Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

ArmSwing: Using Arm Swings for Accessible and
Immersive Navigation in AR/VR Spaces
Yun Suen Pai
Keio Media Design
Keio University
yspai1412@gmail.com

Kai Kunze
Keio Media Design
Keio University
kai@kmd.keio.ac.jp

ABSTRACT

Navigating in a natural way in augmented reality (AR) andvirtual reality (VR) spaces is a large challenge. To this end,
we present ArmSwingVR, a locomotion solution for AR/VR
spaces that preserves immersion, while being low profile compared to current solutions, particularly walking-in-place (WIP)
methods. The user simply needs to swing their arms naturally
to navigate in the direction where the arms are swung, without
any feet or head movement. The benefits of ArmSwingVR arethat arm swinging feels natural for bipedal organisms second
only to leg movement, no additional peripherals or sensors are
required, it is less obtrusive to swing our arms as opposed to
WIP methods, and requires less energy allowing prolong uses
for AR/VR. A conducted user study found that our method
does not sacrifice immersion while also being more low profileand less energy consumption compared to WIP.
ACM Classification Keywords

H.5.m. Information Interfaces and Presentation (e.g. HCI):
Miscellaneous
Author Keywords

virtual reality; locomotion; navigation; arm swing; immersive.
INTRODUCTION

Navigating virtual spaces in VR often causes Virtual Reality(VR) sickness. It might be a critical barrier to use VR as effective rehabilitation and training tool. One attempt to overcome
VR sickness is to simulate locomotion [13]. Current research
implementation related to locomotion in VR such as WIP
methods aim to create a more realistic sensation of walking,thus negating motion sickness by avoiding contradiction withthe body’s sense of balance and spatial orientation [27]. However, as VR usage becomes more mainstream, WIP suffersfrom several issues, mainly 1) jogging in place looks strange toothers, and 2) it becomes tiring after a slightly extended periodof usage, unless it was designed for energy consumption likesports simulation in the first place. This creates a barrier for
some, such as elderly consumers or just the general public
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise,or republish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee. Request permissions from Permissions@acm.org.
MUM 2017, November 26-29, 2017, Stuttgart, Germany© 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-5378-6/17/11. . . $15.00
https://doi.org/10.1145/3152832.3152864

189

who wants to use VR more socially. Unlike foot or head-based
WIP which enables locomotion through foot motion or headbobbing, ArmSwingVR enables users to navigate a VR scene
simply through arm swing, allowing for a more socially acceptable interaction while preserving the realism of walking
that is natural to human gait, as well as consuming less energy.
The user simply needs to swing their arms in a natural movement as they do when walking, providing them the freedom
to look around without affecting the walking direction. No
feet movement is required, making any additional sensors or
3rd party peripherals unnecessary. Users also do not need todeliberately bob their head to allow a better immersion andfocus in the VR scene during locomotion.
This paper pursues 3 major goals: to develop a virtual space
locomotion solution that feels natural, preserves immersion
and is more socially acceptable, ensure that users can easily
utilize the system without additional devices, and evaluate
users’ feedback on the energy consumption, ease of use andimmersion factor of ArmSwingVR compared to existing WIP
methods such as VR-Step [26]. Other locomotion solutions in
VR like the omni-directional treadmill [6], brain interface [8],etc. has been developed towards the similar goal of immersion,
yet they involve hardware that are not easily accessible, too
costly for the average consumer, or simply adds to the numberof peripherals for VR systems that are already plentiful by
default, making these solutions viable only in research labs
or specific applications. This makes software approaches like
ArmSwingVR preferable. Furthermore, with the inclusion ofhand position tracking controllers for the current and future
generation of VR solutions (HTC Vive, Oculus Touch, Google
Daydream, etc.), users do not need 3rd party tracking devices.
Arm swing was never a necessity for human locomotion, however this motion feels natural for humans when walking or
running [1], and simulating this gesture in VR has the potential to induce the sense of navigation while maintaining
immersion.
We perform a user study to compare with another WIP solution, VR-Step [26], to evaluate its effectiveness, immersion,and energy consumption. We use the NASA Task Load to evaluate the physical demand, presence questionnaire to evaluate
immersion, and a score-based method to evaluate effectiveness.
As with most other locomotion studies, we also compared if
ArmSwingVR causes more motion sickness over VR-Stepusing the Simulation Sickness Questionnaire (SSQ). From
the results, we found that ArmSwingVR does not sacrifice on
immersion and can be used for an extended period of time,Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

where all participants agree that it is a viable solution to be
used publicly given its low profile nature.
To summarize, the contributions of this research are as thefollowing:• Develop a VR locomotion solution that feels natural, preserves immersion (no significant decrease in immersion
compared to VR-Step in terms of realism, possibility toact, quality of interface, possibility to examine and selfevaluation of performance), and is more socially acceptable.
• Ensures that users can easily utilize the system with any
current and future VR tools without 3rd party devices.
• Evaluate users’ feedback on energy consumption (52.9%
lesser in terms of rise in heart rate and 26.4% lower scorein NASA task load), ease of use and immersion factor compared to VR-Step through quantitative and qualitative analysis.
RELATED WORK

