# Vibrotactile Feedback to Make Real Walking in Virtual Reality More
Accessible

M. Rasel Mahmud, Michael Stewart, Alberto Cordova, and John Quarles M. Rasel
Mahmud is with the Department of Computer Science, University of Texas at San
Antonio, San Antonio, TX, 78249.
E-mail<EMAIL_ADDRESS>Michael Stewart is with the Department of
Kinesiology, University of Texas at San Antonio, San Antonio, TX, 78249.
E-mail<EMAIL_ADDRESS>Alberto Cordova is with the Department of
Kinesiology, The University of Texas Health Science Center at San Antonio, One
UTSA Circle, San Antonio, TX, USA 78229-3901
E-mail<EMAIL_ADDRESS>John Quarles is with the Department of
Computer Science, University of Texas at San Antonio, San Antonio, TX, 78249.
E-mail<EMAIL_ADDRESS>
###### Abstract

This research aims to examine the effects of various vibrotactile feedback
techniques on gait (i.e., walking patterns) in virtual reality (VR). Prior
studies have demonstrated that gait disturbances in VR users are significant
usability barriers. However, adequate research has not been performed to
address this problem. In our study, 39 participants (with mobility
impairments: 18, without mobility impairments: 21) performed timed walking
tasks in a real-world environment and identical activities in a VR environment
with different forms of vibrotactile feedback (spatial, static, and rhythmic).
Within-group results revealed that each form of vibrotactile feedback improved
gait performance in VR significantly (p $<$ .001) relative to the no
vibrotactile condition in VR for individuals with and without mobility
impairments. Moreover, spatial vibrotactile feedback increased gait
performance significantly (p $<$ .001) in both participant groups compared to
other vibrotactile conditions. The findings of this research will help to make
real walking in VR more accessible for those with and without mobility
impairments.

###### Index Terms:

Virtual Reality, accessibility, usability, vibrotactile feedback, gait
improvement, Head-Mounted Displays, HMDs.

##  1 Introduction

The use of virtual reality (VR) is inaccessible to a significant number of
individuals with disabilities [1, 2, 3, 4, 5]. It is estimated that over one
billion, or 15% of the global population, suffer from a disability [6].
However, research and development in VR rarely consider these individuals,
leading to exclusive and inaccessible experiences. Specifically, many users
experience gait disturbances caused by VR, which ultimately limits its
usability and benefits [1, 3, 4]. This issue can be particularly challenging
for persons with mobility impairments (MI), as they suffer from functional
gait disorders, which make it increasingly difficult for them to use VR
technologies. For example, individuals who suffer from MI and other
disabilities may find it very challenging to perform various locomotor
movements in VR without fear of falling or injuring themselves. Despite these
challenges, little research has been conducted in order to mitigate them.
Research in the field of assistive technologies has revealed that some
multimodal feedback techniques [7, 8] can assist individuals with MI with
their daily activities as well as improve gait and balance. A number of
studies have been conducted with the aim of improving balance and gait for
persons with disabilities using assistive technology based on visual feedback
[9, 10, 11, 12]. However, one of the major issues with visual feedback is that
it interferes with the presence in VR more than the vibrotactile feedback
[13]. Nevertheless, vibrotactile feedback to improve gait and balance in VR
has received very little attention. Our study investigated the mitigation of
gait disturbance issues by implementing various vibrotactile feedback
techniques (e.g., spatial, static, and rhythmic) in VR for participants with
and without MI. We conducted a timed walking task using a pressure-sensitive
walkway (GAITRite) for the purpose of quantitative gait analysis. However,
these feedback effects on gait performance were not measured post-study. The
purpose of this study was to make real walking in immersive VR more accessible
by using vibrotactile feedback, as well as to examine its effectiveness in
enhancing gait efficiency.

##  2 BACKGROUND AND RELATED WORK

###  2.1 Gait Disturbances in VR

The use of VR has been shown to cause instability and gait disturbances in
previous studies. Additionally, the use of head-mounted displays (HMDs) can
result in individuals losing stability as a result of end-to-end latency and
illusory impressions of their body movements. HMDs block visuals from the real
world due to the obstructing of visual feedback [14, 15]. Additionally, long-
term exposure to VR caused postural instability [16]. As a result of these
postural instabilities, walking in a virtual environment (VE) can lead to gait
instability [17]. In addition, Riem et al. [18] found that step lengths were
significantly affected by VR in comparison to baseline settings (p $<$ .05).
In other studies [19], imbalance and gait disturbance in VR have also been
reported. A study conducted by Horsak et al. [20] examined the gait
differences of 21 participants (male: 9, female:12, age: 37.62 ± 8.55 years)
during walking in an HMD-based VE. Their findings revealed that walking speed
was reduced by 7.3% in the HMD-based VE. A study by Canessa et al. analyzed
the differences between real-world walking and immersive VR walking while
wearing an HMD [21]. Based on their results, they found that walking velocity
decreased significantly (p $<$ .05) in immersive virtual reality when compared
to the real world. A VR HMD with continuous multidirectional visual field
perturbation was used by Martelli et al. [22] in order to study how healthy
young adults’ gaits were modified and altered as they walked in a VE while
using a VR Headset. Twelve healthy young adults walked for six minutes on a
pathway in four different settings. As a result of perturbation of the visual
field, stride length, width, and variability of stride were reduced. Despite
these gait disturbance issues in VR, few attempts have been made in the past
to address it. Thus, we investigated these gait disturbance issues in order to
ease the experiences of walking in VR and make VR more accessible.

###  2.2 Gait Improvement After VR Intervention

Despite the focus of our research on VR accessibility and gait improvement
while in VR, it is important to examine how VR rehabilitation applications
have previously been used to foster balance and gait improvement that persists
after the VR experience has ended [23, 24, 25, 26, 27, 28].

A study by Walker et al. [29] investigated the effectiveness of a low-cost VR
system in improving walking and balance abilities among seven post-stroke
patients. By displaying a television screen in front of a treadmill,
participants were able to experience walking along a city street. Head-mounted
position sensors were used to collect postural feedback. During the study, all
participants were supported by an overhead suspension harness. In the study,
participants were within a year after stroke and had previously received
traditional rehabilitation but had significant gait problems as well. The
study included results from six participants (mean age 53.5 years, range 49-62
years) and excluded one participant due to sickness. According to the results
of the study, there was a significant improvement (p $<$ .05) in balance,
walking speed, and gait functionality after the study. They reported 10%
improvements in Berg Balance Scale (BBS), 38% improvements in walking speed,
and 30% improvements in Functional Gait Assessment (FGA) scores.

In a study conducted by Janeh et al. [30], 15 male patients with Parkinson’s
disease were recruited to investigate the effectiveness of a VR-based gait
manipulation approach aimed at adjusting step length in order to achieve gait
symmetry. By using visual and proprioceptive signals, they were able to
compare natural gait with walking situations when performing VR-based gait
activities. In comparison to natural gait, VR gait activities increased step
width and swing time. Furthermore, Janeh et al. reported that VR might improve
the gait of persons with neurological disorders after experiencing it. As a
result of their observations, they stressed the importance of using virtual
walking approaches in rehabilitation [31].

Nevertheless, the majority of gait rehabilitation approaches did not use VR
techniques. In our study, we investigated immersive VR-based walking where the
VEs were rendered using HMDs.

###  2.3 Gait Disturbances Associated With HMDs for Participants With MI

A study by Winter et al. [32] recruited 36 participants (Male: 10, Female: 26)
without a history of MI and 14 patients (MS: 10, Stroke: 4) with MI to
investigate the effect of an immersive, semi-immersive, and no VR environment
on gait during treadmill training. Participants first completed the treadmill
training without VR. In the semi-immersive VR condition, participants were
presented with a virtual walking path on a monitor. In the immersive VR
condition, the participants experienced the same VR scenario via HMDs. The
results of the study indicated that immersive VR during gait rehabilitation
increased walking speed more significantly (p $<$ .001) than semi-immersive
and no VR conditions for both participant groups. VR conditions did not cause
cybersickness or an increase in heart rate.

In addition, Guo et al. [33] examined the effect of VEs on gait in both
participants with and without MI. It was found that MI participants responded
differently in terms of walking speed, step length, and stride length when
compared to non-MI participants. Despite this, other gait parameters did not
differ significantly between participants with and without MI.

Ferdous et al. [3] analyzed the effects of HMDs and visual components on
postural stability in VR for participants with multiple sclerosis (MS) using
visual feedback. However, they did not examine the effect of vibrotactile
feedback on gait, as in most previous studies on immersive VR with HMDs.
Therefore, the impact of vibrotactile feedback on gait in immersive VR with
HMDs has received insufficient attention. Also, most prior studies have
primarily focused on individuals without MI [34, 35, 36, 37, 38]. These led us
to examine the vibrotactile feedback effect on gait for participants with and
without MI using immersive VR with HMDs.

