Armstrong: An Empirical Examination of Pointing at
Non-Dominant Arm-Anchored UIs in Virtual Reality
Zhen Li

Joannes Chan

Joshua Walton

Chatham Labs & University of Toronto
Toronto, Canada
zhen@dgp.toronto.edu

Chatham LabsToronto, Canada
joannes@chathamlabs.com

Facebook Reality Labs
Redmond, United States
joshuawalton@fb.com

Hrvoje Benko

Daniel Wigdor

Michael Glueck

Facebook Reality Labs
Redmond, United States
benko@fb.com

Chatham Labs & University of Toronto
Toronto, Canada
daniel@dgp.toronto.edu

Chatham LabsToronto, Canada
mike@chathamlabs.com

ABSTRACT

1

In virtual reality (VR) environments, asymmetric bimanual interaction techniques can increase users’ input bandwidth by complementing their perceptual and motor systems (e.g., using thedominant hand to select 3D UI controls anchored around the nondominant arm). However, it is unclear how to optimize the layoutof such 3D UI controls for near-body and mid-air interactions. We
evaluate the performance and limitations of non-dominant armanchored 3D UIs in VR environments through a bimanual pointing
study. Results demonstrated that targets appearing closer to theskin, located around the wrist, or placed on the medial side of theforearm could be selected more quickly than targets farther awayfrom the skin, located around the elbow, or on the lateral side of theforearm. Based on these results, we developed Armstrong guidelines, demonstrated through a Unity plugin to enable designers tocreate performance-optimized arm-anchored 3D UI layouts.

As commercial head-mounted displays for augmented reality (AR)and virtual reality (VR) have grown in prevalence, there has been aresurgence in egocentric 3D interaction techniques. Many of thesetechniques enable users to move throughout a 3D environment anddirectly manipulate objects in a virtual scene using hand-held controllers. For example, in Blocks, a double-sided palette is attached
to a controller in the user’s non-dominant hand [18], with various
drawing tools on one side and selectable colors on the other side
(Fig. 1a). Tilt Brush anchors a virtual prism with multiple layersof UI controls to the non-dominant controller [17], which the user
can scroll through using the dominant controller (Fig. 1b).
A variety of techniques use this “palette and brush” metaphor [17,18, 21, 49], which enables users to leverage their proprioceptive
senses to locate their non-dominant hand in 3D space without needing to explicitly search for the menu [38, 44, 56, 65]. Proprioception
thus creates a frame of reference that reduces the visual and mental
effort needed to find the palette [26]. These techniques exploit theasymmetric nature of the hands by capitalizing on bimanual action
feedback [8, 20] to minimize task flow interruptions, so that a user’s
attention is not diverted from her main task every time a paletteselection is made.
As applications grow in complexity and body-tracking technologies mature, we envision that the design of UI control layouts will
migrate beyond the space around the hands (hand-anchored UIs),
to the space around the arms (arm-anchored UIs). A 3D palette thatwraps around the non-dominant arm could provide a user with alarger working volume while leveraging the benefits of bimanualinteractions and proprioception. This trend towards using the arm
for interaction can be observed in on-skin interaction research,which has considered fingers, palms, and forearms as interactive
surfaces [6, 14, 22, 24, 51, 61, 67]. We further evaluated the benefitsof arm-anchored UI controls, through a prototype design exploration that inspired the current study. In a running scenario, arunning app could show the user’s current progress on one side ofher arm and a music app on the other side could show the current
playlist, enabling the user to quickly glance at an app by rotating
her forearm (Fig. 1c). A user could also invoke functions using
swipe gestures on her forearm (e.g., to pause music; Fig. 1d). These
examples demonstrated the potential usefulness of capitalizing on a

CCS CONCEPTS• Human-centered computing → Virtual reality; Empiricalstudies in HCI .

KEYWORDS
Arm-anchored UIs; Virtual reality; Bimanual interaction
ACM Reference Format:
Zhen Li, Joannes Chan, Joshua Walton, Hrvoje Benko, Daniel Wigdor,
and Michael Glueck. 2021. Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality. In CHI Conference on Human Factors in Computing Systems (CHI ’21), May 8–13, 2021,
Yokohama, Japan. ACM, New York, NY, USA, 14 pages. https://doi.org/10.
1145/3411764.3445064

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and/or a fee. Request permissions from permissions@acm.org.
CHI ’21, May 8–13, 2021, Yokohama, Japan© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8096-6/21/05. . . $15.00
https://doi.org/10.1145/3411764.3445064

INTRODUCTION

CHI ’21, May 8–13, 2021, Yokohama, JapanLi et al.

Figure 1: (a, b) Hand-anchored UIs from commercial applications. (a) A 2D color palette is attached to the user’s non-dominant
controller and a brush is attached to the dominant controller [18]. (b) A variety of brush types and UI controls are distributed ona triangular prism attached to the non-dominant controller and can be selected via ray-casting by the dominant controller [17].
(c, d) Arm-anchored UIs from our prototype: a music app and a running app are rendered on opposite sides of the user’s armand (c) the user can rotate her arm to switch between these apps or (d) swipe her arm to interact with the UI controls.
user’s proprioceptive sensing of her arms to deploy arm-anchored
UI controls in 3D environments.
Although commercial applications and prior research projects
have explored attaching controls to user’s hands, wrists, or arms,
these applications have largely migrated UI design practices from
2D WIMP interfaces. In contrast, using the full 3D space around theuser’s limbs might enable opportunities to “display more items in
more varied layouts” [5]. Limited research has focused on developing
design guidelines for 3D UIs around a user’s arms, leading to open
research questions such as: What areas around a user’s arm canbe most quickly and accurately targeted? What is a performanceoptimized layout of 3D UI controls around the arm? Can users
perceive and leverage individual axes for interaction (e.g., along
the longitude, latitude, or height dimensions around the arm)?
This work thus seeks to understand the appropriateness andusability of arm-anchored UIs within the context of AR and VR.
Due to the limited field of view (FOV) of available AR devices, anexperiment was conducted in VR to investigate user performance
during pointing tasks, while targets were located in various positions around the arm. We measured the Fitts’ throughput (TP)of user interactions [16, 30] to generate a heatmap and articulate
the usefulness of different combinations of longitude, latitude, andheight dimensions around the arm. We synthesized the quantitativeand qualitative results to describe 72 regions in terms of visibility,
reachability, and comfort.
The contribution of this work is thus twofold. First, we reportanalysis of quantitative and qualitative results from a pointing study
in VR that identified regions with different levels of performanceand subjective preference around the arm (e.g., targets closer tothe skin, around the wrist, or on the medial side of the forearm
can be selected quicker than targets in other locations) and unique
movement strategies that resulted (e.g., moving the arm to the center of the FOV). Second, we present Armstrong design guidelines,
and implemented a Unity plugin to demonstrate how to create
performance-optimized arm-anchored 3D UI layouts.

2

RELATED WORKThis work was inspired and informed by previous research into
theories of proprioception and peripersonal space, arm-anchored
UI interaction techniques, and existing studies of pointing tasks
around various body parts.