Navigating a virtual space highly depends on the kind of system being used. For example, mobile systems like the Google
Cardboard and Gear VR does not have any physical controls,
making these choices rather limiting when it comes to any
form of interaction in VR space, since the user essentially only
has gaze-based interaction. The Oculus Rift DK1 and DK2,
as well as Sonys PlaystationVR relies primarily on a gamepad
controller for both interaction and navigation, while the current generation of VR devices, namely HTC Vive and the new
Oculus Rift has controllers that are tracked in physical space.
With such variability in VR systems, various researches havebeen conducted to determine the best possible locomotion
method in VR space. One of the biggest advantage offered by
the HTC Vive is the room-scale VR experience, where the user
can physically walk around within a confined physical spacethus improving the sense of presence [24]. The Vive uses a
Chaperone bounding system for the user to see the physicalboundaries in virtual space, so the boundary does not need
to be integrated by developers [2]. This allowance for actual
walking is in line with a human’s psychological requirementthat physical movement is more important than a rich visual
scene when it comes to locomotion [22]. It was found thatboth transitional and rotational body movement helps for efficient navigation [23], though another research showed thatphysical rotation is sufficient for actual walking, implying thatimmersive locomotion can be achieved without the required
physical space [21].
Current Locomotion Methods in VR

More natural interactions where the user simply performs a
jogging action on a spot to navigate in VR methods have been
gaining popularity because spatial information is the same as
the real environment, therefore humans require the correct
motion to adapt to any change in the virtual world [19]. Oneof the more popular methods is called walking-in-place (WIP),
where the user simply performs a jogging action on a spot tonavigate in VR. VR-STEP was one of the newly developed
method aimed for mobile VR that leverages inertial sensors inthe smartphone to provide the user with a realistic method of

locomotion [26]. However, this system only caters for mobile
VR. Another WIP implementation is by using a Wii balanceboard [32]. Since the board has pressure sensors, it was relatively straight forward to use it as a locomotion device forvirtual environments. This proved reliable and that the Wii
board can be easily obtained, though users still need to rely on
this additional peripheral to couple with the already arguably
cumbersome head mounted display (HMD) and controllers
for a VR setup. The same can be said about another workthat uses the Microsoft Kinect to detect walking [34]. The
depth sensors in Kinect allows accurate skeletal tracking of
the user by measuring the angle between the hip, knee andankles. Compared with the Wii board, gesture based recognition means that the user is at least not in physical contact withthe peripheral, preserving relatively more immersion. Other
approaches for WIP are by attaching calibrated sensors on thelegs and calf [36], however, WIP methods tend to be moretiring and a continuous jogging motion may cause perspirationand to a more serious degree, nausea. This is the reason why
VR applications rarely use head bob, which is a way to showthat the virtual character is moving by bobbing the camera[28].
Arguably, the best method for locomotion in VR is by using
an Omni-directional treadmill (ODT) [6]. It was initially developed for the U.S. Army’s Dismounted Infantry Training
Program and it allows the user to realistically perform walking
motions, yet still remain at a single spot. The main disadvantage is that a custom treadmill like the ODT is surely too costly
for average consumers, if even accessible at all.
There are also more unconventional methods for VR locomotion, such as a flight based locomotion by manipulating
the sense of scale [18], rotating the virtual environment [20]or simply using head angle [25]. One of the more uniquemethods of navigation is by using brain-computer interface[8]. In this method, electrodes are attached to the user’s headto obtain electroencephalogram (EEG) signals that are used
as input values for the virtual environment. However, brain
interface tends to have inherent issues such as the presence ofnoisy data, as well as it not being accessible for the average
consumer.
With this current generation of VR systems, most developers
rely on on-rails sequences, controller-based, or teleportationbased navigation. On-rails simply mean that the user is not
given the freedom to walk around and is confined to a fixed railthat usually consistently moves which can be seen on games
like London Heist: The Getaway and Walking Dead. Blink
teleportation is a new method proposed by Cloudhead Games[3] for the Vive where the user simply points and teleports
to the designated spot. Lastly, controller navigation using a
gamepad, normally maps the left analog stick to locomotion
and the right stick to head movement. For these three methods
respectively, the user has no freedom of movements, teleportation is not realistic, and gamepad controls induces motion
sickness.
The closest to our work is research by Mc Cullough et al.
and Wilson et al. [14, 33]. Both use an arm swing method
similar to the one proposed. However, they require additional

190

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

hardware (the myo-arm band). The band has to be adjusted
in a specific angle (susceptible to shifts and not placementindependent). They are also using simply angle changes on
the upper arm or velocity to detect the walking speed, not the
trajectory of the walking direction. The user in AR/VR walks
in the direction of their head orientation not in the directionof the arm swing. Our approach in comparison works without
additional hardware, is sensor-shift/orientation robust and uses
arm swing trajectory for walking direction. The user can lookin any direction while walking.
Arm Swing in Human Locomotion