###  2.4 Vibrotactile Feedback for Gait Improvement in the Real World

Prior studies reported that visual feedback [39], auditory feedback [40], or
vibrotactile feedback [41] in real-world applications reduced postural
instability and gait disturbances for participants with balance and gait
impairments [42, 43, 44]. Vibrotactile feedback is generally favored over
other helpful feedback modalities because it is considered to interact less
with other senses, such as seeing or hearing, which may be restricted by
visual or auditory feedback [45, 46]. A few research have studied the use of
vibrotactile feedback to enhance balance and gait in real-world applications
(e.g., rehabilitation).

For example, Rust et al. examined the impact of vibrotactile feedback on trunk
sway in fifteen MS patients in the real world [47]. The participants wore a
headband containing eight 150 Hz vibrators spaced at 45-degree intervals.
Vibrators were turned on when a sway threshold in the vibrator’s direction was
exceeded. In four weeks, participants performed a variety of training, gait,
and balancing activities. The authors initially assessed trunk sway as a
baseline. Using the SwayStar device, they detected trunk sway with
vibrotactile input. After one and two weeks of training with vibrotactile
feedback, there was a large decrease in trunk sway (p $<$.02) compared to the
baseline. The authors measured a carry-over effect in the fourth week
following three weeks of no training. In addition, they discovered a
considerable carry-over improvement (p $<$.02). In their experiment, standing
with eyes closed on a foam pad had the most significant results, with a 59%
reduction in pitch sway (p $<$.002).

Ballardini et al. [48] recruited 24 participants (11 males, 13 females) to
examine the influence of vibrotactile feedback on standing balance and gait
performance. They designed a system that provides vibrotactile feedback using
two vibration motors located on the front and rear of the body. An
accelerometric measurement encoding that combines the position and
acceleration of the body in the anterior-posterior direction was synced with
the vibration. The objective was to examine two different encoding techniques:
1) constant vibration and 2) vibration with a dead zone (i.e., silence when
the signal was below the given threshold). Using vibrations unrelated to the
actual postural oscillations, they determined whether the informative quality
of the input altered these effects (sham feedback). Nine participants
experienced the vibration always on and sham feedback, while fifteen received
vibration with a dead zone. According to the results, synchronized
vibrotactile feedback lowered postural sway significantly in the anterior-
posterior and medial-lateral directions. In terms of reducing postural sway,
there was no significant difference between the two encoding methods. The
presence of sham vibration feedback enhanced postural sway, highlighting the
significance of the encoded data.

Thirty-nine participants with an imbalance and mobility issues due to the
severe bilateral vestibular loss were recruited by Kingma et al. to examine
how vibrotactile feedback impacts balance and movement in the real world [49].
The participants wore a vibrotactile belt around their waists for two hours
each day for one month. If they were in motion, they had to wear the belt. The
belt’s 12 motors were actuated by a microprocessor. Before and after one month
of daily usage of the belt, participants were asked to verbally assess their
balance and mobility on a 0 to 10 scale. The average ratings for mobility and
balance grew significantly (p $<$.00001) when compared to those obtained
without the belt.

While these studies explored balance and gait in the real world, we
investigated the effects of various vibrotactile feedback on gait in VR for
both participants with and without MI.

##  3 HYPOTHESES

This research investigated the effects of three vibrotactile feedback
conditions (spatial, static, and rhythmic) on gait in a VR environment. We
were inspired by three types of audio feedback that were proven to be
successful in VR [50] and non-VR contexts [51, 52, 53, 54, 55]. Further, based
on previous research on vibrotactile feedback for assistive technology, the
following hypotheses were investigated:

H1: Compared to non-VR baseline without vibrotactile feedback condition, VR
baseline without vibrotactile feedback will exhibit gait disturbances.

H2: Three VR-based vibrotactile feedback conditions (spatial, static, and
rhythmic) will improve gait metrics more than the condition with no
vibrotactile feedback in VR.

H3: Spatial vibrotactile feedback will enhance gait metrics more than static
and rhythmic vibrotactile feedback.

H4: While experiencing the vibrotactile feedback in VR, participants with MI
will encounter greater gait improvement (e.g., velocity) compared to
participants without MI.

##  4 METHODS

###  4.1 Participants, Selection Criteria, and Screening Process

In order to investigate gait improvement using vibrotactile feedback in VR, 39
participants (male: 18, female: 21) were recruited from multiple sclerosis
support groups and the local community. In this study, 18 participants (male:
6, female: 12) had MI due to MS. Among the participants with MI, 44.44% were
White, 25.56% were Hispanic, 4.44% were Asian, and 25.56% were African
American. Furthermore, we recruited a group of twenty-one participants without
MI (male: 11; female: 10). Participants without MI constituted 18.18% White,
22.73% Hispanic, 50% African American, 4.55% American Indian, and 4.54% Asian.
Descriptive statistics (mean and standard deviation of age, height, weight,
etc.) of participants in both groups can be found in Table 1. Exclusion
criteria for participants included those with cognitive impairments, severe
vision impairments, cardiovascular or respiratory conditions, or those unable
to walk without assistance. We intentionally recruited people with MI due to
MS because this population is rarely considered during VR development, which
may lead to inaccessible and non-inclusive experiences.

TABLE I: Descriptive statistics for participants | Participant
---
Group
Participants | Age (years) | Height (cm) | Weight (kg)
| Male | Female | Mean | SD | Mean | SD | Mean | SD
MI | 6 | 12 | 45.89 | 10.18 | 165.60 | 10.26 | 83.86 | 28.27
Without MI | 11 | 10 | 47.29 | 12.09 | 166.12 | 10.05 | 88.26 | 16.05

Screening Process: Several methods were used to recruit participants,
including telephone calls, email lists, and flyers. The eligibility of
participants was determined via a telephone pre-screening process. In order to
determine whether a participant could be included or excluded from the study,
we asked about their general demographics, health, and medical histories. As
an example, we confirmed that the individual could visit the on-campus lab and
participate throughout the study. Additionally, we assessed the participants’
history of MI and their ability to walk independently. In order to minimize
participant characteristic imbalances, we ensured that the age, height, and
weight of both participants were proportionally the same (see Table 1).

###  4.2 System Description

The study used a variety of equipment to ensure the safety of participants and
collect data.

####  4.2.1 Vibrotactile Equipment:

We used the following vibrotactile equipment from bHaptics
(https://www.bhaptics.com):

Vest: Participants wore a wireless vest with 40 independently controlled
Eccentric Rotating Mass (ERM) vibrotactile motors. Twenty vibrotactile motors
were placed anteriorly on the vest, and the remaining twenty were placed
posteriorly. The vest was fitted with shoulder snap buttons that allowed it to
be adjusted. The total weight of the vest was 1.6 kg.

Arm Sleeves: The participants wore arm sleeves located between the wrist and
elbow that had adjustable straps. The arm pieces contained six ERM vibration
motors. The arm pieces weighed 0.30 kg each.

Forehead: Six ERM vibrotactile motors were attached to the HMD and were
positioned on each participant’s forehead. It had a weight of 0.08 kg.

Fig. 1 depicts the placement of the vibration motors.

Figure 1:  Vibration motors positions.

####  4.2.2 Computers, VR Equipment, and Software:

The VEs were developed using Unity3D software. The HTC Vive wireless HMD was
used for our experiment, which had a pixel resolution of 2160 x 1200, a 90 Hz
refresh rate, and a 110-degree field of view. To track the position of the
bHaptics vest, we installed a Vive tracker on the back of the vest. A computer
with an Intel Core i7 processor (4.20 GHz), 32 GB DDR3 RAM, and an NVIDIA
GeForce RTX 2080 graphics card was used to render the VE and record the data.

####  4.2.3 Audio-to-Vibrotactile Software:

A vibrotactile output is generated from corresponding audio input with this
software. First, we attached the audio input to the Unity scenes. Then with
the help of the bHaptics Unity plugin, we delivered the audio input to
bHaptics audio-to-vibrotactile software, which converted the audio input to
the corresponding vibration. White noise was used to generate audio input
rather than music or other user-selected tones as white noise has been shown
to change the signal-to-noise ratio and increase balance performance due to
the stochastic resonance phenomenon [56]. Furthermore, bHaptics software
allowed us to control the intensity of the vibrations. We adjusted vibration
intensity until participants stated it was a comfortable intensity.

####  4.2.4 Safety Equipment:

For the safety of our participants, we used a Kaye Products Inc. suspension
walking system consisting of a body harness, thigh cuffs, and suspension
walker.

####  4.2.5 Gait Analysis:

We collected gait metrics from participants using the GAITRite walkway system.
The system consists of a portable 12 feet pressure sensor pad capable of
measuring the gait metrics of participants during a walking test. The GAITRite
walkway system has the capability to provide spatial and temporal gait data of
the participants.

####  4.2.6 Environment:

A controlled lab environment was used during the study ($>$600 square feet.).
The participants and researcher were the only individuals present in the room
in order to minimize any noise or other disruptions from the surrounding
environment. A comparison of the real-world and virtual environments for the
timed walking task can be seen in Fig. 2.

