0 Research  The six text selection techniques explored in this research,
grouped by three pointing mechanisms: (a) Controller-based: Controller+Click
and Controller+Dwell, (b) Head-based: Head+Click and Head+Dwell, and (c) Hand-
based: Hand+Pinch and Hand+Dwell.

# Evaluation of Text Selection Techniques in Virtual Reality Head-Mounted
Displays

Wenge Xu
DMT Lab, Birmingham City University
Birmingham, UK e-mail<EMAIL_ADDRESS>Xuanru Meng e-mail:
<EMAIL_ADDRESS>Kangyou Yu
Xi’an Jiaotong-Liverpool University
Suzhou, China e-mail<EMAIL_ADDRESS>Sayan Sarcar
DMT Lab, Birmingham City University
Birmingham, UK e-mail<EMAIL_ADDRESS>Hai-Ning Liang
Xi’an Jiaotong-Liverpool University
Suzhou, China e-mail<EMAIL_ADDRESS>(corresponding author)

###### Abstract

Text selection is an essential activity in interactive systems, including
virtual reality (VR) head-mounted displays (HMDs). It is useful for: sharing
information across apps or platforms, highlighting and making notes while
reading articles, and text editing tasks. Despite its usefulness, the space of
text selection interaction is underexplored in VR HMDs. In this research, we
performed a user study with 24 participants to investigate the performance and
user preference of six text selection techniques (Controller+Dwell,
Controller+Click, Head+Dwell, Head+Click, Hand+Dwell, Hand+Pinch). Results
reveal that Head+Click is ranked first since it has excellent speed-accuracy
performance (2nd fastest task completion speed with 3rd lowest total error
rate), provides the best user experience, and produces a very low
workload—followed by Controller+Click, which has the fastest speed and
comparable experience with Head+Click, but much higher total error rate. Other
methods can also be useful depending on the goals of the system or the users.
As a first systematic evaluation of pointing$\times$selection techniques for
text selection in VR, the results of this work provide a strong foundation for
further research in this area of growing importance to the future of VR to
help it become a more ubiquitous and pervasive platform.

###### keywords:

Text Selection, Virtual Reality, Pointing Methods, Selection Mechanisms, User
Study

Human-centered computingHuman computer interaction (HCI)Interaction
paradigmsVirtual reality; Human-centered computingHuman computer interaction
(HCI)interaction techniques; Human-centered computingInteraction
designEmpirical studies in interaction design

Introduction

Text selection is essential when reading text content such as newspapers,
magazines, and academic papers to highlight important elements for later
reference or to copy/cut and transfer the content to another document,
application, or platform. Text selection has been well-researched for PCs
(e.g., using the mouse [27, 4]) and, to a lesser extent, for touchscreen
devices (e.g., using a touch-based text handler in smartphones or tablets).
More recently, there have been some initial explorations with augmented
reality (AR) head-mounted displays (HMDs) (e.g., using a smartphone for text
selection in AR HMDs [7]). However, these methods are not suitable for virtual
reality (VR) HMDs because (1) traditional input devices like the mouse require
a flat surface to operate on [42], which is not accessible or natural to VR
users, (2) using a touchscreen device or smartphone as a dedicated input
device for VR HMDs is not practical and may not lead to good performance and
usability, (3) these external devices are not visible for VR users.

Today’s VR HMDs enable users to chat with friends (e.g., via VR Facebook
Messenger), watch movies/TV shows, read text-based content (e.g., news, blogs,
papers), and even work in a virtual office (e.g., Horizon Workrooms by
Facebook) [13, 35]. These activities typically involve selecting text
fragments frequently. For instance, people use text selection for (1) sharing
information across platforms or applications (newspaper apps, Tripadvisor,
IMDB) with their friends when exchanging instant text messages, (2) searching
a movie review on IMDB before watching it, (3) making notes while reading
academic papers or books, and (4) text editing [7]. Despite the increasing
need for text selection tasks in VR HMDs, to our knowledge, there has been no
study that has explored text selection techniques for these devices,
especially based on common pointing and selection mechanisms available in
current commercial VR HMDs.

There is some research on standard mid-air pointing tasks (e.g., object
acquisition task [22, 3, 56] and virtual keyboard-based text entry task [41,
48]). However, text selection is very different from these tasks because (1)
it requires users to hold the selection mechanism until they finish hovering
over the target sentence (more precise control); (2) the density of text
information is always high during the task: letters and sentences tend to be
close to one another; (3) the size of the letters are very small for selection
compared to objects in other typical selection tasks. Nevertheless, like other
mid-air pointing tasks, text selection in VR HMDs requires (1) a mechanism for
the identification of the objects to be selected, and (2) some signal or
command to indicate their selection [32].

At present, Controller-based pointing (see Fig. Evaluation of Text Selection
Techniques in Virtual Reality Head-Mounted Displaysa) [19, 38, 54] and Head-
based pointing (see Fig. Evaluation of Text Selection Techniques in Virtual
Reality Head-Mounted Displaysb) [52, 50] are the most commonly used pointing
methods in commercial VR HMDs for identifying a target object that needs to be
selected. Controller-based pointing has become the dominant interaction method
because it (1) offers the highest throughput and best accuracy, and is often
preferred by most users [20], (2) does not cause serious hand/arm fatigue, (3)
provides rich functionalities when operating with the pointing device’s built-
in buttons. Head-based pointing (1) is important when controllers cannot be
tracked, are not around, or has no power [50, 29], and (2) represents a
fitting alternative for people with hand mobility or stability issues. With
recent developments of high accuracy and low latency hand tracking techniques,
a third pointing method—Hand-based (palm) ray-casting pointing has gained
rapid attention and has been integrated into several VR HMDs (such as the
Quest and HTC VIVE series). For instance, Quest 2 renders a ray cast from the
point between the user’s thumb and index finger to the virtual environment to
serve as a pointing mechanism (see Fig. Evaluation of Text Selection
Techniques in Virtual Reality Head-Mounted Displaysc).