Using the arm swing movement is often only limited to bipedal
locomotion [1], and is a rather interesting proposal as studies
have shown that arm swing reduces the moment about thevertical axis of the foot while walking [17]. This means that a
relationship does exist between arm swing and foot reaction,
despite it not being necessary while walking. In fact, total energy consumption with arm swing is lower than without during
walking even though energy is consumed for arm movement,
therefore overall reducing the cost of walking [5]. Because of
these traits, researchers have utilized arm swing for walking
rehabilitation [12] and robotics [4].
METHODOLOGY

The mechanics of ArmSwingVR relies solely on the users’arm swing motion, akin to normal walking gait. No form of
additional pressure sensors is required for the feet movement
tracking, or that deliberate head bobbing is necessary. It is important to note that the facing direction and walking direction
should be different as well. This means that the user shouldbe able to continuously walk in the direction their body isfacing, yet still may freely look around. A solution for thiswill be further explained in the next few paragraphs. Since
ArmSwingVR is achieved using purely motion recognition,no buttons are actually required, and can be reserved for otherforms of interaction. This is shown in the user study, wherea simple task is assigned for the participants using the trigger buttons. ArmSwingVR was developed using the HTC
Vive because as of this moment, only the Vive comes withpositional tracking controllers. Therefore, only the Vive controllers and headset are necessary for the user to fully use
ArmSwingVR. The tracking space used for development is
1.6m x 3.1m, though for the user study, the tracking space will
be maximized (4.6m x 4.6m). The entire system was built
using the Unity development environment for seamless integration with the SteamVR plugin. C# was used as the primarycoding language. For a smooth VR experience, a desktop
Windows PC equipped with a Core i7-6700 processor and an
Nvidia GTX 980 graphic card was used which is above therecommended specifications.
−
→ →
−
− →
AB = B − A→−AÂ = −
→
|A|
−−→ −
→
ABz = AB.Âz
191

Gesture Recognition

Finite state machine was used to enable the system to recognize the position of both controllers relative to the positionof the HMD [25]. The relative positional vectors for the controllers and the HMD must first be determined so that thesystem knows which of the objects are in front of the otherregardless of the facing direction. We are only interested on
the forward vector component because the height and side vectors are not important for arm swing. For the relative positions
between controllers, relative vector AB is found by subtracting
vector A with B. The transform vector of A is then normalized and the dot product between AB and the normalized A
is used to find the forward vector component. This procedure
is repeated to find the relative position between the HMD andthe left controller (called vector AC), as well as between the
HMD and the right controller (called vector BC).
Six states were constructed for the motion detection, whichare Idle, RightFront, LeftFront, RightFront2, LeftFront2, andWalk. Figure 1 illustrates the algorithm that connect each ofthese states together through a series of decision making. The
Idle state is the default state of which the user is standing
or interacting with the virtual environment. In this state, novelocity is induced and the system checks the positions of
the controllers. If AB is positive, BC is positive and AC isnegative, this means that the right controller’s position is in
front of the HMD whereas the left controller is behind. Thischanges the state to RightFront. A similar decision process ismade for LeftFront. In this next tier of state with RightFront
as an example, we then check the duration t of which we arein the state. If t is within 1 second and the position remains
the same, the state reverts back to Idle.
Otherwise, if the position switches such that the left controller
is now front, LeftFront2 is then initialized. RightFront2 andLeftFront2 is a safety state layer that ensures that the user does
not accidentally walk, and will only do so on purpose. Similarly, the duration is checked again, and if the arm positions
switches once more, we finally enter the Walk state. The actual
walking motion is conducted in this state. Velocity is induced
based on the walking direction. The walking direction is not
the same as facing direction, which is common in most firstperson view camera applications. In real life, we may walk ina direction yet face another. To achieve this, the walking direction has to depend on the forward vectors of the controllers.
The resulting walking vector D is found by summing vector A
and B. Figure 2 depicts these vectors from a top-down view.
Linear interpolation is then used to smooth the change of vector D when the controllers are constantly swaying back andforth. Furthermore, only the rotation about y-axis of the vector
is important since we just wish to know which way it is facing
and not if the vector is facing downwards or tilting.
It is important to use Quaternion rotations for this because
a controller that faces downwards, which is common when
walking, will be subjected to gimbal lock if Euler rotations
were used. If the user wishes to stop walking, several conditions need to be met. One of the most important parameters
is finding the speed, s of the controller. If the speed of thecontrollers approach zero, it is highly likely that the user has

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

Figure 3. Placement of the trigger plane for determining the point.

stopped swinging their arms. However, the speed also approaches zero at the amplitude or maximum swing of the arms.
To solve this, we created two conditions: walking is halted
when the controllers’ velocity approaches zero and are close to
the HMD at the z-axis, or when the controllers speed remains
close to zero after a period of time. The first condition uses
the vector AC and BC computed previously to determine if theuser has stopped any motion and is standing still. The second
condition uses a Coroutine that delays the next checkpoint by
2 seconds. If the controllers are still relatively static after that
period of time, the state finally reverts back to Idle.