2.1

Proprioception and Peripersonal SpaceAs first identified by Sherrington in 1907 [53], proprioception enables humans to sense the position and movement of their limbs
through their sensory neurons [35, 56]. Neuropsychological experiments have further identified that there is a peripersonal space thatsurrounds our bodies [12, 19, 47, 48]. This space acts as an intermediary between the visual space that we perceive through our eyes
and the tactile, proprioceptive space we perceive on our body. Because this intermediary space is in such close proximity to our body,it enables us to form a unique connection with the objects within
our reach. Prior studies have supported the existence of a “handcentered coordinate system” within the peripersonal space [19, 33].
Makin et al., for example, identified brain areas that exhibited significantly stronger activation patterns to visual stimuli the closer
that the stimuli were to the hand [34]. Participants were also foundto detect targets near the hand with a faster response time thantargets that appeared farther away from the hand, suggesting that“the presence of the hand prioritized the space near the hand for attentional processing” [46]. These findings suggest that there can bebenefits to leveraging (i) the proprioceptive senses to determine
the position and orientation of the arms and (ii) the peripersonalsensing of objects surrounding our arms for interaction in VR.

2.2

Arm-Anchored User Interfaces

Bimanual interactions induce asymmetric divisions of labor, where
the motion of the non-dominant hand creates a frame into which
the motion of the dominant hand inserts content [20, 32]. Guided
by these findings, Bier et al. proposed Magic Lenses, where thenon-dominant hand created a see-through interface as context andthe dominant hand acted within that context [8]. The Worlds-inMiniature (WIM) metaphor represented surfaces in a virtual environment held by the non-dominant hand, while the dominant
hand held a “buttonball” for selection and manipulation [54]. The“hand-relative-to-hand” frame of reference has been demonstrated
to provide perceptual cues that are independent of visual feedback [26], motivating the use of both hands for interaction in VR
to increase the degree of manipulation. Commercial products havealso explored attaching UI elements directly to the non-dominant
hand (e.g., Hand Menu for HoloLens 2 [37] and Wearable Menu
for Leap Motion [27]). This work focuses on 3D layouts of interfaces that are anchored to the non-dominant arm while interaction

Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality

is performed by the dominant hand, echoing the aforementioned
benefits of asymmetric divisions of labor [8, 20, 32, 54].
Proprioception has been leveraged to help users locate UI elements projected directly onto the skin of their hands. Prototypes
have utilized the skin of the palm or fingers as input or output devices, e.g., keyboards [22, 61, 67], trackpads [62], color palettes [22,67], TV remotes [14], menu containers [10, 21], or displays for various applications [39]. As the palm and fingers have small surface
areas, others have investigated the use of the entire arm for interaction. Azai et al. proposed a menu widget that would enable
touching, dragging, sliding, and rotating operations on a user’s forearm [4]. Researchers have also developed vision-based approaches
and used bio-acoustic sensors to perceive touch input on the skinand render menu items on the forearm or palm for output [22–24, 67]. Other projects have used the forearm as a trackpad for 2D
cursor movement or stroke commands [7, 43, 51, 67]. In our work,
we hypothesize that arm-anchored 3D interfaces benefit from proprioception because the UI controls are always within reach andusers can move their arms to minimize occlusions [38].

during pointing tasks in the 3D space around the arm. This work
thus complements the literature’s understanding of users’ pointing
behaviours. The corresponding Armstrong guidelines are proposed
to support the future design of such interactions.

3

2.3

Empirical Studies on Pointing Tasks

USER STUDY

To investigate the utility and feasibility of non-dominant armanchored UI, we conducted an empirical study. The primary goal
was to evaluate user performance during pointing tasks at various
locations around the arm. The secondary goal was to understandpreferred target locations and qualitatively assess user arm movement strategies for different target awareness schemes (i.e., known
or unknown target location). Thus, this study seeks to provide acomplementary perspective to existing studies that have exploredon-skin interfaces or compared the acquisition of targets on different body parts [6, 14, 59, 60, 63].

3.1
A variety of empirical studies have evaluated user performance
while acquiring targets in 3D environments. Users are able to store
and recall a large number of applications on their hand and forearm
if they use landmarks (e.g., fingers, scars, and tattoos) [6]. Dezfuli
et al. evaluated the effectiveness of eyes-free targeting of nine landmarks on the user’s palm and found that the center of the palmachieved the highest accuracy, whereas the pinky finger achieved
the lowest accuracy [14]. Weigel et al. investigated six input locations on the upper limb and found that the forearm was most
preferred for perceived ease and comfort, while the elbow and upper arm were least preferred [63]. Lin et al. found that users wereable to precisely tap up to 6 distinct points between their wrists
and elbows, and that haptic feedback could help to discriminate
where the forearm was touched during eyes-free interaction [29].
Vechev et al. compared tapping speeds on six body parts and foundthat while cycling, the wrist was the fastest region, however, whilerunning, the wrist was the slowest region because users may experience loss of balance while trying to tap on their wrist [59].
Wagner et al. defined 18 on-body target locations and found thatupper body parts achieved higher efficiency compared to lower
body parts, however, targets on the non-dominant arm were notincluded in the study [60]. Experiments by Lediaeva and LaViolafound that spatial, hand, and waist menus were faster to choose
from than arm menus when using ray-based pointing [28]. To ourknowledge, our work is the first to systematically investigate direct
selection of 3D targets positioned around a user’s arm.
Furthermore, existing work exploring the 3D space around thearm also inspired our study. Xu et al. proposed the Hand Range
Interface, which used wrist extension and flexion motions, illuminating the potential for body-centric interactions in VR [64]. Azaiet al. implemented the Open Palm Menu, where virtual menu items
were displayed around the palm of the non-dominant hand [5].
Dachselt and Hübner proposed a collection of classification criteriafor 3D menus [13]. As a result of this work, opportunities exist
to systematically investigate user’s performance and preferences

CHI ’21, May 8–13, 2021, Yokohama, Japan

Participants

Twelve participants were recruited to participate in the study (8females and 4 males; Mean=26 years old, range 19-52 years old).
All participants were right-handed. Eight participants had used a
VR headset less than once a month, whereas the remaining four
had never used a VR headset before. The study took an average of
60 minutes to complete and participants were provided with a $20
honorarium as compensation for their time.

3.2

Apparatus

The study system consisted of a Windows 10 desktop computer, anOculus Rift VR headset, a Vicon server, and 16 Vicon Vantage motion capture cameras that were mounted on the ceiling of the room
where the study took place. The VR application was implemented
in Unity 2018.2 and ran on a desktop computer.
To mimic free-hand interaction, a Vicon motion tracking system
was used for hand and arm tracking. The user’s left arm was tracked
using an armband consisting of two 3D printed pads with retroreflective markers affixed to the top and bottom (Fig. 2a). The rightarm was tracked using a similar pad placed on the dorsal side of thehand and another on the index finger. Position and orientation of
the markers was streamed to a Unity application using UDP packets
over a wired connection. Inverse kinematics were used to computethe parameters of the right index finger joints, based on the position and orientation of the right hand and index finger. The Unity
application displayed virtual arms and hands to the participantsthat replicated their movements in the real world [41, 50].
All participants were right-handed, so targets were displayedaround their left arm and they used their right index finger toselect a target. A click to confirm selection was registered using
the button of an Oculus remote [42] held in their right hand. All
participants were able to freely press the button with their thumbwhile pointing with their index finger. Data recorded during trials
included the position and rotation of the participant’s left arm, righthand, and right index finger (25 fps), along with event-triggered
logs whenever the button was pressed.