There are three primary selection mechanisms in VR HMDs to allow users to
express selection—(1) Dwell: using a dwell time on the target, (2) Click:
clicking a button on the controller, and (3) Pinch: using one hand to perform
a pinch gesture [49, 33]. In this research, we conduct a study with 24
participants to evaluate the performance and usability of 6 text selection
techniques (i.e., Controller+Dwell, Controller+Click, Head+Dwell, Head+Click,
Hand+Dwell, Hand+Pinch) 111The reasons for excluding the other 3 possible
combinations are provided in subsection 2.4. for VR HMDs based on the 3
standard pointing methods (Controller, Head, Hand) and the 3 primary selection
mechanisms (Dwell, Click, Pinch). Our results suggest that Head+Click and
Controller+Click are the leading candidates where Head+Click should be
considered first since it has excellent performance (2nd in speed with a
relatively low total error rate), provides the best user experience, and
produces a very low workload. Controller+Click has the best speed and a
comparable workload as Head+Click, but because it has slightly lower user
experience scores and a worse total error rate, it should be considered as a
second option.

The main contributions of this work include: (1) A first evaluation of six
standard text selection techniques for VR HMDs regarding performance and user
preference; and (2) a set of usage recommendations that are derived from our
experiment results and observations during the experiment.

##  1 Related Work

###  1.1 Text Selection in AR/VR HMDs

To the best of our knowledge, text selection has only been done for AR HMDs.
For instance, EYEditor [11] uses a ring mouse for cursor navigation and text
selection, where a button is used for placing the cursor before and after the
text fragment to be selected while the selection is made via a touchpad. Lee
et al. [26] have employed a force-sensitive smartphone as their input device,
where users exert a force on a thumb-sized circular button to select the
desired text fragment. Similarly, Darbar et al. [7] have explored the use of a
smartphone as the input mechanism for text selection in AR HMDs. They found
that continuous touch is more efficient than discrete touch, spatial movement,
and ray casting. This prior research focusing on AR all employed an external
input device (such as a ring mouse or smartphone) for accomplishing text
selection, which is not feasible or practical for commercial VR HMDs.

Text selection in VR differs from AR in several aspects. (1) VR is less
problematic and complex: text selection in VR is less of a problematic task
than in AR, as there are no layer interference, color blending, and layout
foreground-background issues [48]. As such, text can be presented in a legible
and readable manner to users [23]. (2) VR has more input possibilities to
choose from: the primary pointing methods such as Controller-based pointing,
Head-based pointing, and Hand-based pointing are more accessible for VR users
than AR users. For instance, today’s VR HMDs often come with a dedicated
controller for input, while this is not always the case for AR HMDs (in fact,
only the Magic Leap 1 comes with a hand-held controller). Finally, (3) VR as
the future work/office space: based on current developments of AR/VR
technology, text selection in text editing and document preparation scenarios
is more practical and feasible in VR for possible future office settings
(e.g., VR office environments based on immersive HMDs [13, 35]). Given the
above, this study explores accessible interaction techniques that are
available in commercial VR HMDs for text selection.

Our work mainly focuses on user-driven text selection, where users have to
indicate both the start and the end of the text fragment. We did not focus on
automatic text selection, such as the double-click technique for word
selection and the triple-click technique for the whole paragraph text
selection derived from desktop computers with a mouse. Instead, we decided to
explore user-driven text selection because it is a more suitable starting
point for text selection studies, as it covers all types of text selection
levels (e.g., character, word, sub-word [7]). On the other hand, automatic
text selections are limited to word-, sentence-, or paragraph-level selection.
This is in line with early text entry studies where character-level input is
first explored then the swipe-/gestural-based (i.e., word-level) input
followed after.

###  1.2 Mid-air Pointing-based Interaction in HMDs

There are three main interaction approaches in VR HMDs to allow users to
identify virtual objects—Controller-based, Head-based, and Hand-based. Below,
we present a review of these pointing methods and potential selection
mechanisms that can be used to complete a mid-air interaction [32].

Controller-based. Like the mouse in desktop systems, the controller has become
a standard input device for most HMDs (e.g., Quest series, HTC VIVE series,
Magic Leap series) [19, 38, 54]. In this method, a ray is cast from the
controller to the virtual environment to serve as a pointing mechanism. The
end of the ray is akin to a cursor to assist in the object’s identification.
Users need to point to the target object by rotating or moving the controller
mid-air and, once the cursor is on the desired object, the selection is often
achieved by clicking a button on the controller [19]. When clicking the button
becomes a problem (e.g., for very small objects or an object located in very
close proximity to other objects), a dwell time can be used as the selection
mechanism [48].

Head-based. Head-based interaction has been actively studied in HMDs [1, 49,
19, 28, 6]. It has been widely adopted as a standard way for pointing at
virtual objects without using hands or hand-held pointing devices [24].
Selection for head-based pointing often relies on a dwell time [33, 29, 52], a
second modality such as clicking a button on a controller [24, 52], or making
a gesture using bare hands [49, 33]. One prior study in AR that empirically
compares selection methods shows that Dwell causes a lower error rate than
clicking a button on a keyboard and a hand-held button device, voice (verbally
confirming) [30]. A recent study in VR also shows that Dwell leads to a
significantly lower error rate than button clicking and hand gesture (i.e.,
pinch) [33].