Figure 1. Finite state machine algorithm.

The user can control the walking speed depending on the
speed of arm swing. However, as mentioned earlier, the speed
approaches zero at the maximum or minimum swing point.
This causes a jerky movement as the velocity of the movement
constantly fluctuates between zero and the current arm swing
velocity. Therefore, we created a trigger plane placed on the
HMD as shown in Figure 3 and only take the velocity of thearms at the point of collision with the trigger plane to ensurethat only the maximum velocity value is used.
The resulting system is a relatively solid locomotion methodwith low accidental stops and walks, while allowing the user
freedom to look around and control their walking speed without the push of a button.
USER STUDY

The user study focuses on a direct comparison between the mechanics present in VR-Step (used to represent WIP-based solutions) with ArmSwingVR, both which are easily accessible
for consumers and are software-based solutions. Furthermore,since this study is aimed towards standing VR experiences,
to some degree, the users are able to physically walk arounda fixed amount of space due to the Vive’s tracking. Our implementation of VR-Step was based on the HMD’s spatial
tracking data. The user firstly needs to press both the grip

Figure 2. Top-down view of the directional vectors.

192

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

Figure 5. View of the participant, where the blue arrow is the location of
the ball and red arrow is the location of the basket.
Figure 4. Virtual routes for the user study.

buttons while standing still to calibrate the height data. This
creates a trigger collider above the HMD that can only be
triggered when the WIP state is activated. This is as similar
to VR-Step in which a distinct jogging motion is required[26]. For this method, a slightly modified state machine algorithm was used, composed of five states; Idle, Transition,
Triggered, Walk, and Walk2. After determining the height ofthe collider, the system checks if the user’s head enters thecollider. If it does, the Transition state activates. This state
acts as a transitional phase or safety measure to determine
if the user desires to walk or was simply performing otherforms of interaction. In this state, the system checks the user’s
head’s location within 0.5 seconds. If the head has exited thecollider, the Step phase is initialized. Otherwise, it goes to
the Triggered state. This state activates when the user’s headhas been in the collider for a period of time. If the head exits
the trigger again within 0.5 seconds, Step is registered. Otherwise, it returns to the Idle state. In the Step state, velocity
is induced to the rigid body, creating a forward motion at thefacing direction. Unlike ArmSwingVR, facing direction andwalking direction are not independent. Maintaining this velocity depends on the user’s capabilities to alternate the head
position from entering and exiting the collider, i.e. a headbobbing motion. Therefore, Step2 was created for when thehead enters back the collider while in the Step state. Step2
then reverts back to Step when the head exits the collider again,
for a continues induced velocity. If none of this conditions
are met, the state reverts back to Transition to check again,
and no more velocity is induced. Each participant is given
about 5 minutes to familiarize themselves with the locomotion
controls for both methods before the study is initialized.
To ensure that the speed of movement of the participant for
both the locomotion methods are the same, the previously
mentioned feature regarding controllable locomotion speed isdisabled. We obtain the immersion and Simulator Sickness
data through the Presence Questionnaire and Simulator Sickness Questionnaire (SSQ) [11]. To determine its effectiveness,the participant is required to navigate a series of routes shownin Figure 4 while picking up virtual balls that they find using
the trigger button.

193

These balls must then be placed into a virtual basket locatedfurther down the route, where it counts for 1 point per ball.
After each score, the next route appears and the participantmust repeat the process. Figure 5 shows a giant arrow hovering over both the ball and the basket so that their positions
are known. The routes were designed in a way that forces theparticipants to navigate in all four directions. They are alsostraightforward to eliminate any requirement for the participant’s spatial awareness. This task runs for 15 minutes per
participant, for each of the method. Even though extended
VR sessions are not advisable [15], VR is improving every
day and full story-based 3D games are being developed for
VR as of this moment, therefore we deemed it necessary forparticipants to spend 15 minutes for each method to determine
the outcome and energy expenditure.
At the end of each locomotion experiment, the participants are
required to complete the SSQ to determine their feedback on
any induced motion sickness [10]. To determine the immersion and sense of realism, the presence questionnaire [35] wasdeemed suitable because it covers a wide area of applications
including locomotion in VR. We chose to exclude the soundand haptics-based question as they are not related to the current study. To determine energy consumption and workload,
the NASA task load and heart rate monitoring is used [7]. For
the quantitative analysis of performance, we compare the scoreachieved through both methods. The participant’s beats per
minute (BPM) is taken three times each, prior to and after bothstudies to determine the heart rate, for a total of 12 readings
per participant. Even though heart rate data can be further
improved by coupling it with accelerometer, we determinedthat since the movement mechanic for both of the methods arefundamentally different, using an accelerometer is not suitable.
Furthermore, BPM data can be easily obtained from various
health monitoring devices, in our case, the Samsung Galaxy S6Edge+ smartphone. Thus, data from both the NASA task loadand heart rate monitoring are sufficient and relatively accurate
to determine energy consumption [7, 9, 31]. Additionally, weuse both the NASA task load and heart rate because relying
on only one of them may not achieve the accuracy we desire.
Relying on a purely qualitative analysis is rather subjective,
while heart rate monitoring is also associated with stress level[30]. However, for the purpose of this user study, since theparticipants will mostly be actively engaging in the VR environment using both locomotion methods, it is more than likely

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

tistical significance between pre-test, ArmSwingVR and WIP(F(2) = 10.951, p = 2.92x10−10 ).
In terms of presence, the results are divided into the following
sub categories; realism, possibility to act, quality of interface,possibility to examine and self-evaluation of performance.
Figure 8 shows the participants’ feedback using the presencequestionnaire for both methods.

