technique was counterbalanced according to a balanced Latin square
design to minimize learning effects. Object State and Density were randomly
and equally distributed. There were 6 trails for each condition and 72 trials
in total for each pair of participants.

This experiment was classified as low risk research and was approved by the
University Ethics Committee at Xi’an Jiaotong-Liverpool University
(#21-01-09).

###  4.3. Hypotheses

Based on our literature review and design of our experiment, we postulated the
following four hypotheses:

H1: PL and MT would both perform better with static targets than with dynamic
objects.

H2: PL and MT would have better performance in lower density scenarios. High
density situations could be crowded and have object occlusion, making it more
difficult to identity and select a target.

H3: PL’s more explicit cue showing viewing direction, it would lead to
improved task efficiency and accuracy than MT.

H4: In terms of subjective measures, in general, MT would be more preferred by
users than PL, as MT would make it easier to track cursors movement and the
user’s intention (in our case User A). As such, it would increase
collaborators’ awareness and co-presence.

Figure 4. An example of all the steps involved in a typical trail. Table 1. Visibility states of the visual cues of User A and User B and the target to be selected according to selection stage | During the selection of User A | During the selection of User B
---|---|---
|  User A’s visual cue |  User B’s visual cue |  Target to be Selected |  User A’s visual cue |  User B’s visual cue |  Target to be Selected
User A |  Yes |  No |  Highlighted by System |  No |  Yes |  Visible (Already Selected by this User)
User B |  Yes |  No |  Visible but not highlighted |  No |  Yes |  Visible and Highlighted (Selected by User A)

###  4.4. Task and Procedure

##### Task

For each trial, User A was required to choose the target object (see Step (1)
in Figure 4, highlighted in red) by moving the cursor to the object using head
motions and confirming it via a mouse click. The selected object would turn to
green, which can be seen by User B. Then User B would need to locate the same
object by moving the cursor and doing the confirmation. As such, the task
consisted of the following steps (see Figure 4): (1) system randomly
highlighted in red one object in the scene. Only User A could see the
highlighted target. At this moment, the objects in User B’s view are all grey.
(2) User A would choose the object using the pre-defined technique. Once
confirmed, the selected object would turn into green and both users could see
this object. (3) User B was then required to locate the green object and
confirm it. (4) One trial was finished and the next would begin when both
users were ready to proceed.

In our experiment, the two users in each pair would do the selection in turns.
We designed the experiment so that they could not see both the visual cues of
their partner and their own at the same time. During selection, each user
could only see one visual cue (the cue of the user needing to do the
selection). In other words, during the selection of User A, only the cue of
User A would be visible; while during the selection of User B, only the cue of
User B would be shown. Besides, at the beginning, only User A could see the
target highlighted by system. Only after User A completed the selection would
User B be able to see the selected target, now selected by User A (see the
Table 1 for a summary of this process). This approach was chose to minimize
visual clutter and to make it more aligned with how collaboration takes in
places in more realistic scenarios.

##### Procedure

The experiment procedure was divided into 4 phases: (P1) Informing
participants of the experiment goal and the ethics regulations governing it,
and then completing the consent form plus a short questionnaire to collect
anonymized demographic data ($\sim$5 minutes); (P2) Providing participants
several practice trials to let them become familiar with the AR device and the
task ($\sim$5 minutes); (P3) Participants completing the trials and in between
conditions each participant filling in the Social Presence, and Usability
questionnaires ($\sim$25 minutes); and (P4) Interviewing participants to
collect further feedback and comments ($\sim$5 minutes). The whole experiment
took about 40 minutes to complete for each pair.

Figure 5. Mean completion time (Left) and accuracy rate (Right) results in all
conditions: 2 Technique (PL and MT) × 2 Object State (Static and Dynamic) × 3
Density (L: Low, M: Medium and H: High). Figure 6. Completion Time (a) and
Accuracy Rate (b) according to Technique, Object State, and Density.