Hand-based. Hand-based interaction is now possible in several VR HMDs (e.g.,
Quest series, HTC VIVE series). To select a nearby object [32, 54], users
first hover their hand over the target object and then make a selection by
performing a hand gesture—e.g., a pinch in the Oculus Quest 2. For a distant
object (e.g., keys on a keyboard or items on a menu), users use hand-based
pointing to point at the object followed by a hand gesture for selection. For
instance, the Quest 2 renders a ray cast from the point between the user’s
thumb and index finger to the virtual environment, and the selection of the
object can be made via a pinch gesture. In scenarios when performing gestures
can lead to errors, users can use dwell to prevent these errors.

Although these three mid-air interaction methods have been widely studied in
HMD systems [48, 52, 49, 33], their performance and usability for text
selection tasks are underexplored. Due to the increasing importance of these
tasks, understanding how these methods affect user performance and usability
is crucial to the development of efficient and usable techniques for text
selection. Therefore, this research aims to fill this gap and has conducted
the first systematic study of these pointing mechanisms in combination with
their suitable selection mechanisms for text selection in VR HMDs.

There are some other novel selection mechanisms such as eye
blinking–intentionally blinking the eye(s) [29, 28], voice input–verbally
issuing a command [51], and sound volume–increasing the sound volume [16] can
also be used. They have been explored in the context of hands-free interaction
in a recent work [31].

Table 1: Summary of the advantages and disadvantages of the six text selection methods used in our study. Technique |  Advantages |  Disadvantages
---|---|---
Controller+Dwell |  (1) easy to control, (2) minimal hand fatigue, (3) less error-prone [33] |  (1) not device-free, (2) slow due to dwell time and making users feel ’pushed’ [29]
Controller+Click |  (1) easy to control, (2) minimal hand fatigue, (3) user familiarity (mouse-like interaction), (4) tactile feedback |  (1) not device-free
Hand+Dwell |  (1) device-free, (2) less error-prone [33], (3) natural interaction |  (1) slow due to dwell time and making users feel ’pushed’ [29], (2) hand fatigue, (3) limited hand-interaction area as a result of a limited hand tracking area that can be captured via the front-facing cameras [47]
Hand+Pinch |  (1) device-free, (2) natural interaction |  (1) hand fatigue, (2) limited hand-interaction area [47]
Head+Dwell |  (1) device-free, (2) hands-free, (3) less error-prone [33] |  (1) slow due to dwell time and making users feel ’pushed’ [29], (2) potential cybersickness [53]
Head+Click |  (1) hand mobility does not affect the selection, (2) minimal hand fatigue, (3) tactile feedback |  (1) potential cybersickness [53], (2) not device-free

##  2 Evaluated Methods

This section describes how the final six combinations from the three Pointing
Methods (Controller, Head, Hand) and three Input Mechanisms (Dwell, Pinch,
Click) are implemented and operationalized in our study and why Hand+Click,
Head+Pinch, and Controller+Pinch are not considered. Table 1 summarizes the
advantages and disadvantages of our six text selection methods. All six
techniques were developed and run in Unity3D (Unity v2019.4.10f1) with default
pointing methods and input functions provided by the Oculus Unity Plugin
(OVRPlugin v1.61.0).

###  2.1 Controller-based Pointing

A ray is rendered from the controller to the virtual environment to serve as a
pointing mechanism.

Controller+Dwell (ConD). The user starts the selection of the desired text
fragment by dwelling (staying on an area for 1s) at the beginning of the first
letter and ends the selection by dwelling again at the end of the text. The
dwell duration was decided through a series of pilot studies testing a range
of thresholds from 400ms to 1s (from text entry studies [14, 50, 36, 28]).

Controller+Click (ConC). The user needs to press and hold the main button in
the controller at the beginning of the target letter to start the selection
and then release the button after the cursor reaches the end of the target
text to complete the selection.

###  2.2 Hand-based Pointing

A ray is rendered from the point between the user’s thumb and index finger to
the virtual environment to serve as a pointing mechanism. For this research,
we upgraded the tracking rate to 60Hz to improve the hand tracking performance
(i.e., lower latency for hand-based pointing and higher accuracy for pinch
gesture recognition). In addition, we controlled the lighting of the physical
environment to ensure the hand tracking was reliable and consistent during the
study.

Hand+Dwell (HandD). HandD is analogous to ConD, but the pointing is done
through Hand-based pointing.

Hand+Pinch (HandP). The user needs to use their hand to point at the beginning
of the target text, perform the “pinch and drag” gesture to start the
selection, and then release the pinch gesture at the end of the target text
fragment to end the selection.

###  2.3 Head-based Pointing

A ray is extended from the HMD towards the viewing direction into the virtual
environment.

Head+Dwell (HeadD). HeadD is analogous to ConD, with the pointing achieved by
Head-based pointing.

Head+Click (HeadC). Selection is analogous to ConC, but the pointing is done
through Head-based pointing.

###  2.4 Other Possible Combinations that Are Not Used

We did not include Hand+Click and Controller+Pinch because the existing
literature suggests that one-handed interaction is often preferred by VR users
over using two hands for interacting with objects in VR [34]. This is
especially the case for tasks that can be done with one hand, as one-handed
interaction is less physically and mentally demanding [45] and it offers
numerous benefits (e.g., allowing the other hand to hold other items or
performing other tasks [21, 10]). As such, this research has focused primarily
on using one-hand.

Head+Pinch is excluded because: (1) our pilot study suggests that users are
more likely to make errors as they frequently move their hand unintentionally
outside the ideal hand interaction area of the device when their hand is used
as a peripheral input modality [47], (2) it leads to higher fatigue compared
to Head+Click, which is already included as one of the evaluated techniques,
and (3) it is disliked by users when it is used as a selection mechanism
compared to Click and Dwell [33].

###  2.5 Testbed Environment

Figure 1 shows the test environment. An ‘instruction panel’ is located on the
left side where the participant could see the text that needed to be selected.
The ‘interaction panel’ is located at the center, where the participant needed
to use each technique to select text fragments. In addition, two buttons are
provided: ‘Delete’ for deleting the wrong selection and ‘Next’ for moving to
the next trial.