Figure 7. Chart for SSQ score.

Figure 6. User study for ArmSwingVR (top) and WIP locomotion (bottom).

that the rise in BPM is due to physical activity. A total of
18 participants were recruited, comprising of 12 males and 6
females aged between 20 to 27. All of them do not have priorcardio-related health issues related and are inexperienced withboth mechanics of ArmSwingVR and WIP.

Figure 8. Chart for Presence score.

RESULTS

Comparison was already made between actual walking and
WIP [29], however, ArmSwingVR is a novel method that has
not been developed or studies up previously when it comes to
VR locomotion. Starting with the overall score for the participants, the average score for ArmSwingVR is 7.44 while for
WIP is 7.22, suggesting that they both perform equally underequal locomotion speed. Figure 7 shows the SSQ analysis results for both methods based on the SSQ computation method[11] with regards to nausea, oculomotor, disorientation, andthe total score.
Interestingly, one of our assumptions was that ArmSwingVR
does not cause more motion sickness compared to VR-Step’s
solution. Yet, our results show that ArmSwingVR produces
less sickness with regards to nausea and the total SSQ score,
whereas WIP method shows to be better in terms of oculomotor by a small margin and disorientation. Disorientationin particular is interesting, because the participants seems tobe less disorientated compared to the rest state as well. A
T-test analysis for each of the category between ArmSwingVR
and WIP shows no significant difference except for nauseawith a score of p = 0.0022. This was reinforced with an
Analysis of Variance (ANOVA) for nausea that showed sta-

194

Performing a T-test for these forms of presence gives us a pscore of 0.94, 0.97, 0.37, 0.88 and 0.81 respectively, proving that there exists no statistical significance between ArmSwingVR and WIP. This also means that no significant senseof presence was sacrificed for the participants and the immersion level is comparable and preserved.
By observing the heart rate, we can clearly indicate that theWIP method causes a much higher BPM for the participants
since it requires more motion and energy for a continuous jogging motion, as opposed to ArmSwingVR. Figure 9 shows thegeneral BPM readings for the participants. Figure 10 shows
the rise in heart rate for the participants for both ArmSwingVR
and WIP, by subtracting the heartrate with its pre-test values.
Overall, it can be seen that the highest rise in heart rate fora participant is 51.66, which was a rise from 78.67 BPM to
130.33 BPM for WIP approach. ArmSwingVR has a significantly less rise in heart rate with the highest value being at
24.33, which was a rise from 87 BPM to 120.33 BPM. This
clearly indicates that WIP methods generates a higher heartrate, which is associated to a higher expenditure of energy. To
enforce this, the NASA task load data in figure 11 illustrates aquantitative take on this analysis on energy expenditure. Thisscore is taken after each participant answers the questionnaire

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

for mental demand, physical demand, temporal demand, performance, effort, and frustration, followed by weighting eachof these factors.

Figure 9. Heart rate against time during idle (left) around 80 BPM, ArmSwingVR (middle) around 95 BPM, and WIP (right) around 120 BPM.

both methods. Gliding is when the participant stops moving,
and the system takes an additional second to actually stop.
This gliding issue does cause some motion sickness. However,similar to VR-Step, the gliding issue is not perceived oncevariable speed control is activated [26]. This is because eventhough gliding may still exist, since the system accelerates anddecelerates according to the user, it is more difficult to notice.
The immersion factor of WIP cannot be ignored, however, acontinuous jogging motion is quite tiring as most if not all participants ended up sweaty and panting after just 15 minutes inthe VR session. This is the reason why nausea scored high for
the WIP method, since sweating was taken into considerationin its scoring. Another issue with WIP that is worth mentioning is that since the jogging motion uses feet movement (eventhough it is not obligatory for the system to function), most
if not all participants tend to drift from their original position.
This drifting often causes them to move away from the Vive’s
tracking area, or in some occasions, minor collision with thephysical wall of the room. This is one of the factors that makeWIP methods less desirable for social space usage.
For ArmSwingVR, most participants had no issue performing
it for 15 minutes. Each participant was also clearly told tohold the controllers facing forward since the walking direction
is influenced by it. However, some of them start to hold thecontrollers in other angles particularly after about 10 minutes,
causing them to not walk straight, thus inducing a small but
negligible amount of sickness. However, most participantsalso added that this feature is more realistic. After trying on
the WIP method, they tried to do the same, and this caused
some motion sickness since WIP methods rely solely on facing
direction. Since ArmSwingVR requires precise tracking of
the hand trackers and HMD, it is vital for the participant tomaintain in the Vive’s tracking area. Thankfully, since nofeet movement was present, the drifting issue can be avoided.
Occlusion may still happen occasionally, but this is more of anissue for any infrared (IR)-based tracking. If sensing is basedon inertial sensors, this issue is mitigated.