Figure 2:  Comparison between real environment (top) and virtual environment
(bottom) for timed walking task.

###  4.3 Study Conditions

We evaluated three kinds of VR-based vibrotactile feedback techniques and a
condition without vibrotactile feedback to assess how vibrotactile feedback
affects gait in VR. The audio-to-vibrotactile software from bHaptics converted
the audio into corresponding vibrotactile feedback for each feedback condition
(see section 4.2.3).

####  4.3.1 Non-VR Baseline Trials

Participants performed the timed walking task using the GAITRite system
without VR and without receiving any vibrotactile feedback to record
participants’ baseline gait metrics.

####  4.3.2 VR Baseline Trials

A VR baseline measurement for participants was established by performing the
same timed walking task without the application of any vibrotactile feedback
in VR. HMDs and the bHaptics equipment pieces were worn by participants during
this condition.

####  4.3.3 Spatial Vibrotactile Feedback

To generate spatial vibrotactile feedback, the audio was used to set the
vibrotactile feedback patterns. We used Google resonance audio SDK in Unity
for audio spatialization since the plugin uses head-related transfer functions
(HRTFs) to replicate 3D sound more precisely than Unity’s default [57, 58].
The spatial audio in our research was simulated [59] rather than recorded
ambisonic audio [60]. The spatialized audio simulation was then sent to the
audio-to-vibrotactile software, which created spatial vibrotactile feedback.
The forehead bHaptics equipment vibrated to varying degrees as the user
rotated their head. The vibration of the vest was altered based on its
position as sensed by the Vive tracker. The X, Y, and Z coordinates of the 3D
audio source and participant in the VE were 0, 1, 0, and 0, 0, 0,
respectively, indicating that the audio source was positioned directly in
front of the participant.

####  4.3.4 Static Vibrotactile Feedback

We transmitted white noise to the audio-to-vibrotactile software of bHaptics
in order to provide static vibrotactile feedback. All the vibration motors in
the forehead, arm sleeves, and vest were vibrated continuously. The user’s
location had no influence on the feedback. This technique has been shown in
non-VR studies to improve the balance of adults [53].

####  4.3.5 Rhythmic Vibrotactle Feedback

We transmitted a white noise beat at 1-second intervals to the bHaptics audio-
to-vibrotactile software in order to generate rhythmic vibrotactile feedback.
All the vibration motors in the forehead, arm sleeves, and vest were vibrated.
The length of the rhythmic feedback beat was also 1 second. The previous study
has shown that experiencing a steady beat may improve balance and gait in both
those with neurological impairments and older people in non-VR environments
[55].

####  4.3.6 No Vibrotactile Feedback

This was used to measure participants’ balance in VR without vibrotactile
input. In order to remain coherent with previous conditions, participants
continued to wear the HMD, bHaptics suit, arm sleeves, and forehead component,
but no vibrotactile input was supplied.

###  4.4 Study Procedures

Fig. 3 is the flowchart of the whole study procedure.

Figure 3: Study procedure

We first sanitized all the lab equipment (including the HMD, Vive tracker,
bHaptics devices, safety harness, and suspension system). A COVID-19 symptom
screening questionnaire was completed when participants entered the lab. The
research procedures were then explained to the participants, and formal
consent was documented.

####  4.4.1 Pre-Study Questionnaires

There were two questionnaires completed at the beginning of the study: an
Activities-specific Balance Confidence (ABC) form [61] and an SSQ
questionnaire [62]. We requested that participants take off any footwear that
could interfere with the GAITRite system. Study approval was granted by the
Institutional Review Board (IRB).

####  4.4.2 Real World Walking

A GAITRite walkway was used to measure gait metrics in this study. To prevent
fall-related injuries, participants were securely fastened to safety harnesses
and suspension walker. Participants were instructed to walk on the GAITRite at
a speed that they found comfortable. In addition, we instructed them to
complete 180-degree turns at both ends. The GAITRite software requires
participants to step off between trials, as the system cannot accurately
assess turns. Three timed walking trials were conducted [63] while a stopwatch
was used to measure the walking time of the participants.

Figure 4: Participants used harness while performing the timed walking task
utilizing the GAITRite system in both the real world (left) and virtual
environment (right).

####  4.4.3 Virtual Environment Walking

We replicated the walking task in a VE with various vibrotactile feedback
conditions in order to simulate the real-world walking environment. We used
the same harness and suspension system as in the real-world environment to
prevent sudden falls. During the study, participants were instructed to walk
on the virtual GAITRite, which was layered over the physical GAITRite. HMDs
were used to observe the VE while the vibrotactile feedback was applied
through the bHaptics equipment. Three timed walking trials were performed in
VR for each vibrotactile condition (e.g., spatial, static, and rhythmic) and a
no-vibrotactile in VR condition. A counterbalanced order of the four
conditions was applied to all participants in the study. Fig. 4 shows the
comparison of real and virtual environment walking for the participants.

####  4.4.4 Post-Study Questionnaires

A post-study SSQ questionnaire and a demographic form were completed by the
participants at the end of the study. Finally, each participant received
$30/hour compensation and a parking validation ticket.

##  5 METRICS

###  5.1 Gait Metrics

We examined the following gait metrics relative to each vibrotactile feedback
condition in this study.
\- Walking Velocity: The distance traveled (cm) divided by ambulation time
(sec).
\- Cadence: The number of steps taken per minute.
\- Step Time (Left/Right): The amount of time (sec) between the initial
contact points of the opposite foot.
\- Step Length (Left/Right): The distance (cm) between the centers of the
heels of two successive steps taken by opposing feet.
\- Cycle Time (Left/Right): The time (sec) between the initial contact points
of the same foot’s two successive steps.
\- Stride Length (Left/Right): The distance (cm) between the steps of the same
foot.
\- Swing Time (Left/Right): The time (sec) between a foot’s ultimate contact
point and its starting contact point.
\- Stance Time (Left/Right): The time (sec) between the start and final
contact points of a single footstep.
\- Single Support Time (Left/Right): This is the time (sec) between the final
contact of the current footfall and the first contact of the following
footfall of the same foot.
\- Double Support Time (Left/Right): The time (sec) when both feet are on the
ground.
\- Base of Support (Left/Right): The width between one foot and the
progression line of the opposing footstep.
\- Toe-In/Toe-Out (Left/Right): The angle (degrees) between the progression
line and footprint’s midline. Toe-in means that the center-line of the
footprint is inside the line of progression. Toe-out denotes that the center-
line of the footprint is outside the line of progression.
The GAITRite manual has further details on Gait metrics [64].

###  5.2 Activities-specific Balance Confidence (ABC) Scale

Participants were assessed on their balance, mobility, and physical
functionality using the Activities-specific Balance Confidence Scale (ABC).
Sixteen items are used in this questionnaire to determine whether an
individual is confident in performing daily functions without losing balance
[61]. Each participant is asked to rate their level of confidence in each
specific activity on a scale of 0% (not confident) to 100% (most confident).
ABC Scale scores are calculated by dividing the sum of the ratings (0-1600) by
16. ABC scores below 50 indicate low functioning. Additionally, ABC scores
between 50-80 indicate moderate levels of functioning, and ABC scores above 80
indicate high levels of functioning.

###  5.3 Simulator Sickness Questionnaire (SSQ)

Cybersickness of the participants resulting from virtual environment exposure
was measured by the Simulator Sickness Questionnaire (SSQ). The SSQ assesses
participants’ physiological discomfort due to cybersickness using 16 symptoms
that are organized into three different categories (disorientation, nausea,
and oculomotor disturbance) [62].

##  6 STATISTICAL ANALYSIS

For each investigated gait metric, the Shapiro-Wilk test (p $>$ .05),
histograms, normal Q-Q plots, and box plots revealed that the data was
normally distributed for both participants with and without MI. Then, we
performed a 2$\times$5 mixed-model ANOVA with two between-subject factors
(participants with MI and participants without MI) and five within-subject
factors (five study conditions: baseline, spatial, static, rhythmic, and no
vibrotactile) to identify any significant difference in study conditions. When
there was a significant difference, post-hoc two-tailed t-tests were performed
for pairwise within and between-group comparisons to find the specific
differences between two study conditions. For cybersickness analysis, we also
used two-tailed t-tests comparing pre-session and post-session SSQ scores for
each participant group. Additionally, we conducted two-tailed t-tests
comparing the ABC scores of both participant groups to assess the difference
in physical ability. Bonferroni corrections were applied to all tests
involving multiple comparisons.

##  7 RESULTS

Seven of the twelve studied gait metrics (walking velocity, cadence, step
length, stride length, step time, cycle time, and swing time) improved
significantly under varied vibrotactile feedback conditions, but the other
five gait metrics improved insignificantly for both groups of participants.
Gait improvement also varied greatly depending on the different vibrotactile
feedback conditions. We assessed the gait characteristics from the start to
finish of the trials (not any specific portion of the trials). Additionally,
both left and right leg data were evaluated. There was no significant
difference in the results for the left and right legs data. For simplicity, we
presented the average data for the left and right leg for all gait metrics.