The following parameters are set based on the recommendations from previous
studies and then further tested and agreed upon by 5 users from a pilot study.
We controlled the length of the materials to be between 9-12 lines, with each
line having around 40 characters with spaces [44], in both panels. The plane
is set as 2.6m which was tested and agreed by target users, 2.6m also falls in
the recommended reading distance suggested by [8]. For the text style, we used
Sans-serif Arial with a light color [8]. Angular size was set as 1.8∘, which
was within the recommended range suggested by [8].

Visual support and feedback were provided in three ways: (1) The end of the
ray is akin to a cursor, (2) changing the color of the selected text to
yellow, (3) changing the color of the cursor when a selection was
started/stopped, (4) a visual indicator is provided for showing dwell
progress. We did not visualize the ray because users from our pilot studies
suggest the cursor alone is more effective than a combination of visualizing
the ray and the cursor in helping them understand where they are pointing.

Figure 1: A) We used the same experiment environment for all conditions. An
‘instruction panel’ is located on the left side, slightly tilted towards the
user. The ‘interaction panel’ is located in the center, slightly tilted
towards the user. B) A picture of the experiment setting showing how a
participant is performing a text selection task.

##  3 Experiment

###  3.1 Participants and Apparatus

We recruited 24 unpaid participants (12 males; 12 females) between the ages of
18-30 (M=21, SD=2.06) from a local university campus through a database of
participants. They all had no issues reading the content clearly within the VR
environment and no difficulties moving their arms, hand, and heads. 23
participants were right-handed. 17 participants had previous experience with
VR HMDs, but only 3 were regular VR users (weekly) and none of them had
interacted with the Oculus Quest 2—the device used in this experiment.
Participants were seated on the office chair during the experiment and were
allowed to rest their hands on the chair handles if needed (see Fig. 1B).

###  3.2 Design

The experiment followed a one-way within-subjects design with Interaction
Techniques (HandD, HandP, HeadD, HeadC, ConD, and ConC) as the independent
variable. For each condition, participants needed to complete 3 training
trials (1 short, 1 medium, and 1 long text fragments) and 27 trials (9 short,
9 medium, and 9 long text fragments following a randomized order of
appearance) which were randomly sampled from a corpus of standardized English
reading assessment [37]. Each selection target would only appear once in a
specific condition. The order of the conditions was counterbalanced across
participants to avoid learning effects with Sentence Lengths also randomized.
Excluding the training texts, we collected 3888 trials (24 participants
$\times$ 6 interaction techniques $\times$ 27 texts). Although the local area
had no local COVID-19 cases for 12 months before the experiment, we cleaned
and sanitized the device before and after each participant’s turn and followed
extra safety measures to ensure the safety of the participants and researchers
(e.g., wearing a mask and staying at a safe distance).

For assessing the text selection performance and experience, we collected the
following measurements:

  * •

Objective: (1) Task-completion time: in line with the previous studies in
pointing experiments, we only report task completion time where there were no
errors during the trials to handle the speed-accuracy trade-off. The task
completion time for each trial is defined as the time from when the cursor
first hovers over the first target letter to the time they complete the
correct selection. By recording in this way, the time spent on clicking the
button, performing the pinch, and dwelling are included for analysis. (2)
Total error rate: (the number of wrong sentences + the number of deletions) /
total number of attempts, (3) Not corrected error rate: the number of wrong
sentences / total number of sentences.

  * •

Subjective: NASA-TLX questionnaire [15] to measure workload, User Experience
Questionnaire (UEQ) [25] to measure user experience, and comments on
advantages and disadvantages of each technique plus users’ ranking of the text
selection techniques.

###  3.3 Procedure

Before the experiment began, participants were told the goal of the research
and the experimental procedure. Then, they needed to fill out a demographic
questionnaire (e.g., age, gender, and experience with VR) and signed the
consent to participate in the experiment. In addition, they were told to
finish the trials as fast and as accurately as they could. Error correction
was allowed by using the delete button in the VR scene.

Before each condition started, the corresponding text selection method was
explained to the participants, who then had a practice session with three
warm-up selections before the experiment stage (27 text selections). After
each condition, participants needed to fill out a post-condition questionnaire
(NASA-TLX and UEQ). They proceeded to the next condition when they felt rested
to avoid any fatigue. Once they completed all conditions, they needed to
complete a post-experiment questionnaire and a structured interview. The whole
experiment lasted around 70 minutes for each participant.