CHI ’21, May 8–13, 2021, Yokohama, JapanLi et al.

Figure 2: (a) 3D printed pads with retroreflective markers were used to track the user’s (1) left forearm, (2) right hand, and(3) right index finger. (4) A hand-held Oculus remote was used to register a click. (b) The trial started when the participant
performed the ‘start posture’ and clicked the start button. (c, d) The trial ended when the participant clicked the target sphereanchored to her left arm.

Figure 3: (a) An illustration of the ‘conical frustum’ grids and depictions of how the (b) longitude, (c) latitude, and (d) heighttarget locations were situated around and along the arm. (e) An illustration of the mapping of the eight longitude levels to thetracking results of the left arm, when the arm was held forward with the 3D printed pads on the (i) dorsal side and (ii) ventral
side parallel to each other. The longitude level 0 (N) has an angle of 22.5 degrees perpendicular to the two pads.

3.3

Study Design

During the study, participants performed a series of pointing tasks
in VR. A start button was displayed on the right side of the participant’s body and could only be pressed when the participant wasin a “start posture”, i.e., the participant was standing on a virtual
footprint on the ground and held her left arm at her side, inside avirtual bounding box (Fig. 2b). After the start button was pressed,
the timer started, and a colored sphere appeared around her left
arm as the target. The participant was instructed to touch the target
using her right index fingertip and press the Oculus remote button
as quickly and accurately as possible (Fig. 2c, d). The timer stopped
when the button was pressed. The target color changed as a visualcue when the fingertip entered the target and when the target was
selected, and auditory feedback indicated whether the trial was
successful. If the participant missed the target (i.e., pressing the
remote without touching the target with the index fingertip), thetrial was added to the end of the queue for the participant to repeat
later. Regardless of the result, the participant could then press thestart button to continue the study.
All possible target locations were distributed on virtual conicalfrustum grids centered along the participant’s left arm, generatinga ‘multi-layer interaction space’ ([36, 55]; Fig. 3a). Eight longitudelevels were evenly distributed around the arm in a counterclockwise rotation. Cardinal and ordinal directions are used to annotate
longitude levels, e.g., N for longitude 0 and NW for longitude 1.
The longitude levels were positioned based on the tracking resultsof the 3D printed pads (Fig. 3e). Three latitude levels were placedon the arm moving from the Wrist (latitude 0) up to the Forearm(latitude 1) and Elbow (latitude 2), with an 8 cm gap between levels.
Three height levels radiated away from the skin, with distances of
Close (height 0, 4cm), Medium (height 1, 12cm), and Far (height
2, 20cm). The Close level was chosen to be close enough to theskin while avoiding collision with our arm-anchored tracking pads.
The gap of 8cm between height levels was consistent with the gap
between latitude levels, so targets were ‘evenly’ distributed. Given
the differences in arm radii between participants, the base and top
radius of each conical frustum was calibrated per participant.
Two target awareness schemes were used throughout the study,i.e., unknown or known. In the unknown scheme, the target location
was not known before the start button was pressed. The participant
would need to first identify the target before performing her selection. This scheme was used to simulate users who are not familiar
with the layout of a UI. In the known scheme, the target location
was visible prior to the trial start, and the participant was asked tolocate the target before she pressed the start button. This scheme
simulated an experienced user who knows the position of each UI
element. By manipulating the target awareness scheme, it would
become clear if target selection difficulty arose due to the time it
took participants to find the target (i.e., search time) or the timeit took them to move to the location where the target was located(i.e., acquisition time).
A pilot study was conducted to determine the size of the targets.
In the pilot study (N=4), three sizes of target diameter were evaluated: small (1.0 cm), medium (2.2 cm), and large (4.5 cm). Smaller

Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality

targets took longer to find and had higher error rates than larger
targets, but without interaction effects between the target size,
awareness scheme, or target location. As a result, only medium size
targets were used in the study. When rendered in the virtual scene,these targets had a diameter similar to the size of ‘short-look icons’on an Apple Watch [1].
The experiment was organized into 10 groups of target selection
trials. The first group contained 18 training trials to help participants practice the first target awareness scheme. The next fourgroups were all performed using the same target awareness scheme(i.e., group 2-5), with 72 trials per group. Following this, participants underwent another training group of 18 trials for the second
target awareness scheme (i.e., group 6). They then underwent four
additional groups of trials using the same target awareness scheme(i.e., group 7-10), with 72 trials per group. The target awareness
scheme presentation order was counterbalanced across participants
and the data from the training groups (i.e., first and sixth groups)
were excluded from the analysis. Within each non-training groupof trials, all 72 target locations (i.e., 8 longitude levels × 3 latitudelevels × 3 height levels) appeared exactly once, and were randomly
divided into four blocks of trials, with 18 trials per block. After theconclusion of each block of trials, participants were presented with
their cumulative error rate. Participants were able to take breaks
between blocks and groups of trials. In total, 576 valid trials (i.e., 18
trials per block × 4 blocks × 8 non-training groups) were collected
per participant.

3.4

Study Procedure

A questionnaire was used to collect demographic information andprior VR experience. After the questionnaire, the participant donned
the Vicon marker pads and Oculus headset. The participant thenunderwent a calibration procedure to ensure that the virtual arms
she would see aligned with her actual limbs. The participant first
held the Oculus controller and pressed a button (tracked by Oculus) with her right index fingertip (tracked by Vicon). This action
recorded one data point pair between the two coordinates. Participants were then asked to repeat this process 12 times at various
locations throughout the environment. The final calibration matrix
𝑀, i.e., a 4 × 4 matrix composed by a translation matrix 𝑇 and arotation matrix 𝑅, was calculated using a least-squares solution [3].
A tailor’s tape measure was used to obtain the circumference ofeach participant’s left wrist and elbow with their sleeves rolled up,
so that the scale of the models would align to participants’ actual
limb measurements. The base and top radius of the conical frustum
grids were then adjusted accordingly. All target locations werevisible before the study, so participants could practice touch-andselect actions with their index fingertip and the Oculus remote.
The study was video-recorded and one of the researchers recorded
field observations during the study. Semi-structured interviews
were conducted with the participants after the study to probe theirpreferences and their self-assessment of the difficulties they had
while interacting with various locations on their arm. They werealso asked about the strategies they used to perform the pointing
task and their reasons for using different arm postures for different
targets (if applicable), based on the researcher’s observations.