###  4.5. Results

For the objective data analysis (completion time and accuracy rate), we
employed three-way repeated ANOVAs with an alpha value of 0.05 to determine
any differences across conditions and followed by pair-wise comparisons with
Bonferroni correction for the data with a significant difference. We used a
Greenhouse-Geisser adjustment to correct for violations of the sphericity
assumption. We reported effect sizes ($\eta$p2). For data from the subjective
questionnaires that were ordinal, non-parametric data (e.g., subjective
ratings or rankings), we applied Wilcoxon signed-rank test to look for
differences. For simplicity, M and SD are used to denote mean and standard
deviation values.

####  4.5.1. Task Performance

Participants’ task completion time and accuracy rate were collected to assess
their performance. We recorded the time taken by paired participants to
complete each trial. Accuracy rate was measured by the number of correct
trials among the total number of trials. Figure 5 shows mean time and accuracy
rate for each condition (2 Technique × 2 Object State × 3 Density).

##### Completion Time

We found an interaction effect between Technique × Object State (F1,95 =
3.993, p = .049, $\eta$p2 = .040), Technique × Density (F2,190 = 7.461, p =
.001, $\eta$p2 = .073), Technique × Object State × Density (F2,190 = 4.065, p
= .019, $\eta$p2 = .041) on completion time, but no significance was found
between Object State × Density (p = .758). For Technique × Object State,
pairwise comparisons revealed that PL_Dynamic took significantly less time
than MT_Dynamic (p = .001). PL_Static also took less time than MT_Static, but
no significant difference was found (p = .149). In addition, both PL and MT
techniques took significantly less time in static condition than that in
dynamic one (both p ¡ .001).

For Technique × Density, PL_Medium was significantly faster than MT_Medium (p
¡ .001). Although we noticed that PL_High and PL_Low were respectively faster
than MT_High and MT_Low, no significant difference was found (both p ¿ .05).
Besides, PL_High was significantly slower than PL_Medium (p = .027) and PL_Low
(p ¡ .001). MT_Low was significantly faster than MT_High (p = .001) and
MT_Medium (p ¡ .001). For Technique × Object State × Density,
PL_Dynamic_Medium took less time than MT_Dynamic_Medium (p ¡ .001). For other
conditions, although using PL was faster than MT, there was not any
significant difference (p ¿ .05).

Table 2. Means (Standard Deviations) of accuracy rate on different conditions. | Static | Dynamic
---|---|---
|  Low |  Medium |  High |  Overall Mean |  Low |  Medium |  High |  Overall Mean
PL |  97.9% (14.4%) |  93.8% (24.3%) |  92.7% (26.1%) |  94.8% (22.3%) |  95.8% (20.1%) |  95.8% (20.1%) |  94.8% (22.3%) |  95.5% (20.8%)
MT |  93.8% (24.3%) |  87.5% (33.2%) |  91.7% (27.8%) |  91.0% (28.7%) |  90.6% (29.3%) |  92.7% (26.1%) |  87.5% (33.2%) |  90.3% (29.7%)
Figure 7. Users’ ratings on Social presence in all conditions (2 Techniques ×
2 Object States); Significance results are highlighted in Bold; PL: Pointing
Line, MT: Moving Track; S: Static, D: Dynamic; Subscales, CP: Co-presence, AA:
Attention Allocation, and PMU: Perceived Message Understanding.

In addition, there was a significant effect of Technique (F1,95 = 14.559, p ¡
.001, $\eta$p2 = .133), Object State (F1,95 = 33.360, p ¡ .001, $\eta$p2 =
.260), and Density (F2,190 = 15.303, p ¡ .001, $\eta$p2 = .139) on completion
time. As shown in Figure 6a, post-hoc analyses indicated that using PL was
significantly faster than using MT. Besides, time spent on trials with Static
objects was significantly lower than with Dynamic ones. Post-hoc analyses
showed that completing High and Medium density trials took significantly
longer time than Low density trials (both p ¡ .001). We also found an
increasing trend of the mean time spent on Low, Medium and High density tasks.
More details of the main results on completion time are provided in the table
in Appendix A.

