Innovative Biofeedback Interface for Enhancing Stroke Gait Rehabilitation
Phase: NA
Conditions: Gait, Hemiplegic, Stroke
Sponsor: Emory University

Study Protocol 
 
TITLE: Innovative Biofeedback Interface for Enhancing Stroke Gait 
Rehabilitation 
 
NCT NUMBER: NCT04013971 
 
Protocol Date: September 5, 2023 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
1. Title
Innovative Biofeedback Interface for Enhancing Stroke Gait Rehabilitation
2. Precis/Abstract:
There is paucity of gait training approaches that target specific deficits, focus practice preferentially on 
the paretic leg, and capitalize on motor learning principles to optimize walking. Reduced paretic 
propulsion during terminal stance is an important post-stroke gait deficit that can negatively impact gait 
speed, inter-limb symmetry, and walking function. Propulsion can be measured using anterior ground 
reaction forces (AGRF) recorded from a force platform. We have developed an innovative real-time 
AGRF biofeedback gait training paradigm that targets propulsive deficits preferentially on the paretic 
leg. Exciting new data from our laboratory demonstrate that AGRF biofeedback training results in 
improved push-off, trailing limb angle, and step length from the paretic leg, without concomitant 
changes in the non-paretic leg. However, our current gait biofeedback interface is a simple, 2-
dimensional, non-engaging display. Our objective is to develop a more engaging, customizable, game-
based system specifically designed for post-stroke AGRF biofeedback gait training. Our project will 
address a major challenge for rehabilitation clinicians - to make gait training enjoyable and meaningful 
so that patients complete sufficient repetitions, intensity, and challenge to maximize therapeutic 
effectiveness. “Gamification” refers to use of video game elements in non-gaming systems (such as 
rehabilitation) to improve user engagement and shape user behavior, a goal that resonates strongly 
with rehabilitation clinicians and scientists.  Games provide interactive, real-time, experiential learning 
facilitated via a computer interface.  We propose to develop an AGRF biofeedback gait training game 
that increases patient motivation and engagement while also distracting participants from fatigue or 
boredom during training. Augmented reality (AR) game-interfaces add virtual characters and visual 
effects to real-world experiences, while reducing deleterious effects (disorientation, dizziness) often 
observed in fully-immersive virtual environments. The popular ‘Pokemon Go’ game (2016), is an 
example of AR. Transformative advancements in hardware, virtual and augmented reality, and mobile 
computing technologies have led to revolutionary new types of computer interfaces for recreational 
gaming. However, intuitive and engaging games designed specifically for gait retraining are not 
currently available in rehabilitation clinics.  The project specific aims are: (1) To design a visual effects 
gaming interface that streams in AGRF data as input variable; (2) Synchronize the gaming interface 
with AGRF data recorded during treadmill walking to develop a real-time AGRF biofeedback gait 
training game, including an augmented reality (AR) or virtual reality (VR) game version; (3) To obtain 
user data from stroke survivors and neuro-rehabilitation clinicians regarding our newly developed real-
time AGRF biofeedback gait training game. 
3. Introduction and Background:
Stroke is the leading cause of adult disability 1. Even after discharge from rehabilitation, 
residual gait deficits are prevalent in stroke survivors, leading to decreased walking speed and 
endurance 2-6. Because gait dysfunctions limit community mobility, stroke survivors and rehabilitation 
clinicians consider restoration of walking a major goal of rehabilitation 7-9. Biomechanical impairments, 
such as reduced knee and ankle flexion during swing phase negatively affect gait function and increase 
the risk for falls
4, 5. Decreased paretic propulsion during terminal stance is an important gait impairment 
that has received considerable attention, due to its relationships with swing phase knee flexion, 
hemiparetic severity, and gait speed 10-14. 
Several challenges and research gaps limit the effectiveness of current clinical gait 
rehabilitation practices. While there is consensus that stroke survivors benefit from gait rehabilitation 
15-23, agreement is lacking on which specific training interventions are most efficacious 
24-29. We believe 
that several challenges and research gaps contribute to this lack of consensus. First, there is a paucity 
of interventions customized to a stroke survivor’s gait deficits, as an alternative to the “one-size-fits-all” 