3.5

CHI ’21, May 8–13, 2021, Yokohama, Japan

Metrics

During the study, a trial where the target was not selected would be
added to the end of the block queue, until it was correctly completed.
As a result, in each block, more than one data point may have beencollected for a target if at least one incorrect target selection was
made. For each target, the total errors made and the movement time(𝑀𝑇 ) for the last successful selection were computed. The error rate
was calculated as the total errors made averaged over repetitions.
MT alone would not account for the varying distances acrosstrials. Though the distance from the left forearm centroid to theright index fingertip was consistent due to the mandatory ‘start
posture’ (Fig. 2b), the distance from each target to the fingertip
varied due to the scale of the conical frustum grids, and MT varied
based on participants’ individual selection strategies. We consideredusing Speed as a measure, but this would not factor in the size ofthe target. To overcome the complexity of these variations, we
adopt throughput (𝑇 𝑃) as an ‘index of performance’ [15, 30, 31, 40,
45, 57] based on the actual starting distance (𝐷), size of the target(𝑊 ), and movement time (𝑀𝑇 ) in each trial. While this choicelimits the granularity of our results, TP offers a consistent andcomparable measure of the relative performance across targets,trials, and participants. Although our target width is fixed acrosstrials, we felt using TP would enable the broader reproducibility of
our results.


𝐼𝐷
𝐷
𝑇𝑃 =
, where 𝐼𝐷 = 𝑙𝑜𝑔2
+1
𝑀𝑇
𝑊

(1)

To complement the quantitative measures, a total of 12.2 hours ofvideos were also recorded. Post-study interviews were transcribed
and linked to screen-captures from the videos. Themes were codedby two researchers using affinity diagramming [52], focusing on
users’ subjective preferences for different target locations and the
movement strategies they used to acquire targets.

4 RESULTS
4.1 Quantitative Results
4.1.1 Fitts’ Law Fitting of Bimanual Pointing Tasks to 3D ArmAnchored Targets. We investigated whether Fitts’ Law [16] holds
for bimanual pointing tasks to 3D arm-anchored targets:
𝑀𝑇 = 𝑎 + 𝑏 · 𝐼𝐷

(2)

In the known scheme, where expert behavior was simulated,participants knew the location of targets before they started to move.
As a result, the total time that was recorded fulfills the definition of
MT in the Fitts’ Law model. Because ID was continuous, it needed tobe partitioned into q-quantiles [11]. Averaged over 10-quantiles (i.e.,
345 data points in each quantile), the model was 𝑀𝑇 = 1.0+0.18 ·𝐼 𝐷
(𝑟 2 = .18). In general, Fitts’ Law does not hold for this bimanual
3D pointing task, however individual differences existed when themodel was evaluated using data from individual participants. By
averaging over 10-quantiles, P2’s model was 𝑀𝑇 = −3.2 + 0.96 · 𝐼 𝐷(𝑟 2 = .90) and P7’s model was 𝑀𝑇 = 1.6 + 0.097 · 𝐼𝐷 (𝑟 2 = .024).
This distinction among participants could be explained by differing
individual strategies that they used to find targets, which include
moving the arms with or without rotation, and moving one arm

CHI ’21, May 8–13, 2021, Yokohama, JapanLi et al.

Figure 4: Mean throughput (TP) data segmented by (a) Awareness Scheme × Latitude and (b) Awareness Scheme × Height. Error
bars show 95% CIs. Gray lines indicate selected significant pairwise comparisons within the same awareness scheme (𝑝 < .0033).
Other comparisons across different awareness schemes are excluded for brevity.
only or moving both arms simultaneously. As a result, MT varied
for targets at similar distances, but different rotation angles.
In addition, since all trials were started in the same posture(Fig. 2b), the variation of ID was only affected by the scale of theconical frustum and the variations allowed by the size of the bounding box (𝑟𝑎𝑛𝑔𝑒 = 4.44 − 5.94, 𝜇 = 5.30, 𝜎 = .23). We believe that
introducing diverse distances and target sizes in future work will increase the variation of ID and further validate the Fitts’ Law fittingin such tasks.
4.1.2 Throughput Results. A repeated measures ANOVA was performed using the TP data on the within-subject factors, i.e., Awareness Scheme (2) × Longitude (8) × Latitude (3) × Height (3). Mauchly’s
tests indicated the assumption of sphericity had been violated for
2 = 57.20, 𝑝 = .001) and Awareness Scheme × LatiLongitude (𝜒27tude (𝜒 22 = 17.33, 𝑝 < .001), and were corrected using GreenhouseGeisser (G-G) estimates. Bonferroni corrections were adopted toadjust the significance level 𝛼. Mean TP results are presented foreach factor (Fig. 6).
Awareness Scheme × Latitude. A two-way interaction was foundbetween Awareness Scheme × Latitude (𝐹 1.10,12.07 = 13.02, 𝑝 =.003, 𝜂𝑝2 = .54). Paired t-tests (𝛼 = .0033) revealed that the latitude
location of the target influenced participants’ TP (Fig. 4a). When thetarget location was known, TP was significantly higher when thetarget was located at the Wrist (𝜇 = 3.047, 𝜎 = .625) compared to
the Elbow (𝜇 = 2.822, 𝜎 = .628; 𝑡 11 = 6.48, 𝑝 < .0001) and when thetarget was located at the Forearm (𝜇 = 3.020, 𝜎 = .624) comparedto the Elbow (𝑡 11 = 6.82, 𝑝 < .0001). Differences of TP comparing
targets at the Wrist and Forearm yielded no significance under
this condition (𝑡 11 = 1.291, 𝑝 = .2232). When the target location
was unknown, TP was significantly higher when the target was
located at the Wrist (𝜇 = 2.697, 𝜎 = .433) compared to the Forearm(𝜇 = 2.584, 𝜎 = .424; 𝑡 11 = 5.90, 𝑝 = .0001) and when the target
was located at the Forearm compared to the Elbow (𝜇 = 2.265, 𝜎 =.388; 𝑡 11 = 8.67, 𝑝 < .0001). Targets at the Wrist also had higher
TP compared to the Elbow (𝑡 11 = 9.35, 𝑝 < .0001). As for the
impact of awareness scheme, higher TP was achieved when thetarget location is known compared to unknown, and targets at theForearm (𝑡 11 = 3.74, 𝑝 = .0033) and Elbow (𝑡 11 = 5.00, 𝑝 = .0004)
revealed significant differences. Targets at the Wrist also had higher