##### Accuracy Rate

Overall, we found a high average accuracy rate for all conditions (M = 92.8%,
SD = .257) in all trials (see Figure 5 (Right)). A further analysis showed
that there was no significant interaction effect of Technique × Object State
(p = .688), Technique × Density (p = .988), and Technique × Object State ×
Density (p = .410) on accuracy rate. When looking at the descriptive data, we
found PL_Dynamic and PL_Static got higher rates than MT_Dynamic and MT_Static,
respectively. Besides, for mean results of accuracy rate, PL performed better
than MT in static trials, while MT performed better in dynamic trials. For
Density, the accuracy rate of two techniques gradually decreased with the
increase in density. In addition, from Figure 5 (Right), we can see that PL
led to a higher rate than MT in general in any density condition. More details
of all conditions on accuracy rate are shown in Table 2.

Besides, we found that there was a significant effect of Technique on accuracy
rate (F1,95 = 7.355, p = .008, $\eta$p2 = .072). As shown in Figure 6b, Post-
hoc analyses showed that participants with PL got significantly higher
accuracy rate than ones with MT. In addition, we noticed that the rate
decreased gradually with the increase in density of objects. The Static and
Dynamic trials led to the same rate. However, we did not find any significant
difference of Object State and Density (all p ¿ .05).

####  4.5.2. User Experience

Participants’ collaboration and user experience were quantified using the data
from post-experiment questionnaires that contained Likert-scale questions. We
collected three sets of subjective data: Social Presence, System Usability
Scale, and User Preference.

##### Social Presence

We adapted the Social Presence Questionnaire (Harms and Biocca, 2004)
according to the nature of our trials. 3 sub-scales, Co-presence (CP),
Attention Allocation (AA) and Perceived Message Understanding (PMU), were used
and consisted of 9 rating items on a 7-point Likert scale (1: Strongly
Disagree $\sim$ 7: Strongly Agree). Overall, participants had a high feeling
of social presence when interacting in AR (M = 5.507, SD = .895). Besides, we
found that MT was rated higher than PL in all conditions for median scores
(see Figure 7). A Wilcoxon signed-rank test revealed that there was a
significant interaction effect of social presence between PL and MT in dynamic
trials (Z = -1.993, p = .046) but no interaction significance was found in
static trials (p = .180). Besides, there was a significant effect of Technique
on social presence (Z = -2.618, p = .009). Pairwise comparisons revealed that
overall MT got a significantly higher score than PL.

For the subscales, the Wilcoxon signed-rank test revealed a significant
difference of CP on PL and MT (Z = -2.142, p = .032). Post-hoc tests showed
that MT got a significantly higher score than PL (Z = -1.961, p = .050) in
Dynamic trials but there was no significance in Static trials (p = .310). For
AA, we found a significant difference between PL and MT (Z = -2.128, p =
.033). MT got a significantly higher score than MT in Dynamic trials (Z =
-1.976, p = .048), but there was no significance in Static trials (p = .163).
We did not find any significance on the PMU subcale (all p ¿ .05).

Figure 8. Users’ ratings of SUS on the technique’s usability with significance
(*: statistically significant); SUS score $\in$ [0, 100], the higher, the
better.

Figure 9. User preference of the two visual techniques.

##### System Usability Scale (SUS)

SUS (Brooke et al., 1996) was selected to measure and evaluate the
participants’ responses towards the usability of the two visual techniques. It
consisted of 10 items using 5-point Likert scales (1: Strongly Disagree $\sim$
5: Strongly Agree). Participants gave an average of 78.162 (SD = 12.958) and
73.235 (SD = 14.414) for PL and MT, respectively. Figure 9 shows the users’
rating with median and significance results for all conditions. A Wilcoxon
signed-rank test showed that there was a significant difference between PL and
MT (Z = -2.611, p = .009). Pairwise comparisons showed a significant effect
between PL and MT in Dynamic trials (Z = -2.042, p = .041). No other
significance was found (all p ¿ .05).