Table 2: Performance data for each Interaction Technique among three Sentence Lengths, mean (SD). The top 3 techniques of each condition are presented by Roman numerals (I: light green; II: darker light green; and III: blue-green). Performance Metrics |  Sentence Lengths |  ConC |  ConD |  HandP |  HandD |  HeadC |  HeadD
---|---|---|---|---|---|---|---
Task- |  Small |  mycolor1I: 1.44 (0.51) |  mycolor3III: 3.08 (1.75) |  4.49 (3.79) |  3.86 (1.23) |  mycolor2II: 1.69 (0.35) |  3.2 (0.87)
completion |  Medium |  mycolor1I: 2.11 (0.65) |  mycolor3III: 3.28 (0.71) |  5.20 (2.64) |  4.96 (1.65) |  mycolor2II: 2.52 (0.66) |  4.38 (1.87)
time |  Long |  mycolor1I: 2.39 (0.63) |  mycolor3III: 3.71 (0.70) |  5.22 (2.25) |  5.20 (1.94) |  mycolor2II: 2.92 (0.72) |  4.98 (1.47)
TER |  Small |  25.7% (18.1%) |  mycolor1I: 9.4% (9.3%) |  32.9% (19.0%) |  25.5% (17.8%) |  mycolor2II: 11.5% (13.8%) |  mycolor3III: 12.3% (13.3%)
|  Medium |  18.1% (13.3%) |  mycolor1I: 8.3% (13.0%) |  28.2% (17.8%) |  22.3%(14.4%) |  mycolor3III: 10.8% (10.9%) |  mycolor2II: 8.7% (10.3%)
|  Long |  20.7% (13.4%) |  mycolor1I: 6.1% (9.9%) |  30.7% (15.7%) |  21.9% (14.3%) |  mycolor3III: 12.0% (13.1%) |  mycolor2II: 10.8% (9.3%)
NCER |  Small |  1.6% (4.9%) |  mycolor2II: 1.2% (3.3%) |  3.4% (7.7%) |  1.9% (4.6%) |  mycolor1I: 0.0% (0.0%) |  mycolor2II: 1.2% (3.4%)
|  Medium |  mycolor3III: 1.3% (3.9%) |  1.6% (4.5%) |  3.7% (8.2%) |  1.4% (3.2%) |  mycolor1I: 0.4% (2.0%) |  mycolor2II: 0.9% (4.4%)
|  Long |  mycolor1I: 1.6% (3.3%) |  2.4% (6.0%) |  4.2% (9.4%) |  3.6% (7.1%) |  mycolor2II: 2.0% (4.8%) |  mycolor3III: 2.2% (5.8%)

###  3.4 Results

We used Shapiro-Wilks tests and Q-Q plots to check if the data had a normal
distribution. All tests are with two-tailed p values. Effect sizes were
reported whenever feasible ($\eta_{p}^{2}$). Performance analysis. For
normally distributed data, we employed two-way repeated-measures ANOVAs with
Interaction Techniques (six techniques) and Sentence Lengths (short, medium,
long) as the within-subjects variables. For data that were not normally
distributed, we processed the data through Aligned Rank Transform (ART) [46]
before using repeated-measure ANOVAs with the transformed data. Bonferroni
corrections were used for all pairwise comparisons.

Experience analysis. For normally distributed data, we employed one-way
repeated-measures ANOVAs with Interaction Techniques as the within-subjects
variable. For data that were not normally distributed, we conducted non-
parametric Friedman tests for the ranking. Bonferroni correction was used for
pairwise comparisons and Greenhouse-Geisser adjustment was used for degrees of
freedom if there were violations of sphericity.

####  3.4.1 Performance

Task Completion Time. To handle the speed-accuracy trade-off for task
completion time, we followed the analysis process from pointing experiments
where trials with errors are excluded [57, 55, 43]. In total, we collected
3888 trials (24 participants $\times$ 6 interaction techniques $\times$ 27
texts) besides training trials. To analyze selection time, we discarded trials
in which participants made a wrong selection (709 error trials, 18.2$\%$), and
removed outliers (65 trials, 1.7$\%$), in which the selection time was more
than three standard deviations from the mean (mean $\pm$ 3std.) in each
condition. In total, there were 3179 success trials used for analyzing the
task completion time.

There was a statistically significant main effect of Interaction Techniques on
task completion time ($F_{5,391}=145.464,p<.001,\eta_{p}^{2}=.590$). Post-hoc
analysis with Bonferroni correction suggested that (1) ConC outperformed all
other techniques (all $p<.001$), (2) HeadC outperformed all techniques except
ConC (all $p<.001$), (3) ConD outperformed HandD, HandP, HeadD (all $p<.001$).
Table 2 shows the mean task completion time among the 6 Interaction Techniques
across 3 Sentence Lengths.

There was a significant main effect of Sentence Lengths on Task Completion
Time ($F_{2,391}=68.972,p<.001,\eta_{p}^{2}=.222$). Post-hoc analysis with
Bonferroni correction suggested that (1) participants completed Small faster
than Medium and Long (both $p<.001$), and (2) participants also completed
Medium faster than Long ($p<.001$). We did not find any interaction effect of
Sentence Lengths $\times$ Interaction Techniques
($F_{10,391}=0.981,p=.459,\eta_{p}^{2}=.019$).

Total Error Rate (TER). There was a statistically significant main effect of
Interaction Techniques on TER ($F_{5,391}=34.391,p<.001,\eta_{p}^{2}=.270$).
Post-hoc analysis with Bonferroni correction suggested that (1) ConC
outperformed HandP ($p<.05$), (2) ConD outperformed ConC, HandD, and HandP
(all $p<.001$), (3) HeadC outperformed ConC, HandD, and HandP (all $p<.001$),
(4) HeadD outperformed ConC, HandD, HandP (all $p<.001$). There was no
significant main effect of Sentence Lengths
($F_{5,391}=2.367,p=.095,\eta_{p}^{2}=.010$) and the interaction effect of
Interaction Techniques $times$ Sentence Lengths
($F_{10,391}=0.406,p=.944,\eta_{p}^{2}=.008$). Details of TER data can be
found in Table 2.

Not Corrected Error Rate (NCER). Table 2 presents the NCER data for each
Interaction Technique. There were significant effects of Interaction
Techniques ($F_{5,391}=8.217,p<.001,\eta_{p}^{2}=.073$), Sentence Lengths
($F_{5,391}=6.455,p<.01,\eta_{p}^{2}=.024$), and Interaction Techniques
$times$ Sentence Lengths ($F_{10,391}=2.524,p<.01,\eta_{p}^{2}=.046$). Post-
hoc analysis with Bonferroni corrections was conducted based on the
interaction effect, however, we could not find any significant difference.