TP in the known awareness scheme, though the difference was not
significant (𝑡 11 = 2.81, 𝑝 = .0170). In summary, TP was lower as
the target was located farther from the Wrist, regardless of theawareness scheme, and the influence of various latitude locations
was more prominent when the target location was unknown.
Awareness Scheme × Height. A two-way interaction was also
found between Awareness Scheme × Height (𝐹 2,22 = 6.09, 𝑝 =.008, 𝜂𝑝2 = .36). Paired t-tests (𝛼 = .0033) revealed that TP decreased
as targets moved farther away from the arm for both known andunknown schemes (Fig. 4b). In the known scheme, TP decreased
significantly when targets moved from the Close (𝜇 = 3.059, 𝜎 =.615) to Far distance (𝜇 = 2.815, 𝜎 = .635; 𝑡 11 = 10.28, 𝑝 < .0001),
and from the Medium (𝜇 = 3.014, 𝜎 = .625) to Far distance (𝑡 11 =8.06, 𝑝 < .0001). Targets at the Close distance also had higher TP
compared to the Medium distance, but the differences were notsignificant (𝑡 11 = 2.60, 𝑝 = .0246). When the target location was
unknown, TP decreased significantly as the targets moved from
Close (𝜇 = 2.660, 𝜎 = .438) to Medium (𝜇 = 2.565, 𝜎 = .418) to Far(𝜇 = 2.321, 𝜎 = .376; i.e., Close vs. Medium: 𝑡 11 = 5.47, 𝑝 = .0002,
Medium vs. Far: 𝑡 11 = 11.31, 𝑝 < .0001, and Close vs. Far: 𝑡 11 =13.08, 𝑝 < .0001). In addition, TP increased when participants knew
the target location, particularly for targets at the Medium (𝑡 11 =3.95, 𝑝 = .0023) and Far distances (𝑡 11 = 4.07, 𝑝 = .0019). Differences
between the two awareness schemes were not significant whentargets were at the Close distance (𝑡 11 = 3.52, 𝑝 = .0048). Similar to
Awareness Scheme × Latitude, differences due to height were more
pronounced when the target location was unknown. In general, TP
was lower the farther the target was from the arm’s surface.
Longitude × Latitude. A two-way interaction was found between
Longitude × Latitude (𝐹 14,154 = 6.66, 𝑝 < .001, 𝜂𝑝2 = .38; Fig. 5).
Paired t-tests (𝛼 = .00018) revealed that TP was higher when thetarget was located around the Wrist or Forearm compared to the
Elbow for most longitude levels: in particular, significant differenceswere found between the Wrist and Elbow when the target was in
the W (𝑡 11 = 7.22, 𝑝 < .0001), SW (𝑡 11 = 10.13, 𝑝 < .0001),
S (𝑡 11 = 8.40, 𝑝 < .0001), and SE (𝑡 11 = 7.50, 𝑝 < .0001) areas.
Significant differences were also found between the Forearm andElbow when the target was in the W (𝑡 11 = 7.38, 𝑝 < .0001),
SW (𝑡 11 = 14.18, 𝑝 < .0001), and S (𝑡 11 = 10.67, 𝑝 < .0001)
areas. No significant results were found while comparing the Wrist

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CHI ’21, May 8–13, 2021, Yokohama, Japan

Figure 5: Mean throughput (TP) data segmented by Longitude × Latitude. Error bars show 95% CIs. Gray lines indicate selectedsignificant pairwise comparisons within the same longitude level (𝑝 < .00018). Other comparisons are excluded for brevity.

Figure 6: Mean throughput (TP) data segmented by the (a) Awareness Scheme, (b) Longitude, (c) Latitude, and (d) Height. Graylines indicate significant pairwise comparisons (𝛼 = .05, .0018, .017, and .017, respectively). Error bars show 95% CIs.
and Forearm for targets in the same longitude area. In summary,
TP decreased when the target moved from the Wrist to Forearm
to Elbow, and the differences between latitude levels were more
prominent in the W , SW , S , and SE areas.
Paired t-tests also revealed different orders of longitude levels
for each latitude level, though the differences were not significant
after Bonferroni corrections. For targets located in the Wrist area,
highest TP was achieved in the SE area (𝜇 = 2.965, 𝜎 = .499), andlowest TP was achieved in the NW area (𝜇 = 2.677, 𝜎 = .482).
For the Forearm, targets in the NE area had the highest TP (𝜇 =2.964, 𝜎 = .460), while targets in the W area had the lowest TP(𝜇 = 2.649, 𝜎 = .520). Finally, for the Elbow, targets in the NE area
had the highest TP (𝜇 = 2.752, 𝜎 = .451), while targets in the SW
area had the lowest TP (𝜇 = 2.373, 𝜎 = .449). In summary, targets
placed on the medial side of the arm (the side toward the midline of
the body, e.g., SE and NE areas) had higher TP, while targets
placed on the lateral side (the side away from the midline of thebody, e.g., NW , W , and SW areas) had lower TP.
Main Effects and Additional Interactions. A main effect of Awareness Scheme was found (𝐹 1,11 = 15.10, 𝑝 = .003, 𝜂𝑝2 = .58), with

TP being significantly higher when the target location was known(𝜇 = 2.963, 𝜎 = .623) and lower when unknown (𝜇 = 2.515, 𝜎 = .409;
see Fig. 6a). This difference meets our expectation, and is attributable to extra search time in the unknown scheme, which negatively
impacted their TP.
A main effect was found for Longitude (𝐹 3.11,34.23 = 9.75, 𝑝 <.001, 𝜂𝑝2 = .47; Fig. 6b). Paired t-tests (𝛼 = .0018; see Appendix A:
Table 1) suggested that targets located in the NE area enabled
participants to exhibit significantly higher TP than targets in the
NW , W , and SW areas. Targets in the E area also supported
significantly higher TP than targets in the NW , W , SW , andS areas. Those targets located in the N area enabled significantly higher TP than targets located in the NW area. None ofthe other pairwise comparisons revealed any significant differences.
These results suggest that targets located on the medial side of thearm (N , NE , E , SE ) are easier for users to interact withthan those on the lateral side of the arm (NW , W , SW , S ),perhaps because they are not occluded by the arm and they do not
require arm orientation adjustments.

CHI ’21, May 8–13, 2021, Yokohama, Japan

A main effect of Latitude was found (𝐹 2,22 = 93.86, 𝑝 < .001, 𝜂𝑝2 =.90; Fig. 6c). Paired t-tests (𝛼 = .017) revealed that TP decreased astargets moved from the Wrist (𝜇 = 2.872, 𝜎 = .493) to Forearm (𝜇 =2.802, 𝜎 = .494; 𝑡 11 = 3.76, 𝑝 = .003) and from the Forearm to Elbow(𝜇 = 2.543, 𝜎 = .485; 𝑡 11 = 10.62, 𝑝 < .001). Significant difference
was also found between the Wrist and Elbow (𝑡 11 = 10.53, 𝑝 < .001).
These results suggest that targets closer to the elbow or upper arm
are harder to interact with, perhaps due to ergonomics impacting
seeing and touching these targets.
A main effect was found for Height (𝐹 2,22 = 156.05, 𝑝 < .001, 𝜂𝑝2 =.93; Fig. 6d), with TP decreasing significantly as targets moved
from the Close (𝜇 = 2.859, 𝜎 = .497) to Medium (𝜇 = 2.790, 𝜎 =.493; 𝑡 11 = 6.28, 𝑝 < .001) distance and from the Medium to Far(𝜇 = 2.568, 𝜎 = .477; 𝑡 11 = 12.53, 𝑝 < .001) distance. Significant
difference was also found between the Close and Far distances(𝑡 11 = 13.67, 𝑝 < .001). These results reveal that targets are moredifficult to interact with as they move farther away from the arm
surface, which may indicate a boundary of the peripersonal spacearound our arms.
In addition, one 2-way interaction between Longitude × Height(𝐹 14,154 = 3.74, 𝑝 < .001, 𝜂𝑝2 = .25) and one 3-way interaction
between Awareness Scheme × Latitude × Height (𝐹 4,44 = 2.85, 𝑝 =.035, 𝜂𝑝2 = .21) were also found. Due to their small effect sizes,
and thus decreased importance, a further examination of thesefactors was not conducted. No other interactions were found to besignificant (𝑝 > .05).
4.1.3 Error Rate. The overall error rate of all trials was 5.0%. A
repeated measures ANOVA was performed using the error rate dataon the within-subject factors, i.e., Awareness Scheme (2) × Longitude (8) × Latitude (3) × Height (3). No significant main effects orinteractions were found (all 𝑝 > .05), except for a 3-way Awareness
Scheme × Longitude × Latitude interaction that had a small effectsize (𝐹 14,154 = 1.99, 𝑝 = .022, 𝜂𝑝2 = .15). All target sizes were held
constant, which was a possible cause of the result that no significant
differences were found for different target locations. Increasing thetask difficulty (i.e., decreasing the target size) in future studies may
influence the pointing task accuracy around the arm.
4.1.4 Summary. A TP heatmap is presented for each 2D grid ofLongitude × Latitude, as if the 3D conical frustum was unwrappedat each Height level (Fig. 7). It qualitatively visualizes the diverse distribution of TP results of various locations and awareness schemes.
In summary, we found that:(1) TP was higher when participants knew where the target was(awareness scheme: known) and lower when they did notknow where the target was (unknown)(2) Targets on the medial side of the arm (i.e., N , NE , E ,SE ) had higher TP than those on the lateral side of the arm(i.e., NW , W , SW , S )(3) TP decreased as targets moved up the arm: Wrist > Forearm> Elbow(4) TP decreased as targets moved away from the skin: Close >Medium > Far
Among the entire TP heatmap, targets with the highest TP werelocated at S /SE /E /NE × Wrist/Forearm × Close/Medium
areas, which should be the first choices for deploying UI elements