##### User Preference

At the end of the experiment, we asked participants to choose their preferred
technique for different types of trials (see Figure 9). Overall, MT (54.69%)
was more favored by participants than PL (45.31%). For the Static_Low and
Dynamic_Low trials, the proportion of MT was not higher than PL. Except for
these 2 types of trials, MT got always a higher number of votes. One
interesting finding was that both in Static and Dynamic trials, the higher the
density of objects, more participants would choose MT.

##  5\. DISCUSSION

In this paper, we explored the use of two visual techniques (Pointing Line
(PL) and Moving Track (MT)) for providing awareness cues during collaborative
exploration in AR systems for static and dynamic objects and with different
levels of object density (Low, Medium and High). Overall, results from our
user study indicated that these visual cues provided benefits. We next discuss
the findings in more detail.

###  5.1. User Performance and Usability: PL ¿ MT

When completing the trials, the density and state of objects affected the
participants’ performance. This was expected and our results verified that the
time needed for selecting dynamic objects was significantly longer while the
accuracy was lower, which supports H1. The accuracy rate decreased with the
increase of object density, which also led to increase the time spent on the
tasks. In particular, the time spent on lower density trials was significantly
less than on higher density trials, which is in line with our expectations and
also aligns with H2.

Our results show that PL led to significantly higher performance than MT,
which aligns with H3. This result aligns with the findings from Kim et al.
(Kim et al., 2019) who found that a pointer could be a main visual
communication cue with fast completion time. On the other hand, other studies
(Fussell et al., 2004; Kim et al., 2013b) found that users completed assembly
tasks faster with drawing sketch (moving track) cues than with pointing cues,
which differs from our results. However, this difference can be explained with
reference to the nature of the tasks in the 2 experiments. For annotations,
once drawn, the moving track cues remain in the shared task space so the
information is available until it is erased. This can be beneficial for
assembling tasks as in the cited experiment. In our scenario, instead, keeping
the moving track cues would add further visual details to an already busy AR
environment and, as such, may not be so convenient (e.g., in high density
cases). For completion time, specifically, PL contributed to a significant
higher efficiency than MT in dynamic trials with medium density. In other
conditions, PL still performed better than MT in general. In other words,
despite the possibility of PL cues occluding more users’ view when objects
were motion, this did not lower their performance. One participant said that
”PL was easy to control even if the objects were moving”, which could have
provided some degree of counterbalance. As such, this result shows that
regardless of whether objects were moving or not and the density level, PL
cues seem more beneficial on task performance.

While the accuracy rate was high for both visual techniques, PL was
significantly better than MT, which supports H3. For mean values, PL led to
higher accuracy than MT in all conditions. It seems that PL was more intuitive
when showing the position of the target that one user was watching. Some
participants (N = 3) mentioned that since a pointing ray was emitted from the
user’s head, they would have control over the start point and orientation of
the ray, much like a physical laser pointer. Participants found MT slightly
more difficult to use, which resulted in taking significantly longer time to
complete the trials. In general, our results show that PL was significantly
better than MT on task performance, especially for improving task efficiency
in dynamic trials with medium density level of objects.

Results from SUS show that participants rated PL significantly higher than MT.
It was preferred significantly more for dynamic trials. In general,
participants found that showing a visual line from the head to a target
allowed them to see the direction of pointing and this extra implicit
information led to improved performance and higher accuracy. Participants used
the words ’intuitive’ and ’natural’ to describe PL but not for MT.

###  5.2. Social Presence and User Preference: PL ¡ MT

We found that MT yielded significantly higher ratings on social presence than
PL overall and, specifically, in dynamic trials, which supports H4. In
particular, MT was rated significantly higher than PL on Co-presence and
Attention Allocation for both overall and in dynamic trials. The visual tail
of MT moving behind the cursor seems to have allowed users to focus and
encouraged them to predict the end target. By focusing on the path,
participants said that they ‘felt more connected’ with the thinking of the
other user and could sense their presence better. MT’s emphasis on the
trajectory path in real-time seems to have enhanced their feeling of social
presence with the other user. One user mentioned that ”it is very interesting
to follow the movement path”, and another one said that ”I can not only know
what the target location is, but also where the focus view starts. This way, I
can strongly feel my partner being with me together.”