The mixed-model ANOVA test for all individuals revealed a significant
difference in walking velocity, F(1,123) = 69.8, p $<$ .001; and effect size,
$\eta^{2}$ = 0.08. In addition, we discovered a statistically significant
difference (p $<$ .001) in cadence, step length, stride length, step time,
cycle time, and swing time. Then we did post-hoc paired t-tests for within-
group and two-tailed t-tests for between-group comparisons to identify
differences between specific research conditions.

###  7.1 Participants With MI: Within-Group Comparisons

####  7.1.1 Non-VR Baseline vs. VR Baseline

Walking velocity was significantly lower in the VR baseline without
vibrotactile feedback condition (Mean, M = 96.69, Standard Deviation, SD = 13)
compared to non-VR baseline without vibrotactile feedback condition (M =
128.25, SD = 10.84); t(17) = 8.19, p $<$ .001; and effect size, Cohen’s d =
0.11. We also found a significant reduction (p $<$ .001) in cadence, step
length, and stride length for the VR baseline without vibrotactile feedback
condition. Step time, cycle time, and swing time were significantly enhanced
(p $<$ .001) in VR baseline without vibrotactile feedback condition than non-
VR baseline without vibrotactile feedback condition. These findings suggested
that participants with MI experienced gait disturbances in VR environments.

####  7.1.2 Spatial Vibrotactiile vs. VR Baseline

Walking velocity enhanced significantly in the spatial vibrotactile feedback
condition (M = 127.35, SD = 15.03) compared to VR baseline without
vibrotactile feedback condition (M = 96.69, SD = 13); t(17) = 5.74, p $<$
.001, d = 0.76. We found a significant increase (p $<$ .001) in cadence, step
length, and stride length for spatial vibrotactile feedback condition compared
to VR baseline without vibrotactile feedback condition. Results also
demonstrated a significant reduction in step time, cycle time, and swing time
(p $<$ .001) in spatial vibrotactile feedback condition compared to VR
baseline without vibrotactile feedback condition. The findings demonstrated
that spatial vibrotactile feedback enhanced gait metrics compared to the VR
baseline without vibrotactile feedback condition.

Figure 5:  A comparison of walking velocity between study conditions for
participants with MI.

####  7.1.3 Spatial Vibrotactile vs. Static Vibrotactile

Experimental results revealed that walking velocity was increased in spatial
vibrotactile feedback condition (M = 127.35, SD = 15.03) relative to static
vibrotactile feedback condition (M = 114.4, SD = 12.6); t(17) = 4.71, p $<$
.001, d = 0.27. We discovered a statistically significant increase (p $<$
.001) in cadence, step length, and stride length for spatial vibrotactile
feedback condition than static vibrotactile feedback condition. Additionally,
we noticed that step time, cycle time, and swing time were significantly
reduced (p $<$ .001) in the spatial vibrotactile feedback condition compared
to the the static vibrotactile condition. Therefore, spatial vibrotactile
feedback condition exhibited greater gait performance compared to the static
vibrotactile condition.

####  7.1.4 Spatial Vibrotactile vs. Rhythmic Vibrotactile

We noticed a significant increase in walking velocity in spatial (M = 127.35,
SD = 15.03) compared to rhythmic vibrotactile feedback (M = 113.45, SD =
13.68); t(17) = 4.1, p $<$ .001, d = 0.36. Also, cadence, step length, and
stride length for spatial vibrotactile feedback increased significantly (p $<$
.001) than the rhythmic vibrotactile feedback. However, we found a significant
reduction (p $<$ .001) in step time, cycle time, and swing time in spatial
compared to rhythmic vibrotactile feedback. The findings showed that spatial
vibrotactile feedback might be more beneficial for gait performance than
rhythmic vibrotactile feedback.

####  7.1.5 Static Vibrotactile vs. VR Baseline

Our results indicated that walking velocity was significantly increased in
static vibrotactile (M = 114.4, SD = 12.6) compared to VR baseline without
vibrotactile feedback (M = 96.69, SD = 13); t(17) = 4.84, p $<$ .001, d =
0.58. We also found a significant increase (p $<$ .001) in cadence, step
length, and stride length in static than VR baseline without vibrotactile
feedback. However, we also noticed a significant reduction (p $<$ .001) in
step time, cycle time, and swing time in static than VR baseline without
vibrotactile feedback. Thus, static vibrotactile feedback outperformed VR
baseline without vibrotactile feedback.

####  7.1.6 Static Vibrotactile vs. Rhythmic Vibrotactile

We did not find a significant difference in walking velocity between static (M
= 114.4, SD = 12.6) and rhythmic vibrotactile feedback (M = 113.45, SD =
13.68); t(17) = 0.35, p $=$ .73, d = 0.12 after post-hoc two-tailed paired
t-test. We also noticed no significant difference for other gait parameters
between static and rhythmic vibrotactile feedback condition. Therefore, study
was equivocal as to whether rhythmic or static vibrotactile input is more
efficient for improving gait performance.

TABLE II: Gait metrics in five conditions for participants with MI | Gait
---
Metrics
| Non-VR
---
baseline
Mean
(SD)
| VR
---
baseline
Mean
(SD)
| Spatial
---
Mean
(SD)
| Rhythmic
---
Mean
(SD)
| Static
---
Mean
(SD)
Cadence |  | 113.67
---
(9.84)
| 92.14
---
(8.56)
| 111.33
---
(6.17)
| 103.65
---
(5.03)
| 102.36
---
(7.89)
| Step
---
Length
| 69.46
---
(8.01)
| 48.11
---
(6.62)
| 66.02
---
(7.23)
| 55.19
---
(9.04)
| 53.86
---
(5.94)
| Stride
---
Length
| 78.93
---
(11.19)
| 69.38
---
(8.04)
| 78.11
---
(9.75)
| 68.77
---
(9.06)
| 70.01
---
(11.23)
| Step
---
Time
| 1.03
---
(0.56)
| 1.07
---
(0.42)
| 0.91
---
(0.51)
| 0.99
---
(0.37)
| 0.98
---
(0.29)
| Cycle
---
Time
| 1.87
---
(0.94)
| 1.99
---
(0.91)
| 1.81
---
(0.77)
| 1.91
---
(0.65)
| 1.89
---
(0.82)
| Swing
---
Time
| 0.39
---
(0.07)
| 0.48
---
(0.09)
| 0.36
---
(0.05)
| 0.42
---
(0.06)
| 0.41
---
(0.05)

####  7.1.7 Rhythmic Vibrotactile vs. VR Baseline

We obtained a significant increase in rhythmic (M = 113.45, SD = 13.68)
compared to VR baseline without vibrotactile feedback (M = 96.69, SD = 13);
t(17) = 3.81, p $<$ .001, d = 0.38. We also observed a significant increase (p
$<$ .001) in cadence, step length, and stride length in rhythmic than VR
baseline without vibrotactile feedback condition. However, results indicated a
significant reduction (p $<$ .001) in step time, cycle time, and swing time in
rhythmic compared to VR baseline without vibrotactile feedback condition.
Consequently, rhythmic vibrotactile feedback exceeded the VR baseline
condition in terms of gait performance.

Fig. 5 depicts the comparisons of walking velocity across five distinct study
conditions for the MI group. Table 2 displays the relative mean and standard
deviation (SD) for the five research conditions for the remaining gait metrics
that showed a significant improvement.

###  7.2 Participants Without MI: Within-Group Comparisons

####  7.2.1 Non-VR Baseline vs. VR Baseline

Walking velocity was significantly lower in VR baseline without vibrotactile
feedback (M = 128.22, SD = 5.58) than the non-VR baseline without vibrotactile
feedback condition (M = 144.2, SD = 8.25); t(20) = 10.08, p $<$ .001, d =
0.14. We also noticed that cadence, step length, and stride length for non-VR
baseline were significantly increased (p $<$ .001) than VR baseline without
vibrotactile feedback condition. However, we observed a significant reduction
(p $<$ .001) in step time, cycle time, and swing time in non-VR baseline
compared to VR baseline without vibrotactile feedback condition. The findings
demonstrated that participants without MI experienced gait disturbances in VR.