Figure 10. Chart for each participant’s rise in heart rate.

Figure 11. Chart for overall Nasa Task Load score.

It can be seen that the participants unanimously score higher
for the WIP solution with the highest score at 65.67. The
average ArmSwingVR score is 39.87, while for WIP is 54.18.
DISCUSSION

We showed the feasibility of ArmSwingVR. Studies have already proved that dynamic walking overall induces less motion
sickness compared to static walking in VR [10]. This can also
be mentioned as a comparison between room scale VR where
the user is required to physical walk around and interact, withsitting VR where physical motion is kept to a minimum. According to the feedback of some of the participants, almost
all of them agree that locomotion by arm swing is low in profile yet remains immersive, making it suitable for public use.
Furthermore, cables on VR HMDs caused some annoyancefor WIP and rarely for ArmSwingVR, though as VR evolves,
wireless solutions are more than likely. Some of the participants noted that there is some gliding issue with regards to

195

Overall, all participants agree that ArmSwingVR is the better
choice for VR navigation use even in public spaces, unless the
VR environment was designed for working out. Furthermore,since some degree of physical interaction is required for locomotion as opposed to more traditional means like button input
and blink teleport, the immersion is preserved. This allows fora wider target audience including the elderly to indulge in amore immersive VR experience.
LIMITATIONSNaturally, the main drawback of ArmSwingVR at this pointof time is that it is only limited to VR systems that provide
controllers that tracks both hand positions. However, as VR
becomes progressively better and more main stream, such controllers will surely be adapted in other VR solutions, until the
point where gesture-based recognition becomes mainstream.
Additionally, ArmSwingVR was developed purely for navigation on a terrain, and was not designed for jumping because
there is no noticeable or distinct arm motion when a human
jumps. In this regard, WIP systems should be able to performbetter, though this is another matter entirely but worth mention-

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

ing. Lastly, since the arms need to be constantly swinging, it
is difficult to perform other forms of interactions that requires
hand gestures while navigating. Although the same can be said
with most other WIP methods, it is nevertheless a matter thatneeds to be considered depending on the application. Even
though sitting VR experience was not covered in this study,
nevertheless it is worth considering. The algorithm currently
used for ArmSwingVR does not support sitting experience at
the moment, though that can be easily added on. Interestingly,
some of the participants did mention that arm swing while
sitting would be quite acceptable as opposed to head bobbing
motion which is strange with no leg movement. This is bestexperienced with a chair that can rotate for turning around.

4. Steve Collins, Andy Ruina, Russ Tedrake, and Martijn
Wisse. 2005. Efficient bipedal robots based on
passive-dynamic walkers. Science (New York, N.Y.) 307,5712 (2005), 1082–1085. DOI:
http://dx.doi.org/10.1126/science.1107799

5. Steven H Collins, Peter G Adamczyk, and Arthur D Kuo.
2009. Dynamic arm swinging in human walking.
Proceedings. Biological sciences / The Royal Society 276,
1673 (2009), 3679–3688. DOI:
http://dx.doi.org/10.1098/rspb.2009.0664

CONCLUSION AND FUTURE WORKS

As mentioned previously, seeing how different VR systems
can be, it is difficult if not impossible to find one best locomotion method in VR. The proposed ArmSwingVR manages
to reduce energy consumption while still being immersive,
making it suitable for wider audience of a different age gap, as
well as for social spaces. Due to its software-based solution,
users do not need to rely on additional peripherals and sensing
methods like sensor-equipped shoes, etc.
In the future, ArmSwingVR will be further modified for to be
used while sitting. With regards to motion tracking controllers,
another tracking method that is quite likely implemented in
the next few generations of VR hardware is eye tracking [16].
This is an interesting notion since eye tracking can be built into
the HMD itself, thus still negating the need for additional third
party sensing hardware, yet still provide an additional layerof input. By combining eye tracking with ArmSwingVR, newexperiences can be developed with high immersion. This iswhere WIP methods may find it hard to benefit, since continuous head bobbing motion does not bode well with eye tracking.
Lastly, we plan to perform an in-depth analysis on the degreeof acceptance for ArmSwingVR in social spaces. Since wehave gathered a rather positive feedback regarding this matterfrom the participants, we plan to conduct a social experimentto quantify this degree of acceptance. ArmSwingVR may notoffer the subtlest of interactions, but it provides a higher immersion that is essential for any VR experience without being
too obtrusive.
REFERENCES