Interestingly, when asked which technique they prefer to use, MT was rated
higher than PL in most conditions, except for low density. This will support
our H4. Participants in general said that they prefer MT over PL but there is
a general agreement that PL has a better usability, is more intuitive to use,
and can help them perform faster. This also explains the findings from the SUS
results. This result shows that when working with another person, the feeling
of being together, of being connected (even by using a simple visual cue) with
the collaborator is an important factor that affects user experience. Some
prior studies seem to support our results. For example, Teo et al. (Teo et
al., 2018) reported that all of their participants preferred having visual
annotation cues, and also stated that participants felt that the task was
easier and more enjoyable with dynamic visual cues. Their findings are aligned
with our results. Unlike performance results, these results are more
subjective and reflect the emotional side of working with other users, which
is an important aspect for collaborative systems.

In short, the results show that PL was better on task performance and
usability but MT was rated on social presence and user preference.

###  5.3. Design Implications of our Findings

Our results and findings point to the following implications for the design
and use of visual cues in co-located AR that involves pointing tasks:

  * •

The objective results showed that users performed better with Pointing Lines
(PL). Therefore, if the goal is to maximize task performance, especially
considering efficiency and accuracy, a technique like the PL could be helpful.

  * •

PL is considered easy to control and seems more usable, which can transfer to
improved task performance. As such, when ease of control and high usability is
important, a PL-based technique could be beneficial, especially when dealing
with dynamic objects within a medium level of density (slightly occluded and
crowded conditions).

  * •

Based on users’ preference and feedback, it seems that being able to follow
the movement of the partner’s viewing direction provides a higher focus and
attention. In this context, they rated Moving Tracking (MT) higher than PL.
Therefore, if the goal is to provide higher collaborative experience and
social presence, a technique similar to MT is more suitable.

##  6\. LIMITATIONS AND FUTURE WORK

This research has some limitations, which can serve as directions for future
work. As stated earlier, given the results of our pilot study and our
literature review, we did not include a baseline technique as by including one
it would unnecessarily lengthen our experiment unnecessarily but without
providing additional insights. Future work could involve more variations and
different implementations of the two visual techniques, a baseline technique
can be helpful to allow pinpointing the effect of more specific aspects of
visual techniques for pointing tasks in collaborative scenarios.

Although the size of our sample population is in line with some publications
reporting similar experiments (e.g., (Huang et al., 2019; Piumsomboon et al.,
2019)), the sample size was not very large. However, our data still had enough
power and was able to show significant differences across the various
conditions of the experiment, leading to some interesting findings. In the
future, it will be useful to run a study with a larger sample (that has a more
diverse group of participants, including pairs who are familiar and unfamiliar
with each other) and see if the same results hold or new insights are found.
Users in the current study have specific roles, i.e., one initiates the task
and the other follows. It would be interesting to study more complex tasks
where users’ roles are not clearly specified (and can switch back and forth
between roles). Future work may also investigate how visual cues can
facilitate communication without a specific task and in more fluid and
flexible scenarios).

We did not consider the effect of position arrangement of each pair of users
and cases where they are located in separate physical spaces. There may not be
a significant effect with MT. However, it is not clear what the effect is with
PL because it provides implicit direction information about where the user is
looking at. In the future, it will be helpful to explore cases where users are
moving and positioned at different locations and orientations and when they
are separated physically.

In this experiment, we did not consider other types of non-visual sensorial
cues, like audio and haptic feedback. While the implementation of such cues
requires careful design, it is interesting and useful because AR systems are
multimodal and suitable for combining several modes of sensorial feedback to
create a more immersive work environment.