TABLE III: Gait metrics in five conditions for participants without MI | Gait
---
Metrics
| Non-VR
---
baseline
Mean
(SD)
| VR
---
baseline
Mean
(SD)
| Spatial
---
Mean
(SD)
| Rhythmic
---
Mean
(SD)
| Static
---
Mean
(SD)
Cadence |  | 128.37
---
(7.18)
| 106.63
---
(4.33)
| 126.52
---
(9.47)
| 117.89
---
(6.51)
| 118.44
---
(12.35)
| Step
---
Length
| 77.28
---
(7.19)
| 54.34
---
(5.09)
| 75.01
---
(7.1)
| 66.87
---
(8.12)
| 65.42
---
(6.88)
| Stride
---
Length
| 90.78
---
(8.11)
| 71.46
---
(6.89)
| 89.14
---
(7.45)
| 81.57
---
(5.78)
| 82.84
---
(6.87)
| Step
---
Time
| 1.14
---
(0.09)
| 1.31
---
(0.4)
| 1.11
---
(0.1)
| 1.19
---
(0.08)
| 1.18
---
(0.05)
| Cycle
---
Time
| 1.68
---
(0.31)
| 1.99
---
(0.16)
| 1.76
---
(0.19)
| 1.88
---
(0.15)
| 1.86
---
(0.27)
| Swing
---
Time
| 0.34
---
(0.09)
| 0.41
---
(0.1)
| 0.33
---
(0.06)
| 0.37
---
(0.07)
| 0.36
---
(0.04)

####  7.2.2 Spatial Vibrotactile vs. VR Baseline

Experimental results indicated a significant increase in walking velocity in
the spatial (M = 142.89, SD = 11.39) compared to VR baseline without
vibrotactile feedback condition (M = 128.22, SD = 5.58); t(20) = 6.87, p $<$
.001, d = 0.75. Likewise, results also revealed that cadence, step length, and
stride length for spatial vibrotactile condition increased significantly (p
$<$ .001). However, we noticed that step time, cycle time, and swing time were
significantly decreased (p $<$ .001) in spatial compared to VR baseline
without vibrotactile feedback condition. Therefore, spatial vibrotactile
feedback produced superior outcomes than the VR baseline condition without
vibrotactile feedback.

####  7.2.3 Spatial Vibrotactile vs. Static Vibrotactile

We observed that walking velocity was significantly higher in spatial (M =
142.89, SD = 11.39) compared to static vibrotactile feedback condition (M =
134.62, SD = 7.65); t(20) = 3.53, p $<$ .001, d = 0.38. Additionally, we found
a significant increase (p $<$ .001) in cadence, step length, and stride length
for spatial compared to static vibrotactile feedback condition. However, there
was a significant reduction (p $<$ .001) in step time, cycle time and swing
time in the spatial vibrotactile feedback condition. The results suggested
that spatial vibrotactile feedback may be more efficacious than static
vibrotactile feedback for gait performance.

####  7.2.4 Spatial Vibrotactile vs. Rhythmic Vibrotactile

Experimental results showed a significant increase in walking velocity in the
spatial (M = 142.89, SD = 11.39) compared to the rhythmic vibrotactile
feedback condition (M = 134.57, SD = 7.19); t(20) = 3.36, p $<$ .001, d = 0.6.
Likewise, cadence, step length, and stride length for spatial condition
significantly incremented (p $<$ .001) than rhythmic vibrotactile feedback
condition. However, we observed significant reduction (p $<$ .001) in step
time, cycle time, and swing time in spatial compared to rhythmic vibrotactile
feedback condition. As a result, spatial vibrotactile feedback could be
favored more than the rhythmic vibrotactile feedback for gait improvement in
VR environments.

####  7.2.5 Static Vibrotactile vs. VR Baseline

Experimental results revealed that walking velocity in static (M = 134.62, SD
= 7.65) was significantly higher compared to VR baseline without vibrotactile
feedback condition (M = 128.22, SD = 5.58); t(20) = 2.97, p $<$ .001, d = 0.3.
Likewise, significant increase (p $<$ .001) were observed in cadence, step
length, and stride length for static vibrotactile feedback than VR baseline
without vibrotactile feedback condition. However, we found a significant
decline in step time, cycle time, and swing time in static vibrotactile
feedback condition (p $<$ .001) compared to VR baseline without vibrotactile
feedback. Thus, static vibrotactile feedback surpassed the no vibrotactile
feedback in VR.

####  7.2.6 Static Vibrotactile vs. Rhythmic Vibrotactile

We found no significant difference in walking velocity between static (M =
84.76, SD = 13.78) and rhythmic vibrotactile feedback condition (M = 82.66, SD
= 15.69); t(20) = 1.11, p $=$ .138, d = 0.14. Likewise, there was no
significant difference for the other gait metrics between static and rhythmic
vibrotactile feedback condition. Hence, it was uncertain whether rhythmic or
static vibrotactile input would be more effective for gait improvement.

####  7.2.7 Rhythmic vibrotactile vs. VR Baseline

Walking velocity was significantly increased in rhythmic (M = 134.57, SD =
7.19) than VR baseline without vibrotactile feedback condition (M = 128.22, SD
= 5.58); t(20) = 3.63, p $<$ .001, d = 0.25. However, step time, cycle time,
and swing time significantly decreased (p $<$ .001) in rhythmic vibrotactile
feedback compared to VR baseline. Also, significant increment (p $<$ .001) in
cadence, step length, and stride length was observed for rhythmic condition
compared to VR baseline without vibrotactile feedback condition. This analysis
revealed that rhythmic vibrotactile feedback might be more effective for
improving gait in virtual reality environments than the absence of
vibrotactile feedback in VR.

Figure 6: Walking velocity comparison between study conditions for
participants without MI.

Fig. 6 compares the walking velocity of participants without MI in five
different study conditions. Table 3 contains the relative means and standard
deviations (SD) for the other six gait metrics that exhibited a statistically
significant improvement. Fig. 7 shows comparisons of effect sizes (Cohen’s d)
for walking velocity across various study conditions.

Figure 7: Comparisons of effect size for walking velocity between study
conditions for participants with and without MI.

###  7.3 Between-Group Comparisons

The mixed-ANOVA and post-hoc two-tailed t-tests revealed that the walking
velocity for participants with MI was significantly lower than that of
participants without MI for non-VR baseline condition; t(38) = 4.59, p $=$
.003; d = 1.06, and for VR baseline condition; t(38) = 2.88, p $=$ .002; d =
1.15. Furthermore, we found that walking velocity was significantly lower for
participants with MI than participants without MI for all VR-based
vibrotactile feedback conditions: spatial vibrotactile (t(38) = 1.21, p $=$
.01; d = 0.92), static vibrotactile (t(38) = 1.93, p $=$ .008; d = 0.8), and
for rhythmic vibrotactile feedback condition (t(38) = 1.14, p $=$ .02; d =
0.77). For all conditions (non-VR baseline, VR baseline, spatial, static, and
rhythmic vibrotactile), participants with MI demonstrated a significant
decrease (p $<$ .05) in cadence, step length, and stride length compared to
participants without MI.

###  7.4 Activities-specific Balance Confidence (ABC) Scale

Two-tailed t-test was performed on the response scores from the Activities-
specific Balance Confidence (ABC) Scale between participants with MI ($M$ =
72.64, $SD$ = 19.33) and participants without MI ($M$ = 93.35, $SD$ = 8.06),
t(38) = 6.19, p $<$ .001, d = 1.01. The computed mean score on the ABC Scale
for participants with MI was 72.64%, indicating a moderate degree of
functioning. However, the computed mean ABC score for those without MI was
91.76% which demonstrated a high level of functioning. These ratings indicated
a significant difference between those with and without MI in terms of
physical functioning.

###  7.5 Simulator Sickness Questionnaire

For all participants, we performed a two-tailed t-test between pre-study SSQ
scores and post-study SSQ scores. We did not notice a statistically
significant rise in SSQ scores among participants with and without MI. We
found t(17) = 1.71, p = .07, d = 0.2 for participants with MI, while t(20) =
1.58, p = .09, d = 0.1 for participants without MI.

##  8 DISCUSSION

###  8.1 Gait Disturbances in VR Without Vibrotactile Feedback

Mixed ANOVA and post-hoc t-tests for both participant groups revealed that
walking velocity, step length, stride length, cadence, step time, cycle time,
and swing time were significantly changed (p $<$ .001) in the VR baseline
without vibrotactile condition relative to the non-VR baseline without
vibrotactile condition. Therefore, we observed that gait disturbances occurred
for all individuals in the absence of vibrotactile feedback, supporting our
hypothesis H1. Previous research has shown that VR may produce postural
instability, which may result in gait disturbances [17, 18, 19].

###  8.2 Gait Improvement in VR-based Vibrotactile Feedback Conditions

For both participants with and without MI, results revealed that all VR-based
vibrotactile conditions increased gait performance significantly (p $<$ .001)
compared to VR baseline condition, which supported our hypothesis H2. In
addition, effect size (Cohen’s d = 0.5) demonstrated that Spatial vibrotactile
feedback had a medium effect on both participants with and without MI. The
static vibrotactile had a medium effect (Cohen’s d = 0.5) on participants with
MI but a small effect (Cohen’s d = 0.2) on those without MI. Rhythmic audio
had a small effect (Cohen’s d = 0.2) on both participant groups.

Spatial vibrotactile feedback performed significantly better (p $<$ .001) than
other VR-based vibrotactile feedback conditions, supporting our hypothesis H3.
Additionally, spatial vibrotactile feedback exhibited a larger effect size
than other conditions (Fig. 7). In prior research, simulated spatial
vibrotactile feedback was shown to be useful in the real world for improving
gait and postural stability due to its superior quality [48]. However, the
majority of prior research was conducted in non-VR environments, while we
evaluated the influence of vibrotactile feedback in VR. Also, earlier studies
only examined spatial vibrotactile or only a specific form of vibrotactile
feedback, while this research compared three distinct types of vibrotactile
feedback in VR.