Li et al.

around the arm. On the contrary, targets located at the Elbow orin the Far region often exhibited lower TP, so these regions should
be avoided while placing UI elements. Some exceptions to theseobservations do, however, exist: e.g., targets in the E /NE ×Elbow × Far areas had higher TP compared to the W × Forearm× Medium area when the target location was known. This suggeststhat as a user becomes more familiar with a UI layout, she may beable to use more regions. Developers and designers can use theseresults to position UI controls based on expectations of interaction
frequency and user expertise.

4.2

Qualitative Results

The participants’ subjective assessments of their performance invarious task conditions provided a valuable lens on the quantitative
results. Targets around the wrist or closer to the arm were foundto be easier, and the interactions that appeared to exist between
different target longitude and latitude locations were also supportedby the interview results.
4.2.1 Subjective Assessment. Participants used their own words
to describe the easiest and hardest regions to select targets. Tenof the twelve participants agreed that targets closer to the wrist
were easier to select. Many of their explanations were coupled withlongitude descriptions, i.e., “(the easiest region is) around the top
or right side of my wrist, where I can bring them to me” (P5) and“on top of my wrist because you just raise your hand and you see thetargets” (P12). Their preferences for targets around the wrist was not
surprising, and aligned with the quantitative results demonstratingthat the Wrist had the highest TP (Fig. 6c).
However, it was interesting that participants had diverse preferences for the longitude levels. Other than the aforementioned
quotes from P5 and P12, P1 preferred targets on the ‘right’ side (medial side, e.g., E /NE ) “because you don’t need to rotate the arm”,
P8 found “bottom side of my wrist is easier because you naturally
raise your arm”, and P6 favored “top or left or right side”. Referringto the TP of longitude and latitude results (Fig. 5), targets locatedaround the Wrist had the highest TP regardless of the longitudelevel, whereas targets in the S /SE /E /NE areas (i.e., ‘bottom/right side’) had relatively higher TP. P10 also commented that“the easiest (target) is around the wrist ... doesn’t matter what otheraxis is”. As for the other latitudes (i.e., the Forearm or Elbow), theE /NE areas had the highest TP, followed by N /SE areas,echoing the participants’ comments that targets on the right side
are easier to select.
As for height, two participants felt that targets at lower heights
were easier to select, as P6 commented, “when it is closer to the skin I
don’t need to judge the distance or height, I just need to move closer tothe skin.” Other participants did not discuss height. This aligns with
the quantitative results, where targets closer to the arm exhibited
higher TP.
When asked about the most difficult task conditions, eight oftwelve participants mentioned that targets near the elbow were
hard to approach. P7 considered the region near the elbow to be an“awkward position” and P2 commented that “when it gets too close
to me, especially when it is here close to the elbow and high enough... it is very close to my head and that is hard.” It is interesting that
another four participants also used the term “close to my head”,

Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality

CHI ’21, May 8–13, 2021, Yokohama, Japan

Figure 7: Throughput (TP) heatmap for targets at varying locations, for both unknown (1st row) and known (2nd row) awareness schemes. The x-axis is reordered to [2, 3, . . . 7, 0, 1] to better illustrate the clustered areas of higher and lower TP.

Figure 8: Photos from the user study, demonstrating how
P4, P5, and P12 were trying to center the target in front of
their body for convenience and better visibility by (a) rotating their arm, (b) lifting their arm, and (c) bending their arm.

or descriptors that referenced body parts in the head, to describe
these difficult conditions, including “close to my eyes” (P4), “close tome” (P6), and “under your eyes” (P7). In such scenarios, participants
might lose track of the target when they moved their arms or thetarget might be too close to their headset, due to the limits of thenearest rendering plane. As a result, targets in the NE /N /NW× Elbow × Far areas, i.e., “close to head” region, had lower TP,
especially in the unknown scheme where the participants needed
to search for the target during the trial (Fig. 7).
Four participants also found that the targets on the ‘left’ side(lateral side, e.g., W /SW ) were hard to reach, especially when
the targets were far away from the arm. Participants sometimes
could not see targets in these regions easily (P5/P11), or they had
trouble approaching them, as P9 stated, “it is difficult when it’s outside of the arm and I had to turn my arm ... it’s in very uncomfortable
position.” This could be explained by our TP heatmap: when targets
were located in the Close × Forearm areas, SW area had a higher
TP than the NW area, however, this contradicts the results thatwere found in the Medium/Far × Forearm areas (Fig. 7).
4.2.2 Strategies. Participants were also observed using different
movement strategies in the known and unknown schemes. Whileperforming the target selection task in the known scheme, four participants mentioned that they would raise their arm if the target wasunder their wrist (i.e., S /SE × Wrist areas). Three participants

said they would twist their arm first if the target was under theirelbow (i.e., S /SE × Elbow areas). While approaching targets on
the lateral side (W /SW ), P10 adopted a more unique strategy,“I tried to limit the movement (of my left arm) ... I would actually
use my left arm to stabilize my right arm to make sure the depth
was appropriate.” While many participants were trying to speed up
their movements by moving both arms in parallel, P10’s strategy
enabled them to focus on the accuracy and stability of their target
selection instead of their speed.
Further, participants also developed other, more location-genericstrategies to help them with their selections. For example, six participants tried to center the target in their field of view (FOV) by
moving their left arm, i.e., “I was trying to bring the targets to thesame region, so I can move my right hand to the same region, sortof in front of me” (P5; see Fig. 8b). Some participants applied thisstrategy to overcome occlusion issues, such as “what I was doing is
rotating my arm to bring the data points to my visibility” (P4; Fig. 8a)and “it’s natural for me to rotate my arm to move the target and I
can conveniently see the target” (P11). Others adopted this strategy
to ease the ergonomics of their target selections, such as “if it isfurther than me I would like to turn my arm closer to me so it’s easier
to click” (P9), and “I try to bend my arm inside to bring the target easy
to tap” (P12; Fig. 8c). Thus, this single goal to bring the target into
the center of one’s FOV manifested itself via a variety of different
user behaviors.
When the target location was unknown, four participants looked
for the target by introducing visual motion cues within their FOV,
i.e., “lift my arm and twist at the same time ... it gave me a view of
everything” (P8). P6 further explained that “when I move my leftarm, I can see the blur of the blue ball moving, and I knew where toaccurately twist my arm.” Interestingly, many users often apply asimilar strategy when searching for their mouse cursor on desktop
computers, i.e., quickly moving the cursor back and forth to makethe cursor more distinct (“shake mouse pointer to locate” [2]).