Figure 2: UEQ subscale ratings of the tested methods with respect to comparison benchmarks. Table 3: ANOVA test results for the UEQ subscales. Significant results where $p<.05$ are highlighted with light green and $p<.001$ with dark green. |  Interaction Technique |  Post-hoc
---|---|---
Attractiveness |  mycolor2 $F_{3.307,76.066}=8.405,p<.001,\eta_{p}^{2}=.268$ |  HandD$<$HeadC ($p<.01$), HandD$<$ConC ($p<.01$), HandP$<$HeadC ($p<.01$), HandP$<$ConC ($p<.05$), ConD$<$HeadC ($p<.01$)
Perspicuity |  mycolor2 $F_{5,115}=12.631,p<.001,\eta_{p}^{2}=.354$ |  HandD$<$HeadC ($p<.01$), HandD$<$ConC ($p<.001$), HandP$<$HeadC ($p<.001$), HandP$<$ConC ($p<.001$), HeadD$<$HeadC ($p<.05$), ConD$<$HeadC ($p<.05$), ConD$<$ConC ($p<.05$)
Efficiency |  mycolor2$F_{5,115}=15.801,p<.001,\eta_{p}^{2}=.407$ |  HandD$<$HeadC ($p<.001$), HandD$<$ConC ($p<.001$), HandP$<$HeadC ($p<.001$), HandP$<$ConC ($p<.001$), HeadD$<$HeadC ($p<.05$), ConD$<$HeadC ($p<.01$) ConD$<$ConC ($p<.05$)
Dependability |  mycolor2 $F_{3.728,85.746}=11.077,p<.001,\eta_{p}^{2}=.325$ |  HandD$<$HeadC ($p<.01$), HandD$<$ConC ($p<.01$), HandP$<$HeadC ($p<.001$), HandP$<$ConC ($p<.01$), ConD$<$HeadC ($p<.01$)
Stimulation |  mycolor1 $F_{3.132,72.036}=2.817,p<.05,\eta_{p}^{2}=.109$ |  HeadD$<$HeadC ($p<.05$), ConD$<$HeadC ($p<.01$)
Novelty |  mycolor2 $F_{3.525,81.073}=5.835,p<.001,\eta_{p}^{2}=.202$ |  ConC$<$HeadD ($p<.01$), ConC$<$HeadC ($p<.05$)
Figure 3: The mean responses for the 6 components of the NASA TLX
questionnaire. Error bars indicate 95% confidence interval.

####  3.4.2 User Preference

UEQ. For the average UEQ scores, HeadC was rated the best (M=1.89, SD=0.77)
and HandD (M=0.72, SD=1.26) the worst. ANOVA tests showed a significant main
effect of Interaction Techniques
($F_{3.388,77.935}=8.622,p<.001,\eta_{p}^{2}=.273$) on the UEQ mean score.
Post-hoc pairwise comparisons suggested that (1) HeadC had a significantly
higher average UEQ score than HandD ($p<.01$), HandP ($p<.001$), and ConD
($p<.01$), (2) ConC had a significantly higher average UEQ score than HandD
($p<.01$) and HandP ($p<.05$).

Regarding the UEQ subscales, ANOVA tests yielded significant main effects of
Interaction Techniques on all UEQ subscales. Table 3 shows the detailed
results of the ANOVA tests. Details of each UEQ subscale score can be found in
Figure 2. HeadC was rated above average to excellent (mainly good to excellent
except for only one participant who rated it above average), ConC was rated
below average to excellent (mainly good to excellent except for one
participant who rated it below average). At the same time, ConD and HeadD were
comparable with the benchmark, and HandD, and HandP had worse scores than the
benchmark.

Workload. For overall task workload, ConC was rated the best (M=23.64,
SD=19.32) and HandP (M=54.43, SD=20.72) the worst.An ANOVA test showed a
significant main effect of Interaction Techniques
($F_{3.168,72.871}=12.352,p<.001,\eta_{p}^{2}=.349$) on the overall task
workload ratings. Post-hoc pairwise comparisons indicated that (1) users
experienced less workload when use HeadC than HandD ($p<.05$) and HandP
($p<.001$), (2) users experienced less workload in ConC than HandD ($p<.01$)
and HandP ($p<.001$), (3) users experienced more workload in HandP than HeadD
($p<.01$) and ConD ($p<.05$). Regarding each NASA-TLX workload subscale, ANOVA
tests yielded significant main effects of Interaction Techniques among all
subscales. Details of the results of the ANOVA tests can be seen in Table 4
and of the workload subscales in Fig. 3.

Table 4: ANOVA test results for NASA-TLX subscales. Significant results where $p<.05$ are shown in light green and $p<.001$ in dark green. |  Interaction Technique |  Post-hoc
---|---|---
Mental |  mycolor2 $F_{5,115}=11.740,p<.001,\eta_{p}^{2}=.338$ |  HeadC$<$HandD ($p<.05$), ConD$<$HandD ($p<.01$), ConC$<$HandD ($p<.001$), HeadC$<$HandP ($p<.01$), ConD$<$HandP ($p<.01$), ConC$<$HandP ($p<.001$)
Physical |  mycolor2 $F_{2.657,61.121}=7.990,p<.001,\eta_{p}^{2}=.258$ |  ConC$<$HandD ($p<.05$), HeadC$<$HandP ($p<.05$), ConC$<$HandP ($p<.01$)
Temporal |  mycolor1 $F_{5,115}=2.919,p<.05,\eta_{p}^{2}=.113$ |  N/A
Effort |  mycolor2 $F_{2.739,63.006}=11.701,p<.001,\eta_{p}^{2}=.337$ |  ConC$<$HandD ($p<.05$), HeadD$<$HandP ($p<.01$), HeadC$<$HandP ($p<.01$), ConD$<$HandP ($p<.05$), ConC$<$HandP ($p<.001$)
Frustration |  mycolor2 $F_{3.210,73.833}=8.730,p<.001,\eta_{p}^{2}=.275$ |  HeadC$<$HandD ($p<.05$), ConC$<$HandD ($p<.05$), HeadC$<$HandP ($p<.01$), ConC$<$HandP ($p<.01$)
Performance |  mycolor2 $F_{5,115}=5.474,p<.001,\eta_{p}^{2}=.192$ |  ConC$<$HandD ($p<.05$), HeadC$<$HandP ($p<.05$), ConC$<$HandP ($p<.05$)

Ranking. The ranking of conditions shows a preference for HeadC (11 ranked it
first while 9 ranked it second) and ConC (11 ranked it first and 7 ranked it
second) among the techniques. They were followed by ConD (10 ranked it third
while 8 ranked it fourth) and HeadD (5 votes for third and 8 for fourth) as
their backup. Hand-based techniques were generally the worst, being either in
the fifth place (HandD with 14 votes and HandP with 5 votes) or the final
option (HandP with 13 votes and HandD with 6 votes).