###  8.3 Gait Similarities and Dissimilarities Between Participants With and
Without MI

All five research conditions revealed significant variations in walking
velocity, cadence, step length, and stride length between people with and
without MI. Other gait metrics, however, did not change significantly between
people with and without MI. Thus, our hypothesis H4 was supported by the fact
that some gait metrics (e.g., velocity, cadence, step length, stride length)
were altered differently between individuals with and without MI, while some
other gait metrics were influenced similarly for both groups of participants.
These results were somewhat consistent with a prior study by Guo et al. [33]
where they also compared the gait metrics of people with and without MI in a
VE. They reported walking velocity, step length, and stride length were
significantly different between people with and without MI, although there
were no significant variations in other gait metrics between the two groups.
However, they did not investigate the effect of vibrotactile feedback.

To determine which group had greater gait improvement in response to the
vibrotactile feedback conditions, we first subtracted baseline data from each
condition. Then, ANOVA and post hoc two-tailed t-tests indicated that the gait
improvement of individuals with MI was substantially greater (p $<$ .001) than
that of those without MI. Impact size, as measured by Cohen’s d = 0.9, also
revealed that people with MI had a greater effect. We anticipated that since
people with MI had less gait functioning than those without MI, they could be
more amenable to improvement.

###  8.4 Cybersickness

Previous studies found that VR users exposed to virtual worlds for more than
10 minutes might suffer the development of cybersickness [65, 66].
Participants in our research wore the HMD for around 45 minutes under four
conditions, which raised their likelihood of getting cybersickness symptoms.
However, we built the virtual environment without the illusion of self-motion
to reduce the likelihood of participants experiencing cybersickness [67], and
there were resting periods between conditions. Therefore, there was no
statistically significant difference between pre-SSQ and post-SSQ ratings.
Although we observed that many individuals suffered minor cybersickness after
the trials, this did not seem to significantly affect gait.

##  9 Limitations

The participants were informed of the whole research protocol at the outset of
the trials. Then we conducted a few trials with them until they were used to
the experimental procedure. Before beginning the vibrotactile feedback in VR,
we also conducted three baseline trials. However, the baseline might be
expanded to more trials. We did not do this since the trials took around an
hour to complete and included individuals with MI due to MS who had reduced
physical functioning. However, we counterbalanced the feedback conditions in
VR to decrease the learning effects.

In our research, the feedback conditions were administered in a
counterbalanced sequence, which reduced carryover or practice effects
(learning due to repetition) [68, 69, 70]. Counterbalancing order was also
found to be successful in a number of previous studies [71, 72, 73].
Alternately, we may have administered the vibrotactile feedback conditions in
a random sequence to minimize bias. Nevertheless, our research included MS
patients who are very prone to fatigue and cybersickness. Consequently, we
were particularly worried about the impact of carryover fatigue and
cybersickness on performance. Since counterbalancing significantly minimizes
fatigue and cybersickness effects [70], we preferred counterbalancing over
randomization.

Participants wore harnesses throughout the research to prevent falls, which
may have improved their gait performance somewhat. To ensure the consistency
and safety of the research protocol, we asked all participants to wear
harnesses for all baseline and VR trials, regardless of whether they had the
risk of falling or not. Consequently, investigations examining gait without a
harness may provide slightly different findings.

The duration of the research was long, and each participant had to complete 15
time-consuming GAITRite trials. We discovered that the duration and physical
demands of the research might sometimes induce tiredness in participants. To
reduce tiredness in our research, individuals were permitted to remove their
HMD and relax between trials and conditions. This respite and removal of the
HMD may have enabled them to reestablish spatial orientation inside the room,
resulting in somewhat biased data.

The vibrating motors used for vibrotactile feedback created noise. So it’s
hard to say if the sounds it made contributed to the gait improvement, if it
was the actual vibration, or both. More research will be needed to verify
this.

The ”rhythmic” vibrotactile feedback was provided at one-second intervals. We
did not examine this feedback condition for different time periods (e.g., two-
second). Therefore, studies that provide ”rhythmic” vibrotactile input with
varying time intervals may produce slightly different findings for this
particular circumstance.

For the static vibrotactile feedback, participants may have experienced
fatigue as the vibration was continually played. However, the fatigue impact
for this circumstance was not measured.

In our research, the non-VR baseline was always performed first, which may
have had an effect on the walking speed for this condition. However, we wanted
to limit the learning impact of VR circumstances by introducing sufficient
baseline tasks prior to VR conditions.

In our study, more females than males participated in the MI group
participants. This is due to the fact that we recruited from the MS community,
which is statistically more prevalent in females [6]. Numerous prior studies
have shown no significant gender impact on balance [74, 75, 76]. In our future
studies, we want to examine the gender influence on gait metrics in VR.

During the VR intervention, we assessed gait performance. We did not assess
post-study gait effects. Our objective was accessibility rather than
rehabilitation. Therefore we only examined gait outside of VR as a baseline
and during VR immersion.

There were five distinct research conditions. We conducted three trials for
each research condition, resulting in a total of 15 trials per participant,
and we gathered different data files for each trial. We conducted a total of
585 trials with our 39 participants. The HMD display failed during three
trials involving three individuals (MI group:2, without MI group: 1).
Restarting the ”Vive wireless app” always resolved the problem. We repeated
the three trials and omitted the three flawed data files.

Due to COVID-19 and our intended test group, which included individuals with
MI caused by MS, the recruiting procedure was challenging. Because many
prospective volunteers had impaired immune systems, which placed them at
significant risk for COVID-19. Consequently, they excluded themselves from the
research. We could have recruited more participants if the research had been
conducted outside of COVID-19.

##  10 Conclusion and future work

In this study, we assessed the effects of several vibrotactile feedback
modalities (spatial, static, and rhythmic) on gait in VR. In our research, all
vibrotactile feedback conditions substantially improved gait in VR. Spatial
vibrotactile feedback outperformed rhythmic and static vibrotactile
significantly. There was no statistically significant difference between
rhythmic and static vibrotactile feedback. Researchers will be better able to
comprehend the various types of vibrotactile input for improving gait in an
HMD-based VE as a consequence of these findings. In addition, this study may
assist developers in creating VR experiences that are more accessible and
useful for those with and without mobility issues. Future research will
include evaluating the efficacy of other feedback modalities to make real
walking in VR more accessible.

## Acknowledgments

The authors would like to express their gratitude to everyone who participated
in this study. This project was funded by a grant from the National Science
Foundation (IIS 2007041).