Investigator: Trisha Kesar 
  Draft Date: September 5, 2023
approach. Second, most gait training techniques provide concurrent stepping practice to both legs, 
likely encouraging compensations from the non-paretic leg. Very few interventions provide targeted 
practice of biomechanically appropriate movement patterns exclusively or preferentially to the paretic 
leg. Third, although thousands of steps of walking practice are needed to induce long-term 
improvements in post-stroke gait 
30-33, the number of steps of walking practice provided during clinical 
and research interventions is remarkably low34. Fourth, to enhance neuroplasticity and motor learning, 
there is a need to increase patient engagement, motivation, and salience during training
32, 33. Fifth, the 
progressively limited time for therapy during a clinical rehabilitation session requires the need to 
maximize the potential therapeutic effectiveness of each minute of out-patient rehabilitation, as well as 
improve strategies for home-based exercise prescription. The long-term goal of this proposal is to 
address these challenges by developing personalized, 
engaging, salient gait training treatments founded on 
evidence from neuroscience, biomechanics, motor 
learning, and gaming.  
Real-time biofeedback is a promising gait 
training intervention for targeting specific 
biomechanical impairments. Biofeedback can 
enhance an individual’s awareness of the impairment 
targeted during gait training, enabling self-correction of 
aberrant gait patterns 35.Real-time biofeedback gait 
training has been used for modulating step length 
asymmetry in people post-stroke 36, 37. Franz et. al. 
demonstrated that older adults increase AGRF and gait 
speed in a single session of biofeedback training 38. We 
recently showed that in response to treadmill training 
combined with visual and auditory real-time 
biofeedback, able-bodied individuals can increase 
AGRF unilaterally for the targeted limb39. Thus, AGRF 
biofeedback may be a beneficial strategy to target 
unilateral propulsive deficits in people post-stroke. Yet, 
surprisingly, this approach has never been explored for 
stroke gait training (see our preliminary stroke data in 
Figure 1).  
Incorporation of gaming interfaces for gait 
biofeedback can increase patient motivation, 
distract participants from fatigue or boredom, and 
encourage greater numbers of repetitions during 
gait training. “Gamification” is an informal umbrella 
term for the use of video game elements in non-gaming 
systems (such as rehabilitation) to improve user 
engagement and shape user behavior, a goal that 
resonates strongly with rehabilitation clinicians and 
scientists 40-42. Researchers exploring human computer 
interaction aim to identify design patterns that can afford 
joy of use 
43. Games are designed to be more 
interesting and enjoyable than traditional therapy tasks, 
thereby encouraging higher numbers of repetitions 
44-47. 
Games use cues to provide accurate and immediate 
biofeedback about movement performance, reward 
desirable performance, and discourage maladaptive 
compensations to change behavior, while also being 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
engaging and motivating 45, 48-50. Games can be personalized to each client by modifying task-difficulty 
according to baseline impairments, and designing task-specific programs that are meaningful to the 
patient, considerations that increase motor learning 46, 47, 49, 50. Games can provide opportunity to 
practice activities that may not be safe within the clinical environment 44-46. Furthermore, augmented 
reality (AR) is a cutting-edge gaming tool comprising an interactive visualization system (head-mounted 
display, computer, game console, tablet) allowing the merging of digital contents with the user’s real-
world environment 51. AR allows the augmentation of our real experience using gaming-interfaces, 
while reducing deleterious effects (disorientation, nausea, dizziness) often observed in fully immersive 
virtual environments 51, 52. From the perspective of rehabilitation clinicians, game-based therapies 
provide immediate, quantitative feedback about patient performance and allow for adjustment of the 
challenge during therapy 44-46, 49, 50. Recreational gaming consoles are ubiquitously enjoyed by 
individuals of all age groups, and could have immense potential for stroke rehabilitation.
Rehabilitation studies suggest that adding video games to a conventional rehabilitation 
intervention can enhance therapeutic benefits, patient enjoyment and motivation. The addition of 
a Nintendo Wii video game to an already familiar task of treadmill walking or cycling can increase 
exercise intensity (heart rate, cadence, speed) during training 48, 53. Compared to balance platform 
therapy, video game therapy induced superior  improvements in mobility, selective attention, and 
balance in people with chronic traumatic brain injury 54. In frail, community-dwelling older adults, 
dynamic balance exercises on fixed and compliant surfaces were feasibly coupled to interactive game-
based exercise, resulting in improvements in balance control 55. Off-the-shelf video game systems such 
as the Nintendo Wii 56, 57 and Kinect 
58 have been used for therapy, and shown to increase enjoyment 
during rehabilitation in individuals with disabilities. When video games were used for upper limb 
rehabilitation, users stated that the game made rehabilitation more fun and helped achieve greater 
exercise intensity 58. Neurologically-impaired patients deserve, and may, in due course, demand, 
high-quality entertaining gaming-interfaces as part of their rehabilitation. Regrettably, 
specialized game-based tools that focus training on specific gait deficits are not available in 
rehabilitation clinics 
44, 45. However, the transformative technologies already available to game-
developers and computer scientists provide an unprecedented opportunity to target this 
important and unmet clinical need. 
Our previous 39 and ongoing AGRF biofeedback studies, although successful, utilized a biofeedback 
visual interface that was simple, 2-dimensional, and not enjoyable (Figure 2)39. Therefore, in parallel, 
we initiated development of a visual interface for gait biofeedback, and developed the first visual effect 
game prototype/simulation that inputs/streams AGRF data (Figure 3). This project will conduct a          
preliminary study to test the feasibility and short-term effects of game-based biofeedback, and obtain 
user-data regarding the innovative gait-game design. 
A
B
Figure 2. Schematic showing the simple,  
non-game-based display currently used for 
AGRF biofeedback. (X) indicates the current 
antero-posterior GRF during the stride 
cycle, the bar indicates the target AGRF 
value. Visual feedback provides user an 
estimate of proximity to target AGRF. 
Auditory feedback indicates successful 
AGRF targeting. 
Figure 3: Screenshot from the prototype visual effects animation (Candy 
Cracking Game) developed by SCAD in collaboration with PI Kesar. (A) 
Candy balls move along a conveyor belt (. A hammer elevates and strikes 
the candy ball (once per gait cycle) in timing with generation of pushoff. 
(B) When the generated AGRF matches the targeted AGRF (successful 
increase of AGRF in response to biofeedback), the hammer breaks the 
candy ball, rewarding the user with visual effects and an increased game 
score. See supplementary video file or weblinkbelow for this prototype 
animation. 
http://ecollections.scad.edu/iii/cpro/DigitalItemViewPage.external?lang=eng&sp=1003554&sp=T
&sp=Pall%2CRk1000005%40%2CQdeborah+fowler&suite=def
See video S1 within this manuscript:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460355/