####  3.4.3 Qualitative Feedback

In general, most participants stated positive comments for HeadC:
”simple/easy” (N=7), ”fast” (N=6), “accurate” (N=9), and “creative/innovative
and practical” (N=3), or simply stated, ”the best” (N=2). The only negative
comments were ”slightly dizzy because of the head movements” (N=2). Similarly,
most participants left positive feedback for ConC: ”easy/simple and practical”
(N= 17). The reasons given were that ”it just like how we interact with PC
with a mouse” (N=5). However, we also observed negative comments
”tiredness/mobility of the hand lead to selection error during the selection”
(N=11), with others stating that this technique was ”somewhat boring” (N=3).

In general, participants felt positive about HeadD and ConD. They were
perceived to be ”fast, easy, easy to learn” (N=11), ”creative” (N=3). The
disadvantage of these methods was related to dwell-based selection ”difficult
to hold the pose for dwell” (N=10), ”often suffer the last sec movement which
made me have to dwell again” for Controller users (N=10), and ”dizzy/eye
tiredness” for Head users (N=2).

Although HandD and HandP were perceived to be ”creative/innovative and fun”
(N=8), it suffers from ”tiredness” (N=10) and sometimes are very ”inaccurate
(because of the difficulty in holding/performing gestures” (N=3).

##  4 Discussion

###  4.1 Task Performance

In line with a previous study on text entry [41] and supported by the
bandwidth of the human muscle groups [5], we found that the default VR
Interaction Techniques ConC (i.e., Controller+Click) is the best candidate
regarding the task completion speed, followed by HeadC (i.e., Head+Click), and
then ConD (i.e., Controller+Dwell). Despite being the fastest option, ConC led
to a very high TER (21.5%, in line with previous results in AR HMDs with a
similar interaction style [7]) and NCER (1.5%) while HeadC had a much more
acceptable TER (11.4%) and NCER (0.8%). Consistent with a previous study that
compares selection techniques [33, 9], Dwell-based techniques, as expected,
made the least error during selection, ConD had the lowest total error rate
while HeadD had the second lowest total error rate.

Hand-based interaction has been implemented in several VR HMDs (e.g., in the
VIVE and Quest series). Users can now use it for system control tasks such as
changing the home scene, opening/closing an application, adjusting the
brightness setting, or entering texts. However, this type of interaction does
not seem suitable for long-term fast-paced pointing tasks for selecting small
objects. In line with the results from text entry studies (e.g., [48]), we
found that hand-based methods (HandD [i.e., Hand+Dwell] and HandP [i.e.,
Hand+Pinch]) not only had the worst task completion speed but also caused the
highest number of errors. A possible reason could be due to their hand’s
stability; it is generally challenging to hold their hands in mid-air on a
consistent and stable basis [48], even if they could use the chair handles or
a table to support their hands. Another reason is that the accuracy of hand-
based interaction is just not sufficient enough in today’s HMDs [39].

In addition, we observed that the Sentence Lengths could affect the task
completion time, which is as expected because participants need to move more
distance across the paragraphs.

###  4.2 User Experience

Overall, HeadC and ConC are the leading options based on user experience.
HeadC provided a better user experience than HandD, HandP, and ConD (e.g.,
average UEQ, attractiveness, perspicuity, efficiency, and dependability). In
addition, HeadC was rated better than HeadD (i.e., Head+Dwell) and ConD on
stimulation. ConC provided a better user experience than HandD and HandP
(i.e., average UEQ, attractiveness, perspicuity, efficiency, and
dependability). However, this method was considered less novel compared to
HeadD and HeadC because it mimics how the mouse works for desktop and laptop
computers.

When we compared these 6 text selection methods with the benchmark scores
provided in [40], HeadC and ConC were rated mostly good to excellent, except
for the Stimulation subscale, which was only above average for HeadC and below
average for ConC. HeadD and ConD were mostly rated with average scores, with
hand-based methods rated mostly bad to below average. A possible explanation
for why hand-based methods were rated with low user experience scores may be
because they could cause hand/arm fatigue, a common issue for mid-air
interaction [48, 49]. Another good explanation would be that they are
perceptibly slower and had more errors than controller-based and head-based
approaches.

###  4.3 Workload

In line with the UEQ results, ConC and HeadC are the leading options according
to the NASA-TLX workload scores. They were comparable across NASA-TLX
subscales. ConC had a lower workload than HandD and HandP in all NASA-TLX
subscales, except for Temporal. We also found HeadC outperformed HandP in the
Mental, Performance, Effort, and Frustration scales. In addition, HeadC
outperformed HandD on both Mental and Frustration scales. As mentioned by
participants, the workload is high for hand-based methods because the need to
hold their hands mid-air on a consistent and stable basis is difficult, which
is in line with previous observations [48]. It is worth noting that the Quest
2 already provides an overall stable hand tracking at a 60HZ refreshing rate.
We also made sure that the light condition was consistent and in good
condition during the experiment to ensure the tracking was stable. Therefore,
we believe that the high workload for hand-based methods might be due to
arm/hand fatigue [17], which would have made the task more complicated and
unnecessarily cumbersome [2].