## References

  * [1] Y. Agrawal, J. P. Carey, C. C. Della Santina, M. C. Schubert, and L. B. Minor, “Disorders of balance and vestibular function in us adults: data from the national health and nutrition examination survey, 2001-2004,” _Archives of internal medicine_ , vol. 169, no. 10, pp. 938–944, 2009.
  * [2] S. M. S. Ferdous, I. M. Arafat, and J. Quarles, “Visual feedback to improve the accessibility of head-mounted displays for persons with balance impairments,” in _2016 IEEE Symposium on 3D User Interfaces (3DUI)_. IEEE, 2016, pp. 121–128.
  * [3] S. M. S. Ferdous, T. I. Chowdhury, I. M. Arafat, and J. Quarles, “Investigating the reason for increased postural instability in virtual reality for persons with balance impairments,” in _Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology_ , 2018, pp. 1–7.
  * [4] R. Guo, G. Samaraweera, and J. Quarles, “The effects of ves on mobility impaired users: Presence, gait, and physiological response,” in _Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology_ , 2013, pp. 59–68.
  * [5] G. Samaraweera, R. Guo, and J. Quarles, “Latency and avatars in virtual environments and the effects on gait for persons with mobility impairments,” in _2013 IEEE Symposium on 3D User Interfaces (3DUI)_. IEEE, 2013, pp. 23–30.
  * [6] “[n. d.] people with disabilities in the world,” https://www.un.org/development/desa/disabilities/resources/factsheet-on-persons-with-disabilities.html, accessed: 2021-08-30.
  * [7] C. Franco, A. Fleury, P.-Y. Guméry, B. Diot, J. Demongeot, and N. Vuillerme, “ibalance-abf: a smartphone-based audio-biofeedback balance system,” _IEEE transactions on biomedical engineering_ , vol. 60, no. 1, pp. 211–215, 2012.
  * [8] K. Sienko, S. Whitney, W. Carender, and C. Wall III, “The role of sensory augmentation for people with vestibular deficits: real-time balance aid and/or rehabilitation device?” _Journal of Vestibular Research_ , vol. 27, no. 1, pp. 63–76, 2017.
  * [9] R. Velázquez, “Wearable assistive devices for the blind,” in _Wearable and autonomous biomedical devices and systems for smart environment_. Springer, 2010, pp. 331–349.
  * [10] H. Thikey, F. van Wjick, M. Grealy, and P. Rowe, “A need for meaningful visual feedback of lower extremity function after stroke,” in _2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops_. IEEE, 2011, pp. 379–383.
  * [11] O. Čakrt, M. Chovanec, T. Funda, P. Kalitová, J. Betka, E. Zvěřina, P. Kolář, and J. Jeřábek, “Exercise with visual feedback improves postural stability after vestibular schwannoma surgery,” _European archives of oto-rhino-laryngology_ , vol. 267, no. 9, pp. 1355–1360, 2010.
  * [12] S. Sütbeyaz, G. Yavuzer, N. Sezer, and B. F. Koseoglu, “Mirror therapy enhances lower-extremity motor recovery and motor functioning after stroke: a randomized controlled trial,” _Archives of physical medicine and rehabilitation_ , vol. 88, no. 5, pp. 555–559, 2007.
  * [13] J. K. Gibbs, M. Gillies, and X. Pan, “A comparison of the effects of haptic and visual feedback on presence in virtual reality,” _International Journal of Human-Computer Studies_ , vol. 157, p. 102717, 2022.
  * [14] P. Soltani and R. Andrade, “The influence of virtual reality head-mounted displays on balance outcomes and training paradigms: A systematic review.” _Frontiers in sports and active living_ , vol. 2, p. 233, 2020.
  * [15] A. Martinez, A. I. Paganelli, and A. Raposo, “Analysing balance loss in vr interaction with hmds,” _Journal on Interactive Systems_ , vol. 9, no. 2, 2018.
  * [16] A. Murata, “Effects of duration of immersion in a virtual reality environment on postural stability,” _International Journal of Human-Computer Interaction_ , vol. 17, no. 4, pp. 463–477, 2004.
  * [17] J. H. Hollman, R. H. Brey, T. J. Bang, and K. R. Kaufman, “Does walking in a virtual environment induce unstable gait?: An examination of vertical ground reaction forces,” _Gait & posture_, vol. 26, no. 2, pp. 289–294, 2007\.
  * [18] L. I. Riem, B. D. Schmit, and S. A. Beardsley, “The effect of discrete visual perturbations on balance control during gait,” in _2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)_. IEEE, 2020, pp. 3162–3165.
  * [19] B. Sondell, L. Nyberg, S. Eriksson, B. Engström, A. Backman, K. Holmlund, G. Bucht, and L. Lundin-Olsson, “Altered walking pattern in a virtual environment,” _Presence_ , vol. 14, no. 2, pp. 191–197, 2005.
  * [20] B. Horsak, M. Simonlehner, L. Schöffer, B. Dumphart, A. Jalaeefar, and M. Husinsky, “Overground walking in a fully immersive virtual reality: A comprehensive study on the effects on full-body walking biomechanics,” _Frontiers in bioengineering and biotechnology_ , vol. 9, 2021.
  * [21] A. Canessa, P. Casu, F. Solari, and M. Chessa, “Comparing real walking in immersive virtual reality and in physical world using gait analysis.” in _VISIGRAPP (2: HUCAPP)_ , 2019, pp. 121–128.
  * [22] D. Martelli, B. Xia, A. Prado, and S. K. Agrawal, “Gait adaptations during overground walking and multidirectional oscillations of the visual field in a virtual reality headset,” _Gait & posture_, vol. 67, pp. 251–256, 2019\.
  * [23] I. J. De Rooij, I. G. Van De Port, and J.-W. G. Meijer, “Effect of virtual reality training on balance and gait ability in patients with stroke: systematic review and meta-analysis,” _Physical therapy_ , vol. 96, no. 12, pp. 1905–1918, 2016.
  * [24] D. Meldrum, S. Herdman, R. Moloney, D. Murray, D. Duffy, K. Malone, H. French, S. Hone, R. Conroy, and R. McConn-Walsh, “Effectiveness of conventional versus virtual reality based vestibular rehabilitation in the treatment of dizziness, gait and balance impairment in adults with unilateral peripheral vestibular loss: a randomised controlled trial,” _BMC Ear, Nose and Throat Disorders_ , vol. 12, no. 1, pp. 1–8, 2012.
  * [25] E.-C. Park, S.-G. Kim, and C.-W. Lee, “The effects of virtual reality game exercise on balance and gait of the elderly,” _Journal of physical therapy science_ , vol. 27, no. 4, pp. 1157–1159, 2015.
  * [26] C. Cho, W. Hwang, S. Hwang, and Y. Chung, “Treadmill training with virtual reality improves gait, balance, and muscle strength in children with cerebral palsy,” _The Tohoku journal of experimental medicine_ , vol. 238, no. 3, pp. 213–218, 2016.
  * [27] G. Duque, D. Boersma, G. Loza-Diaz, S. Hassan, H. Suarez, D. Geisinger, P. Suriyaarachchi, A. Sharma, and O. Demontiero, “Effects of balance training using a virtual-reality system in older fallers,” _Clinical interventions in aging_ , vol. 8, p. 257, 2013.
  * [28] M. Bergeron, C. L. Lortie, and M. J. Guitton, “Use of virtual reality tools for vestibular disorders rehabilitation: a comprehensive analysis,” _Advances in medicine_ , vol. 2015, 2015.
  * [29] M. L. Walker, S. I. Ringleb, G. C. Maihafer, R. Walker, J. R. Crouch, B. Van Lunen, and S. Morrison, “Virtual reality–enhanced partial body weight–supported treadmill training poststroke: feasibility and effectiveness in 6 subjects,” _Archives of physical medicine and rehabilitation_ , vol. 91, no. 1, pp. 115–122, 2010.
  * [30] O. Janeh, O. Fründt, B. Schönwald, A. Gulberti, C. Buhmann, C. Gerloff, F. Steinicke, and M. Pötter-Nerger, “Gait training in virtual reality: short-term effects of different virtual manipulation techniques in parkinson’s disease,” _Cells_ , vol. 8, no. 5, p. 419, 2019.
  * [31] O. Janeh and F. Steinicke, “A review of the potential of virtual walking techniques for gait rehabilitation,” _Frontiers in Human Neuroscience_ , vol. 15, 2021.
  * [32] C. Winter, F. Kern, D. Gall, M. E. Latoschik, P. Pauli, and I. Käthner, “Immersive virtual reality during gait rehabilitation increases walking speed and motivation: a usability evaluation with healthy participants and patients with multiple sclerosis and stroke,” _Journal of neuroengineering and rehabilitation_ , vol. 18, no. 1, pp. 1–14, 2021.
  * [33] R. Guo, G. Samaraweera, and J. Quarles, “Mobility impaired users respond differently than healthy users in virtual environments,” _Computer Animation and Virtual Worlds_ , vol. 26, no. 5, pp. 509–526, 2015.
  * [34] A. Lott, E. Bisson, Y. Lajoie, J. McComas, and H. Sveistrup, “The effect of two types of virtual reality on voluntary center of pressure displacement,” _Cyberpsychology & behavior_, vol. 6, no. 5, pp. 477–485, 2003.
  * [35] P. Epure, C. Gheorghe, T. Nissen, L.-O. Toader, A. Nicolae, S. S. Nielsen, D. J. R. Christensen, A. L. Brooks, and E. Petersson, “Effect of the oculus rift head mounted display on postural stability,” in _The 10th International Conference on Disability Virtual Reality & Associated Technologies: Proceedings_. Reading University Press, 2014, pp. 119–127.
  * [36] M. T. Robert, L. Ballaz, and M. Lemay, “The effect of viewing a virtual environment through a head-mounted display on balance,” _Gait & posture_, vol. 48, pp. 261–266, 2016.