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
4. Objectives and Specific Aims 
Our long-term goal is to develop a more engaging, motivating gait biofeedback methodologies 
specifically designed for post-stroke gait training. We aim to address a major challenge for 
rehabilitation clinicians - to make gait training appealing and meaningful so that patients 
engage in sufficient repetitions, intensity, and challenge to maximize therapeutic effectiveness.  
Our premise is that post-stroke individuals will demonstrate greater engagement, motivation, and 
therapeutic benefits during gait training sessions involving biofeedback when training incorporates 
intuitive, entertaining, game-based interfaces. 
Our project specific aims are: 
Aim 1. To design a gaming interface that streams in AGRF data as input variable. Dr. 
Fowler, a visual effects professor from Savannah College of Arts and Design (SCAD) will lead this aim. 
Tasks for aim 1 include: (i) customize game to match user interests and goals (enhance salience during 
practice), (ii) develop scoring system to provide knowledge of performance; (iii) provide meaningful 
rewards within the game; and (iv) add challenge levels to cater to variability in gait impairments across-
individuals and time.
Aim 2. Synchronize the gaming interface with AGRF data recorded during treadmill 
walking to develop a real-time AGRF biofeedback gait training game, including an augmented 
reality (AR) game version.  Dr. Gandy Coleman, a computer scientist at Georgia Tech, will work with 
the P.I. toward this development aim. Tasks for aim 2 include: (i) develop custom software to 
synchronize the game display with AGRF data in real-time; (ii) develop an AR version of the game; (iii) 
confirm that game animations are synchronized with a user’s gait cycle; and, (iv) create patient reports 
about gait performance during training (for use by clinicians).
Aim 3. To obtain user data from stroke survivors and neuro-rehabilitation clinicians 
regarding our newly developed real-time AGRF biofeedback gait training game. Drs. Kesar and 
Wolf, stroke gait rehabilitation experts at Emory University will complete this aim. The game will be 
tested on 12 individuals with chronic post-stroke hemiparesis and 5 neuro-rehabilitation clinicians. 
Participants will complete 1-2 sessions comprising exposure to 2-3 gait biofeedback systems: (i) newly 
developed game-based interface (projector screen display), (ii) traditional, non-game interface, and (iii) 
VR version of the game (head-mounted display). Outcomes will include measures of participant 
engagement, user-reports and survey-responses on motivation, gait performance, fatigue, game 
characteristics, and adverse effects (e.g. nausea, dizziness) during game exposure.
Study Hypotheses
Aims 1 and 2 are focused on design and development of the gait biofeedback gaming 
interface. Aim 3 is a small-sample clinical trial evaluating the newly development biofeedback 
game. For Aim 3, we hypothesize that compared to the traditional non-game biofeedback, 
game-based biofeedback will induce greater improvements in paretic leg biomechanics, 
improve participant motivation and engagement, reduce fatigue, without significant adverse 
effects. 