###  4.4 Recommendations for Text Selection in VR HMDs

The recommendations derived from our experiment are based on a set of
considerations, which include performance, experience, the workload of the
interaction technique, the availability of the controller, and users’ physical
conditions, especially for hand/arm control. They can be divided into two
groups based on the goals of the system or the users:

Performance. If a controller is available, users could choose Head+Click since
it can lead to the second fastest task completion speed and has an acceptable
total error rate. If rapid neck movement is an issue for users to rotate their
head for pointing, we suggest using Controller+Dwell and Controller+Click as
alternative options. Controller+Dwell should be considered before
Controller+Click when accuracy is needed since it has a significantly lower
total error rate. If correcting errors during the session is not an issue,
users can use Controller+Click. However, users should be comfortable with a
relatively higher total error rate due to hand mobility limitations [17].
Head+Dwell should be considered as the first option in both device-free and
hands-free scenarios. As for hands-based interaction techniques (i.e.,
Hand+Dwell and Hand+Pinch), they should be minimized due to poor performance
(in both speed and accuracy).

User Preference. We suggest considering Head+Click and Controller+Click as the
top options when a controller is available. However, their usage should be
based on the user’s preference for usability and workload. If a low workload
is preferred, Controller+Click can be chosen since it produces the lowest
workload and provides an excellent user experience score. When a good user
experience is preferred and users have no neck injuries or concerns about neck
fatigue, Head+Click should be considered as the first option because it
provides the best user experience score and produces a very low workload.

Head+Dwell should be considered as the first option in device-free scenarios
since it provides a better overall user experience and produces a lower
workload than hand-based techniques (i.e., Hand+Dwell and Hand+Pinch).
Hand+Dwell and Hand+Pinch should be minimized in rapid pointing-based text
selection tasks since they generate a high overall workload and low overall
user experience, with the potential to lead to the gorilla arm syndrome [17].
However, they can be considered when the user has an issue with rotating their
neck.

The above recommendations are derived from our study where the text selection
task is assumed to be the primary task. Their inclusion in actual applications
should be dependent on the context of use, target users, and availability of
equipment.

###  4.5 Limitations and Future Work

One limitation of this research is that the study only consists of a single
session. It will be useful to perform longer experimental sessions (e.g., 1 or
2 sessions over consecutive 4-5 days like text entry studies [53, 50, 12]).
Nevertheless, our study, as the first one to explore text selection in HMDs
and to evaluate 6 interaction techniques from 3 common pointing methods
(Controller, Head, Hand) and 3 selection mechanisms (Dwell, Click, Pinch),
provides a valuable starting pointing for future studies on text selection in
VR HMDs.

This work has focused primarily on user-driven text selection where the text
is displayed outside users’ hand reach area (i.e., far-field interaction).
There are situations where (1) the text may be presented closer to users’ view
and within the reach of their arms, which requires near-field interaction to
manipulate the text, or (2) a user wants to select a single word or a whole
sentence or paragraph akin to a double-clicking of the mouse in desktop
systems (see subsection 1.1). Because we want to make text selection as fast
and usable as possible and in line with how users do this on their desktops,
we plan to expand our work and explore new techniques and approaches that are
suitable for near-field text manipulation and also can be used for automatic
text selection of words, sentences, and paragraphs in the future.

Some design considerations, such as dwell duration, visualization of the ray,
and interaction distance, that have been determined via pilot studies could
also be revisited to allow their fine-tuning and customization by users in
actual application scenarios. In addition, we considered text selection as the
primary task during the interaction with VR HMDs and future work will explore
techniques to support a seamless experience on text selection to avoid the
need for users to switch between techniques and interaction platforms.

We did not consider dual-hand text selection methods, given our focus on
available pointing and selection mechanisms of current VR HMDs. However, they
are worth exploring in the future because, when designed appropriately to
minimize workload and maximize efficiency, they can extend the range of
possibilities for users. For instance, researchers could consider: (1)
Hand+Hand: users use their dominant hand for pointing and the other hand for
selection, or vice-versa; (2) Controller+Controller: One controller is used
for pointing and the other one for selection; (3) Hand+Controller: users can
use their dominant hand for pointing and the controller for selection; (4)
Controller+Hand: users can use a controller with their dominant hand for
pointing and their non-dominant hand for selection [18]. This exploration will
produce additional results and help develop VR systems that are more usable
and will take us one step closer to a practical immersive office of the
future.

##  5 Conclusion

In this work, we have implemented six text selection techniques that resulted
from the combination of three pointing methods (Controller, Head, Hand) and
three selection mechanisms (Dwell, Click, Pinch) that are widely available and
frequently used in commercial virtual reality (VR) head-mounted displays
(HMDs). Then, we empirically evaluated these six techniques through a user
study with 24 participants to assess their performance and user experience. In
general, our results suggest that Head+Click and Controller+Click are the
leading candidates among the 6 techniques. Head+Click should be considered as
the first option since it has an excellent performance, provides the best user
experience, and produces a very low workload—followed by Controller+Click,
which has comparable results with Head+Click, except for having a higher total
error rate. Controller+Dwell has the lowest total error rate and should be
preferred if a low error rate is a plus for the user. Our results also show
that current hand-based techniques should be the last possible options for
rapid text selection tasks, except when users have difficulties with neck
motions.

###### Acknowledgements.

The authors want to thank the participants who joined the study and the
reviewers for their insightful comments and useful suggestions that helped
improve our paper. This work was supported in part by Xi’an Jiaotong-Liverpool
University (XJTLU) Key Special Fund (#KSF-A-03), XJTLU Research Development
Fund (#RDF-17-01-54), and Future Networks Research Fund (#FNSRFP-2021-YB-41).

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