  * [37] C. G. Horlings, M. G. Carpenter, U. M. Küng, F. Honegger, B. Wiederhold, and J. H. Allum, “Influence of virtual reality on postural stability during movements of quiet stance,” _Neuroscience letters_ , vol. 451, no. 3, pp. 227–231, 2009.
  * [38] G. Samaraweera, A. Perdomo, and J. Quarles, “Applying latency to half of a self-avatar’s body to change real walking patterns,” in _2015 IEEE Virtual Reality (VR)_. IEEE, 2015, pp. 89–96.
  * [39] A. U. Alahakone and S. A. Senanayake, “A real-time system with assistive feedback for postural control in rehabilitation,” _IEEE/ASME Transactions on Mechatronics_ , vol. 15, no. 2, pp. 226–233, 2010.
  * [40] L. Chiari, M. Dozza, A. Cappello, F. B. Horak, V. Macellari, and D. Giansanti, “Audio-biofeedback for balance improvement: an accelerometry-based system,” _IEEE transactions on biomedical engineering_ , vol. 52, no. 12, pp. 2108–2111, 2005.
  * [41] K. H. Sienko, M. D. Balkwill, and C. Wall, “Biofeedback improves postural control recovery from multi-axis discrete perturbations,” _Journal of neuroengineering and rehabilitation_ , vol. 9, no. 1, pp. 1–11, 2012.
  * [42] M. Henry and S. Baudry, “Age-related changes in leg proprioception: implications for postural control,” _Journal of neurophysiology_ , vol. 122, no. 2, pp. 525–538, 2019.
  * [43] G. T. Wannstedt and R. M. Herman, “Use of augmented sensory feedback to achieve symmetrical standing,” _Physical Therapy_ , vol. 58, no. 5, pp. 553–559, 1978.
  * [44] K. H. Sienko, R. D. Seidler, W. J. Carender, A. D. Goodworth, S. L. Whitney, and R. J. Peterka, “Potential mechanisms of sensory augmentation systems on human balance control,” _Frontiers in neurology_ , vol. 9, p. 944, 2018.
  * [45] A. D. Goodworth, C. Wall III, and R. J. Peterka, “Influence of feedback parameters on performance of a vibrotactile balance prosthesis,” _IEEE Transactions on Neural Systems and Rehabilitation Engineering_ , vol. 17, no. 4, pp. 397–408, 2009.
  * [46] C. Wall III, D. M. Wrisley, and K. D. Statler, “Vibrotactile tilt feedback improves dynamic gait index: a fall risk indicator in older adults,” _Gait & posture_, vol. 30, no. 1, pp. 16–21, 2009.
  * [47] H. Rust, N. Lutz, V. Zumbrunnen, M. Imhof, Ö. Yaldizli, V. Haller, and J. H. Allum, “Benefits of short-term training with vibrotactile biofeedback of trunk sway on balance control in multiple sclerosis,” _Physical Medicine and Rehabilitation Research_ , vol. 5, no. 1, pp. 1–10, 2020.
  * [48] G. Ballardini, V. Florio, A. Canessa, G. Carlini, P. Morasso, and M. Casadio, “Vibrotactile feedback for improving standing balance,” _Frontiers in bioengineering and biotechnology_ , vol. 8, p. 94, 2020.
  * [49] H. Kingma, L. Felipe, M.-C. Gerards, P. Gerits, N. Guinand, A. Perez-Fornos, V. Demkin, and R. Van De Berg, “Vibrotactile feedback improves balance and mobility in patients with severe bilateral vestibular loss,” _Journal of neurology_ , vol. 266, no. 1, pp. 19–26, 2019.
  * [50] M. R. Mahmud, M. Stewart, A. Cordova, and J. Quarles, “Auditory feedback for standing balance improvement in virtual reality,” in _2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)_. IEEE, 2022, pp. 782–791.
  * [51] M. N. Stevens, D. L. Barbour, M. P. Gronski, and T. E. Hullar, “Auditory contributions to maintaining balance,” _Journal of Vestibular Research_ , vol. 26, no. 5-6, pp. 433–438, 2016.
  * [52] L. Gandemer, G. Parseihian, R. Kronland-Martinet, and C. Bourdin, “Spatial cues provided by sound improve postural stabilization: evidence of a spatial auditory map?” _Frontiers in neuroscience_ , vol. 11, p. 357, 2017.
  * [53] J. Ross, O. Will, Z. McGann, and R. Balasubramaniam, “Auditory white noise reduces age-related fluctuations in balance,” _Neuroscience letters_ , vol. 630, pp. 216–221, 2016.
  * [54] T. Cornwell, J. Woodward, M. Wu, B. Jackson, P. Souza, J. Siegel, S. Dhar, K. E. Gordon _et al._ , “Walking with ears: altered auditory feedback impacts gait step length in older adults,” _Frontiers in Sports and Active Living_ , vol. 2, p. 38, 2020.
  * [55] S. Ghai, I. Ghai, and A. O. Effenberg, “Effect of rhythmic auditory cueing on aging gait: a systematic review and meta-analysis,” _Aging and disease_ , vol. 9, no. 5, p. 901, 2018.
  * [56] S. K. Helps, S. Bamford, E. J. Sonuga-Barke, and G. B. Söderlund, “Different effects of adding white noise on cognitive performance of sub-, normal and super-attentive school children,” _PloS one_ , vol. 9, no. 11, p. e112768, 2014.
  * [57] U. Chong and S. Alimardanov, “Audio augmented reality using unity for marine tourism,” in _International Conference on Intelligent Human Computer Interaction_. Springer, 2020, pp. 303–311.
  * [58] J. Pinkl and M. Cohen, “Spatialized ar polyrhythmic metronome using bose frames eyewear,” 2020.
  * [59] H. Kim, L. Remaggi, P. J. Jackson, and A. Hilton, “Immersive spatial audio reproduction for vr/ar using room acoustic modelling from 360 images,” in _2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)_. IEEE, 2019, pp. 120–126.
  * [60] L. McCormack, A. Politis, R. Gonzalez, T. Lokki, and V. Pulkki, “Parametric ambisonic encoding of arbitrary microphone arrays,” _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , 2022.
  * [61] L. E. Powell and A. M. Myers, “The activities-specific balance confidence (abc) scale,” _The Journals of Gerontology Series A: Biological Sciences and Medical Sciences_ , vol. 50, no. 1, pp. M28–M34, 1995.
  * [62] R. S. Kennedy, N. E. Lane, K. S. Berbaum, and M. G. Lilienthal, “Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness,” _The international journal of aviation psychology_ , vol. 3, no. 3, pp. 203–220, 1993.
  * [63] T. M. Steffen, T. A. Hacker, and L. Mollinger, “Age-and gender-related test performance in community-dwelling elderly people: Six-minute walk test, berg balance scale, timed up & go test, and gait speeds,” _Physical therapy_ , vol. 82, no. 2, pp. 128–137, 2002.
  * [64] “[GaitRite Manual] details gait parameters for gaitrite walkway system.” https://www.procarebv.nl/wp-content/uploads/2017/01/Technische-aspecten-GAITrite-Walkway-System.pdf, accessed: 2021-08-30.
  * [65] E. Chang, H. T. Kim, and B. Yoo, “Virtual reality sickness: a review of causes and measurements,” _International Journal of Human–Computer Interaction_ , vol. 36, no. 17, pp. 1658–1682, 2020.
  * [66] H. Kim, D. J. Kim, W. H. Chung, K.-A. Park, J. D. Kim, D. Kim, K. Kim, and H. J. Jeon, “Clinical predictors of cybersickness in virtual reality (vr) among highly stressed people,” _Scientific reports_ , vol. 11, no. 1, pp. 1–11, 2021.
  * [67] M. E. McCauley and T. J. Sharkey, “Cybersickness: Perception of self-motion in virtual environments,” _Presence: Teleoperators & Virtual Environments_, vol. 1, no. 3, pp. 311–318, 1992.
  * [68] “[Counterbalancing] one of the best ways to avoid pitfalls of repeated measure designs.” https://explorable.com/counterbalanced-measures-design#:~:text=Experiments%20conducted%20with%20a%20counterbalanced%20measures%20design%20are,subjects%20are%20exposed%20to%20all%20of%20the%20treatments., accessed: 2021-08-30.
  * [69] “[Counterbalancing] counterbalancing to reduce carryover effect.” https://opentext.wsu.edu/carriecuttler/chapter/experimental-design/, accessed: 2021-08-30.
  * [70] “[Counterbalancing] counterbalancing to reduce fatigue and practice effect.” https://psychology.illinoisstate.edu/jccutti/psych231/SP01/week8.html#:~:text=Advantages%3A%20The%20biggest%20advantage%20is%20that%20exposure%20to,levels%20of%20the%20IV%20Counterbalancing%20is%20not%20required, accessed: 2021-08-30.
  * [71] A. J. Millner, M. D. Lee, K. Hoyt, J. W. Buckholtz, R. P. Auerbach, and M. K. Nock, “Are suicide attempters more impulsive than suicide ideators?” _General hospital psychiatry_ , vol. 63, pp. 103–110, 2020.
  * [72] A. Plechatá, V. Sahula, D. Fayette, and I. Fajnerová, “Age-related differences with immersive and non-immersive virtual reality in memory assessment,” _Frontiers in psychology_ , p. 1330, 2019.
  * [73] M. Sheskin and F. Keil, “Thechildlab. com a video chat platform for developmental research,” 2018.
  * [74] B. O. Kahraman, T. Kahraman, O. Kalemci, and Y. S. Sengul, “Gender differences in postural control in people with nonspecific chronic low back pain,” _Gait & Posture_, vol. 64, pp. 147–151, 2018.
  * [75] A. Faraldo-García, S. Santos-Pérez, R. Crujeiras-Casais, T. Labella-Caballero, and A. Soto-Varela, “Influence of age and gender in the sensory analysis of balance control,” _European Archives of Oto-Rhino-Laryngology_ , vol. 269, no. 2, pp. 673–677, 2012.
  * [76] S. Schedler, R. Kiss, and T. Muehlbauer, “Age and sex differences in human balance performance from 6-18 years of age: a systematic review and meta-analysis,” _PLoS one_ , vol. 14, no. 4, p. e0214434, 2019.