1. Pandy Marcus Anderson, Frank C., Pandy Marcus
Anderson Frank C., Pandy Marcus Anderson, Frank C.,
and Pandy Marcus Anderson Frank C. 2001. Dynamic
Optimization of Human Walking. Journal of
Biomechanical Engineering 123, October (2001),
381–390. DOI:http://dx.doi.org/10.2307/1218045
2. Gabriel Cirio, Maud Marchal, Tony Regia-Corte, andAnatole Lécuyer. 2009. The Magic Barrier Tape: a Novel
Metaphor for Infinite Navigation in VirtualWorlds with a
RestrictedWalkingWorkspace. Proceedings of the 16th
ACM Symposium on Virtual Reality Software andTechnology - VRST ’09 1, 212 (2009), 155. DOI:

6. Rudolph P. Darken, William R. Cockayne, and David
Carmein. 1997. The omni-directional treadmill: A
Locomotion Device for Virtual Worlds. Proceedings ofthe 10th annual ACM symposium on User interface
software and technology - UIST ’97 (1997), 213–221.
DOI:http://dx.doi.org/10.1145/263407.263550
7. S L Fischer, P B Watts, R L Jensen, and J Nelson. 2004.
Energy expenditure, heart rate response, and metabolic
equivalents (METs) of adults taking part in children’s
games. Journal of sports medicine and physical fitness 44,
4 (2004), 398.
8. Doron Friedman, Robert Leeb, Angus Antley, Maia
Garau, Christoph Guger, C. Keinrath, Anthony Steed,
Gert Pfurtscheller, and Mel Slater. 2004. Navigating
virtual reality by thought: First steps. Exploring the Sense
of Presence (2004), 160.
9. Andrew P Hills, Najat Mokhtar, and Nuala M Byrne.
2014. Assessment of physical activity and energy
expenditure: an overview of objective measures.
Frontiers in nutrition 1, 5 (2014), 1–16. DOI:
http://dx.doi.org/10.3389/fnut.2014.00005

10. Beverly K Jaeger and Ronald R Mourant. 2001.
Comparison of Simulator Sickness Using Static andDynamic Walking Simulators. Proceedings of the Human
Factors and Ergonomics Society Annual Meeting 45(2001), 1896–1900. DOI:
http://dx.doi.org/10.1177/154193120104502709

11. Robert S. Kennedy, Norman E. Lane, Kevin S. Berbaum,and Michael G. Lilienthal. 1993. Simulator Sickness
Questionnaire: An Enhanced Method for Quantifying
Simulator Sickness. (1993). DOI:
http://dx.doi.org/10.1207/s15327108ijap0303_3

12. Marc D Klimstra, Evan Thomas, Rebecca H Stoloff,
Daniel P Ferris, E Paul Zehr, Marc D Klimstra, Evan
Thomas, Rebecca H Stoloff, and Daniel P Ferris. 2015.
Neuromechanical considerations for incorporating
rhythmic arm movement in the rehabilitation of walking
Neuromechanical considerations for incorporating
rhythmic arm movement in the rehabilitation of walking.
026102, 2009 (2015). DOI:
http://dx.doi.org/10.1063/1.3147404

13. Ben D Lawson. 2014. Motion Sickness Symptomatology
and Origins. (2014).

http://dx.doi.org/10.1145/1643928.1643965

3. Games Cloudhead. 2015. VR Navigation. (2015).
http://cloudheadgames.com/cloudhead/vr-navigation/

196

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany

14. Morgan McCullough, Hong Xu, Joel Michelson,
Matthew Jackoski, Wyatt Pease, William Cobb, William
Kalescky, Joshua Ladd, and Betsy Williams. 2015. Myoarm: swinging to explore a VE. In Proceedings of the
ACM SIGGRAPH Symposium on Applied Perception.
ACM, 107–113.

presence in virtual reality. ACM Transactions on
Computer-Human Interaction 2, 3 (1995), 201–219. DOI:
http://dx.doi.org/10.1145/210079.210084

25. LÃl’o Terziman, Maud Marchal, Mathieu Emily, Franck
Multon, Bruno Arnaldi, and Anatole Lécuyer. 2010.
Shake-your-head. Proceedings of the 17th ACM
Symposium on Virtual Reality Software and Technology VRST ’10 (2010), 27. DOI:

15. Oculus. 2014. Introduction to Best Practices. (2014).
https://developer3.oculus.com/documentation/intro-vr/
latest/concepts/bp_intro/

http://dx.doi.org/10.1145/1889863.1889867

16. Yun Suen Pai, Benjamin Tag, Benjamin Outram,
Noriyasu Vontin, Kazunori Sugiura, and Kai Kunze.
2016. GazeSim: simulating foveated rendering using
depth in eye gaze for VR. In ACM SIGGRAPH 2016
Posters. ACM, 75.

