My name is Chek and I represent the Centre for Immersification at SIT,
to present our research on VR walking-in-place locomotion methods.
VR locomotion based on tracking real walking is greatly limited by the finite, and often rather small, real-life spaces.
Joystick-based locomotion is a simple classic method to move around virtually,
but commonly suffers from excessive cybersickness with head-mounted displays.
Teleportation does not suffer from cybersickness as much
but lacks the desired immersion required in VR use cases that prioritize natural locomotion.
Walking-in-place (WIP) offers some kind of middle ground
between the highly immersive real walking and highly practical synthetic methods.
Mechanical locomotion platforms have commonly been used to portray the future of VR,
but has questionable usability and the equipment are not readily accessible to the common consumer.
For the much more accessible tracker-based walking-in-place solutions,
initial work has shown promise in minimizing cybersickness whilst offering immersion.
There is a large body of work in VR locomotion,
and walking-in-place is one of the main types of methods being studied.
Current literature is primarily focused on devising novel methods focused on technical quantitative evaluations.
For example, the accuracy and latency of the movements.
There are limited studies targeted at understanding the deeper user experiences afforded by different walking-in-place methods.
We hence present our study that aims to provide a
deeper understanding of WIP user experiences across common tracker-based solutions.
The VR system used for the study is a commuting simulation of Singaporean urban environments,
developed in collaboration with the Land Transport Authority of Singapore,
to explore how VR can be used as a tool to evaluate deeper commuter experiences.
Natural locomotion is hence central to the interactive experience in this commuting simulator.
Informed by prior literature, we implemented several popular tracker-based walking-in-place methods.
Participants first completed a profiling survey.
They then proceeded to 1 of the 4 locomotion conditions.
In each condition participants first familiarized themselves with the equipment and controls.
They then proceeded to experience the locomotion method via a walking commuting experience that lasted around 10 minutes.
After completing each condition, the participants filled 3 questionnaires while having some physical rest before the start of the next condition.
After all conditions are completed, a semi-structured interview was conducted
to understand any remaining nuances on their experiences that were not reported previously.
A total of 40 participants completed the entire study, with the demographics as shown.
The primary qualitative data collected for each participant consisted of mainly
the video recordings and transcripts containing audio and observational notes.
We did an 80/20 split of the data into a training and test set.
The test set was reserved for calculation of the Inter-Rater Reliability score.
The coding team of 5 started with a coding workshop to define and standardize the procedures and create an initial set of codes.
The team first coded the 80% training set, reviewing both the transcripts and video recordings together,
to generate a complete codebook of codes with descriptions and sample corpus segments.
Using the completed codebook, two researchers independently coded the test set and the IRR was calculated using Cohen's Kappa.
The final Kappa value is as shown.
After 100% of the corpus has been coded, it is checked and compiled into the table organized as shown here.
The Virtual Reality Sickness Questionnaire,
the Igroup Presence Questionnaire,
and Flow Short Scale questionnaire data were also consolidated to serve as auxiliary data.
In this presentation, we will focus on 2 out of the 6 themes to explain how we interpreted the data.
Please refer to the paper for the rest of the themes.
The first theme highlights how overall reports of CYBERSICKNESS were minimal.
With low VRSQ scores across the board.
This corresponds with much prior work with, walking-in-place.
However, there are also some more interesting deeper insights that can be gathered from our data,
for example, the relationship between exertion and cybersickness levels.
Within the cybersickness accounts, the conditions with lower exertion levels, for example ARM-SWING, induced more CYBERSICKNESS.
Relatively more participants appeared EFFORTLESS when performing ARM-SWING, and nobody mentioned that ARM-SWING was FATIGUING.
On the other hand, LEG-LIFT has no reports of CYBERSICKNESS.
Here, EFFORTLESS accounts were relatively low compared to the high accounts of FATIGUING.
ARM-SWING also had the highest overall VRSQ scores, while LEG-LIFT has the lowest.
The next theme shows there was great variety in the way participants performed walking-in-place.
With some appearing to instinctively exhibit gestures that were AFFORDED-BY-TRACKERS,
related to where these trackers were on their bodies.
While others evolved into an ADAPTED-WIP-TECHNIQUE over time.
An important data point from the Flow State Scale results,
was that Fluency of Performance was reported to be moderate to high overall,
even though there were many observations of AWKWARD-WIP-TECHNIQUEs across the conditions.
This may indicate that participants felt competent with their own way of performing WIP, however awkward it might appear to an observer.
Based on an analysis of our results, we laid out the following key insights to aid the design of walking-in-place solutions in our context.
For example, based on the findings on the effects of trackers on user behaviors,
and the relationships between exertion and cybersickness, as seen earlier,
we recommend to consider the affordances induced when designing a tracker system for walking-in-place.
As another example, based on the other finding of users requiring mental effort in regulating their physical positions,
we recommend to factor in physical space when designing for WIP.
For example, simply placing a textured mat under the users’ feet may help to provide a better sense
of physical position without much cognitive load.
Please refer to our paper for more details on how to interpret these insights.
In conclusion, focus of the paper was to present the 6 key themes in a detailed yet structured fashion
so that the reader can use it for your own interpretation depending on your purpose.
We hence end this presentation with some important factors to understand when interpreting the data.
We focused on accessible components, and our results relate best with setups that have similar tracking precision,
like those that include external beacons.
We also targeted VR use cases that have natural walking at the core of their experiences,
and our results may not directly apply to other types of VR experiences.
Finally there are many nuances when implementing the different walking-in-place methods using different trackers,
which is best understood by reading the code released in the github repository linked here.
For everything else, please read our paper. Happy walking in the metaverse.