5

ARMSTRONG: DESIGN GUIDELINES

The results of the user study demonstrated the potential that theheatmap and the interview results could have to assist in the optimization of UI layouts. We next discuss the design of arm-anchored

CHI ’21, May 8–13, 2021, Yokohama, JapanLi et al.

Figure 9: (a, b) Five UI controls are arranged using the heatmap to achieve the highest possible TP, for both unknown (a1–a5)
and known (b1–b5) schemes. (c) A slice of the known scheme dataset (e.g., latitude preference: Forearm), could also be used toachieve the highest possible TP (c1–c5). (d) The heatmap annotated with corresponding labels for the UI elements in panels a,b, and c.
UIs for unknown, diverse user populations, and suggest how thesefindings could be integrated within a plugin for real world use.

5.1

Optimize Aggregated Throughput

The heatmap results could be used to optimize overall TP whilearranging UI controls around the arm. For example, if a designer
wishes to deploy a collection of icons on a smart wristband, theheatmap results for the Wrist × Close dimensions revealed that
SW /S /SE /E /NE areas would be suitable choices (Fig. 7).
Furthermore, if more icons need to be added, the heatmap suggests
a number of possible axes along which to position icons while maintaining overall TP. Based on the goal of the design, the unknown
or known heatmap data could be used to find the best arrangement
plan (Fig. 9a, b). The designer could also make use of partial datafrom the heatmap. For example, if the designer wishes to have allicons along the Forearm, the corresponding row slice from theheatmap could be used to place the icons on the same surface along
the Forearm and achieve the highest aggregated TP (Fig. 9c, d).
Differences in TP between the Wrist and Forearm areas were
found to be smaller for the NW /N /NE /E areas (Fig. 5).
Therefore, it may be possible that some directional UI controls,
such as sliders, could be placed in these regions (e.g., from Wrist to
Forearm in the N area). As a result, smooth interaction could beachieved when using controls that make use of this axis. The interview results revealed that participants preferred the targets aroundthe wrist, which is not surprising given that users are already familiar with interacting with watches and wristbands. Interestingly, as
the latitude level increases, targets get closer to the user’s shoulder

and head, and these locations were considered to be “awkward”(P7) and were not preferred by the participants. Designers should
exercise caution when placing targets close to the elbow or upperarm or reserve these locations for controls where false activation isharmful.
The Close and Medium areas also had higher TP, suggestingthat combining on-skin locations with in-air locations could resultin increased TP. This result complements prior research that has
utilized the surface of the skin as an I/O device [22, 24]. The interviews indicated many participants preferred to center the target totheir field of view, which was easier when the target was closer totheir arm.
It is worth noting that locations on the skin do not always ensure increased TP, for example, targets in the SE /E /NE ×
Medium areas in the heatmap had higher TP than targets in the
NW /W × Close areas. This suggests that there may be a nontrivial distribution of “hot spots” around the arm that could be
utilized. As a result, designers should consider the 3D space aroundthe arm as a whole, rather than simply placing UI controls on theskin. In addition, because targets in the W /NW /N /NE /E
areas had similar TP at the Close and Medium heights (Fig. 7), linear UI containers like the ‘drop-down’ menus could be positioned
perpendicular to the arm in these regions (e.g., extending from theskin into the air in the N area).
Based on the results from our study, we summarize the following
Armstrong design guidelines:(1) When TP is of priority, the UI controls should be placed on
the medial side of the arm (i.e., N , NE , E , and SE ),Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality

CHI ’21, May 8–13, 2021, Yokohama, Japan

Figure 10: Screenshots of the Unity plugin for Armstrong guidelines. Possible layouts for UI controls are displayed to the user.
(a) When no ‘surface preference’ is designated by default (red box), the layout is optimized to achieve the highest overall TP(green box). (b) When a preferred surface is selected, e.g., “same longitude” (red box), the distributions of targets with highest
overall TP are limited to that surface. More than one preference can be selected at a time.
around the wrist, and close to the skin. Alternately, if UI
controls are expected to be used infrequently or avoiding
accidental activation is critical, they could be deliberately
placed in the ‘low-TP’ areas, such as the lateral side of thearm (i.e., S , SW , W , and NW ), around the elbow,
and far away from the skin.
(2) When arranging a number of UI controls around the arm,
the TP heatmap could be referenced to optimize the layout.
Partial data from the heatmap could also be leveraged if
certain constraints exist, such as when arranging controls
around a wristband, a row slice of the heatmap dataset atWrist × Close could be used.
(3) For UI controls that span multiple cells of the heatmap (e.g.,a slider or a drop-down menu), TP results of individual
cells could help to optimize the arrangement of the subcomponents: the most commonly used sub-component shallbe placed at the position with the highest TP.
(4) UI controls close to the user’s shoulder or head are ‘awkward’
to interact with, as they are more likely to overlap with theuser’s head when the user is raising or bending her arm(e.g., N × Elbow × Far is a risky area). Designers should
be cautious when placing targets close to these areas.
(5) Users who are not familiar with the UI layout will need extra
time to find the target UI controls. Visualization techniques
could be used to create motion cues to help find UI controls,
especially when they are placed in the low-TP areas.

5.2

Unity Plugin for Armstrong Guidelines

To illustrate how these guidelines could be used in practice, a Unity
plugin was implemented. The TP heatmap generated from the study(Fig. 7) was used as input and potential locations of UI controls
were distributed around the user’s arm according to the conicalfrustum grids (Fig. 3). These locations were stored in a sorted queue
based on their TP. Designers can create UI controls in Unity anddrag them to this plugin. After loading the perimeters of the user’s
arm and setting surface preferences for the UI controls, the plugin
will automatically position and orient desired UI controls around avirtual arm model optimizing the highest aggregated TP (Fig. 10).
Designers can navigate through various layouts generated by theplugin and compare their TP values in real time. By default, thetargets are arranged freely in space to optimize aggregated TP whenno surface preference is designated (Fig. 10a). UI controls like sliders
could also be optimized for orientation based on the aggregated TP
results and their individual shape. If a designer prefers to have all
UI controls along the same axis, e.g., the same longitude level, thecorresponding surface preference checkbox could be selected toenable a customized layout (Fig. 10b). Then, all targets positioned
in the E area, for example, could optimize the aggregated TP and
the sliders would be re-orientated to follow these restrictions (i.e.,either along the latitude or height directions). More than one option,
including “same longitude”, “same latitude”, and “same height”, areavailable to meet various designer’s requirements.