##  7\. CONCLUSION

In this paper, we evaluated the effect of visual cues on pointing task
performance, usability, and social presence in co-located AR collaboration. A
user study was conducted by comparing two different visual cues (Pointing Line
(PL) and Moving Track (MT)) during collaboration with Static and Dynamic tasks
in Low, Medium, and High levels of object density. Based on the results of an
experiment following a 2 × 2 × 3 (2 Technique × 2 Object State × 3 Density)
within-subjects design with 16 pairs of participants, we found that users with
PL cues performed better than with MT cues on task performance. Users were
more positive about the usability of PL than MT, especially in dynamic tasks
with a medium level of object density. Besides, we found that MT cues were
useful for enhancing the sense of social presence and user experience when
completing pointing tasks in co-located AR. Overall, our results show that PL
was better on task performance and usability, while MT was better on social
presence and user preference. With these findings, we discussed the
implications for the design and use of visual cues for co-located AR
collaboration.

###### Acknowledgements.

The authors wish to thank the participants for their time and the reviewers
for their insightful comments that have helped improve our paper. This
research was funded in part by Xi’an Jiaotong-Liverpool University’s Key
Special Fund (#KSF-A-03 and #KSF-A-19) and Research Development Fund
(#RDF-16-02-43), the Natural Science Foundation of Guangdong Province
(#2021A1515012629), and Guangzhou Basic and Applied Basic Foundation
(#202102021131).

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##  Appendix A Appendix A: Summary Table of Main results of completion time
showing significant differences between conditions.

Variable |  Condition |  Mean (SD) / s |  ANOVA - P |  Post-hoc Test Results
---|---|---|---|---
Technique |  PL |  3.339 (1.103) |  p ¡ .001*** |  PL ¡ MT (p ¡ .001***)
|  MT |  3.556 (1.205)
Object State |  S |  3.245 (0.960) |  p ¡ .001*** |  S ¡ D (p ¡ .001***)
|  D |  3.651 (1.299)
Density |  L |  3.202 (1.103) |  p ¡ .001*** |  L ¡ H (p ¡ .001***)
|  M |  3.554 (1.181) |  L ¡ M (p ¡ .001***)
|  H |  3.587 (1.157) |  M ¡ H (P = .998)
Technique × Object State |  PL_S |  3.195 (0.981) |  p = .049* |  PL_D ¡ MT_D (p = .001**) PL_S ¡ MT_S (P = .149)
|  PL_D |  3.483 (1.197)
|  MT_S |  3.294 (0.937)
|  MT_D |  3.818 (1.376)
Technique × Density |  PL_L |  3.161 (1.114) |  p = .001** |  PL_M ¡ MT_M (p ¡ .001***) PL_H ¡ MT_H (P = .381) PL_L ¡ MT_L (p = .428 )
|  PL_M |  3.392 (0.937)
|  PL_H |  3.551 (1.200)
|  MT_L |  3.243 (1.094)
|  MT_M |  3.717 (1.366)
|  MT_H |  3.622 (1.115)
Technique × Object State × Density |  PL_S_L |  2.951 (0.894) |  p = .019* |  PL_D_M ¡ MT_D_M (p ¡ .001***) PL_D_H ¡ MT_D_H (p = .494) PL_D_L ¡ MT_D_L (p = .579) PL_S_H ¡ MT_S_H (p = .588) PL_S_M ¡ MT_S_M (p = .103) PL_S_L ¡ MT_S_L (p = .516)
|  PL_S_M |  3.250 (1.006)
|  PL_S_H |  3.385 (0.998)
|  PL_D_L |  3.372 (1.267)
|  PL_D_M |  3.360 (0.894)
|  PL_D_H |  3.718 (1.357)
|  MT_S_L |  3.024 (0.909)
|  MT_S_M |  3.424 (0.982)
|  MT_S_H |  3.435 (0.866)
|  MT_D_L |  3.461 (1.218)
|  MT_D_M |  4.184 (1.516)
|  MT_D_H |  3.809 (1.295)
Note: Level of significance: *¡0.05, ** ¡0.01, and *** ¡0.001; Significant results are highlighted in Bold; PL: Pointing Line, MT: Moving Track; S: Static, D: Dynamic; L: Low, M: Medium, H: High. |  |  |  |