26. Sam Tregillus and Eelke Folmer. 2016. VR-STEP:
Walking-in-Place using Inertial Sensing for Hands Free
Navigation in Mobile VR Environments. Proceedings ofthe 2016 CHI Conference on Human Factors in
Computing Systems - CHI ’16 (2016), 1250–1255. DOI:
http://dx.doi.org/10.1145/2858036.2858084

17. Jaeheung Park. 2008. Synthesis of natural arm swing
motion in human bipedal walking. Journal of
Biomechanics 41, 7 (2008), 1417–1426. DOI:

27. Michel Treisman. 1977. Motion sickness: an evolutionary
hypothesis. Science 197, 4302 (1977), 493–495.

http://dx.doi.org/10.1016/j.jbiomech.2008.02.031

28. Unity. 2015. Movement in VR. (2015).

18. Randy Pausch, Tommy Burnette, Dan Brockway, andMichael E. Weiblen. 1995. Navigation and locomotion in
virtual worlds via flight into hand-held miniatures.
Proceedings of the 22nd annual conference on Computer
graphics and interactive techniques - SIGGRAPH ’95(1995), 399–400. DOI:

https://unity3d.com/learn/tutorials/topics/virtualreality/movement-vr

29. Martin Usoh, Kevin Arthur, Mary C Whitton, Rui Bastos,
Anthony Steed, Mel Slater, and Frederick P Brooks. 1999.
Walking {&}gt; walking-in-place {&}gt; flying, in virtualenvironments. Proceedings of the 26th annual conference
on Computer graphics and interactive techniques SIGGRAPH ’99 (1999), 359–364. DOI:

http://dx.doi.org/10.1145/218380.218495

19. Patrick Péruch, LoÃŕc Belingard, and Catherine
Thinus-blanc. 2000. Transfer of Spatial Knowledge from
Virtual to Real Environments. Spatial Cognition II 1998
(2000), 253–264. DOI:

http://dx.doi.org/10.1145/311535.311589

30. T G Vrijkotte, L J van Doornen, and E J de Geus. 2000.
Effects of work stress on ambulatory blood pressure,heart rate, and heart rate variability. Hypertension (Dallas,
Tex. : 1979) 35, 4 (2000), 880–6.

http://dx.doi.org/10.1007/3-540-45460-8{_}19

20. Sharif Razzaque, David Swapp, Mel Slater, Mary C
Whitton, and Anthony Steed. 2002. Redirected Walking
in Place. (2002).

31. Klaas R. Westerterp. 2009. Assessment of physicalactivity: A critical appraisal. European Journal ofApplied Physiology 105, 6 (2009), 823–828. DOI:

21. Bernhard E. Riecke, Bobby Bodenheimer, Timothy P.
McNamara, Betsy Williams, Peng Peng, and Daniel
Feuereissen. 2010. Do we need to walk for effective
virtual reality navigation? Physical rotations alone may
suffice. Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence andLecture Notes in Bioinformatics) 6222 LNAI (2010),
234–247. DOI:

http://dx.doi.org/10.1007/s00421-009-1000-2

32. Betsy Williams, Stephen Bailey, Gayathri Narasimham,
Muqun Li, and Bobby Bodenheimer. 2011. Evaluation ofwalking in place on a Wii balance board to explore avirtual environment. ACM Transactions on Applied
Perception 8, 3 (2011), 1–14. DOI:

http://dx.doi.org/10.1007/978-3-642-147494{\protect\T1\textbraceleft}{_}{\protect\T1\textbraceright}21

http://dx.doi.org/10.1145/2010325.2010329

33. Preston Tunnell Wilson, William Kalescky, Ansel
MacLaughlin, and Betsy Williams. 2016. VR locomotion:
walking> walking in place> arm swinging. In
Proceedings of the 15th ACM SIGGRAPH Conference on
Virtual-Reality Continuum and Its Applications in
Industry-Volume 1. ACM, 243–249.

22. Roy A. Ruddle and Simon Lessels. 2006. For efficientnavigational search, humans require full physicalmovement, but not a rich visual scene. Psychological
Science 17, 6 (2006), 460–465. DOI:
http://dx.doi.org/10.1111/j.1467-9280.2006.01728.x

34. Preston Tunnell Wilson, Kevin Nguyen, Alyssa Harris,and Betsy Williams. 2014. Walking in place using theMicrosoft Kinect to explore a large VE. Proceedings ofthe 13th ACM SIGGRAPH International Conference on
Virtual-Reality Continuum and its Applications in
Industry - VRCAI ’14 (2014), 27–33. DOI:

23. Roy. A. Ruddle and S. Lessels. 2009. The benefits ofusing a walking interface to navigate virtualenvironments. ACM Transactions on Computer-Human
Interaction 16, 1 (2009), 1–18. DOI:
http://dx.doi.org/10.1145/1502800.1502805

http://dx.doi.org/10.1145/2670473.2670492

24. Mel Slater, Martin Usoh, and Anthony Steed. 1995.
Taking steps: the influence of a walking technique on
197

Session 6: Experiencing Virtual Reality

MUM ’17, Stuttgart, Germany(1998), 225–240. DOI:

35. Bob G Witmer and Michael J Singer. 1998. Measuring
Presence in Virtual Environments: A Presence
Questionnaire. Presence: Teleoper. Virtual Environ. 7, 3

http://dx.doi.org/10.1162/105474698565686

36. L Yan and R S Allison. 2004. New simple virtual walking
method âĂŞ walking on the spot. (2004).

198