6

DISCUSSION

The user study investigated user performance and preference fortargets along the longitude, latitude, and height axes. The results
revealed that participants tended to center targets in their FOV andthat targets closer to the skin, located around the wrist, or placedon the medial side of the arm could be selected more quickly thantargets in other locations. These results informed the Armstrong
guidelines, based on the TP heatmap, which are demonstrated in aplugin to help with the design process of arm-anchored UIs. Herein,
the implications and limitations of this work are discussed.

6.1

Design for Both Known and Unknown
Schemes

While placing UI controls that need to be universal across applications, such as the window-frame controls, users will graduallylearn the target locations as they use the system and thus prepare
their arm motions before approaching possible target locations. As
a result, the TP heatmap results and participants observations in
the known scheme could be leveraged for the placement, and thepre-selection information could be utilized within a recognition
algorithm to predict target locations or provide hover-like feedback
to the users about their selection before they make it.
When designing layouts for UI controls that are dynamic or
adaptable across different applications, users who are not familiar
with the interface will need to spend extra time searching for targets.

CHI ’21, May 8–13, 2021, Yokohama, Japan

Participants were observed quickly twisting their arms to generatevisual cues to help them with their search. Inspired by this behavior
and the “shake mouse pointer to locate” feature from Mac OS [2],such arm-anchored interfaces could enlarge UI controls if theydetect that a user is beginning to twist her arm or utilize othermotion-based cues to help find targets.

6.2

Granularity of Arm-Anchored Targeting

This study investigated the impact of the four variables, i.e., longitude, latitude, height, and awareness scheme, on pointing task
performance. The quantitative results compared different discretelevels; however, the number of levels was not large enough to enable smooth interpolation between the levels, especially for thelatitude and height variables. As the interviews demonstrated, selecting targets close to the head felt awkward and challenging, so
it appears that there may be an upper bound on the latitude (upperarm or shoulder) and height (up to the head) for arm-anchoredtarget selection. As we design for a larger space around the arm,
ergonomics becomes another important factor: the maximum rotation angle and the comforts of the arm shall be considered tomaintain the system’s usability. Future work could increase thenumber of levels of each variable to increase the possible areas ofinteraction and also reduce the gap between levels to ensure that
more fine-grained, ‘continuous’ data can be obtained and used tointerpolate between the various levels.

6.3

Targeting in VR versus AR

The user study was conducted in VR due to the limited FOV andtracking capabilities available for AR devices today. The study results and design guidelines could help designers to create armanchored interfaces in VR, while further investigation is required
to generalize the results to AR. Although the size of the VR arm
models was calibrated for each participant using their own wristand elbow measurements, the arm models inevitably looked different from their real arms, which may have caused them to behave
and move differently than they would have if they were looking attheir own actual arms. The surface texture details have been foundto have a high impact on the visual perception in a study analyzing
anatomical structures of the liver in VR [25]. On the other hand,arm-anchored UIs in AR may face unique challenges, for example, the user’s own clothes may clash with the overlaid content
or interfere with the rendered colors. As AR devices have been
improving with larger FOV and better body-tracking functionality,
future research should explore how these results apply to otherhardware and modalities such as AR.

6.4

Quantitative Design Techniques for
Various UI Controls

The Unity plugin for Armstrong guidelines adopted a simple objective function to maximize the sum of TP to achieve the best overall
performance. As more UI controls are involved, the disambiguation
between adjacent controls becomes another important factor, especially when future work explores an increased number of interface
layers. Involving a ‘density factor’ in the objective function might
be a promising direction [9]. The importance or usage frequency ofeach UI control could be another useful factor to adjust the weights

Li et al.

in the function. Zhai et al.’s work also inspired us to consider theequivalent of ‘digraph frequency’ in the context of 3D UI [66]. A
weighted combination of TP results and the aforementioned factors
could be involved in a multi-objective function to optimize the UI
layout [58].
This study investigated target selection performance using acolored sphere with status feedback that simulated a 3D button.
This design decision enabled for a comparison of the performance
between different locations without bias from the design of theactual UI control (e.g., size, shape, or function). The interaction
between the size and the location of a target is worth investigating
further because it appears that different target properties influence
selection. For example, targets in the Elbow × Far areas could beenlarged to achieve higher TP, enabling for a smooth and consistentuser experience over the entire space. In addition, future research
could investigate compound UI controls (e.g., a number of targets
in a drop-down list) and analyze how the individual grid cells of
the TP heatmap could help in such scenarios.

6.5

Limitations

The results of this study are limited by the sparse distribution ofpotential locations and the upper bounds of latitude and heightlevels. In addition, though simulated skin textures were applied
to participants’ virtual arms and size adjustments were made to
the virtual arm models, they inevitably looked different from theuser’s own arms. Further studies are thus necessary to validate
the feasibility of transferring these results to an AR system. We
demonstrated that the pointing strategies adopted by individual
participants were inconsistently modeled by Fitts’ Law. Future work
to develop a model that accounts for the contributions of positionaland rotational movements to MT will help to better understand the
dynamics of these bimanual interactions. This work also focusedmore on the impact of various target locations around the arm,
rather than on the implications of individual target properties (e.g.,
size or shape of the UI element). Opportunities exist to, for example,
evaluate how targets might be enlarged in low-TP locations tocompensate for the difficulty users have selecting them.

7

CONCLUSION

In summary, this work has explored the efficiency and accuracyof pointing tasks with targets situated around the non-dominantarm. The impact of the target’s longitude, latitude, and height levels
are investigated, contributing to the literature on proprioceptive
sensing of arm-anchored UIs. Our study results demonstrated thattargets that were closer to the skin, located around the wrist, or
placed on the medial side of the arm were selected more quickly
than those far away from the skin, located around the elbow, or
placed on the lateral side of the arm. Findings derived from participants’ subjective comments and strategies were analyzed tosupplement the quantitative results and found that participantstended to center the target in their field of view, and they would
also introduce motion cues to assist their finding process by rotating their arm. The high-level findings on individual factors, aswell as the ‘best spots’ revealed by the interaction effects of thesefactors were summarized into a 3D TP heatmap around the arm,

Armstrong: An Empirical Examination of Pointing at Non-Dominant Arm-Anchored UIs in Virtual Reality

and Armstrong design guidelines were proposed to be used by designers to position UI controls around the arm while optimizing
for aggregated TP. A Unity plugin was also presented to help designers and developers to automate this process and design betterarm-anchored UIs by enabling them to navigate through potential
layouts for UI controls.

ACKNOWLEDGMENTS
The authors would like thank Tovi Grossman and Michelle Annett for their valuable comments, and Dan Clarke for recording
voice-overs for our video. We also thank our participants for theirinvolvement in our research and their presentation in our video.

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A

TABLES

Table 1: Pairwise comparisons of throughput (TP) data between Longitude levels (𝛼 = .0018). For each comparison, theformer longitude level (bold) has a larger TP than the latterone. Non-significant pairs were excluded for brevity.
Pairwise Comparison

𝜇Δ

𝜎Δ

𝑝

NE
NE
NE
E
E
E
E
N

.213
.286
.269
.153
.191
.263
.246
.164

.175
.206
.137
.110
.132
.162
.170
.114

.0014
.0005
<.0001
.0005
.0004
.0002
.0004
.0004

vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.

SW
W
NW
S
SW
W
NW
NW