Investigator: Trisha Kesar 
  Draft Date: September 5, 2023
5. Study design and methods
Our 2-year project comprises aims related to the design and development phase (aims 1 and 2) and 
testing phase (aim 3) for an innovative, game-based, gait biofeedback system. The overview, timeline, 
and potential future directions for the proposal are provided in Figure 4. 
The study will comprise 2 phases: 
Phase 1 will include the design and development of the game. For study phase 1, the P.I. will 
work with collaborators at Savannah College of Art and Design (SCAD) and Georgia Institute of 
Technology to develop the software interface (visual display and graphics) and the hardware 
instrumentation for the gait biofeedback game system. During the later stage of phase 1, preliminary 
feedback about the game design will be obtained from 10 stroke survivors and 5 able-bodied 
individuals.
Phase 2 will include the preliminary testing of the game-based feedback in comparison with a 
non-game-based feedback during treadmill walking training.  During phase 2, study participants will 
complete 1-2 experimental sessions comprising exposure to 2-3 types of gait biofeedback interfaces. 
User-feedback regarding engagement, fatigue, design will be obtained from 5 able-bodied individuals, 
12 stroke survivors, as well as 8 neuro-rehabilitation clinicians. The protocol and experimental 
conditions for phase 2 may be influenced by the progress and feasibility testing during phase 1. 
Research Procedures 
Aim 1. To design a gaming interface that streams in AGRF data as input variable
Methods and sub-tasks for Aim 1: Game design is the art of applying design and aesthetics to create 
a game that allows interaction with the user. Our goal is to develop an interface that provides an 
entertaining and engaging experience for stroke survivors, motivates them with positive feedback 
toward correct gait patterns, while being easy to play and learn. The game must also allow the therapist 
/user to adjust the game difficulty. Dr. Fowler’s team, as part of an ongoing collaboration with the P.I. 
Kesar, has developed a prototype for a visual effects game designed for gait biofeedback. The current 
prototype (candy-cracking game) provides the advantage of high-definition graphical display (Figure 3), 
in contrast to a simple, boring, and non-intuitive display currently available for biofeedback (Figure 2). 
The engaging visual effects animation involves candy balls that move rhythmically across a moving 
conveyor belt (to match the rhythmicity of gait cycles), with a hammer that elevates and strikes each 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
candy ball (to match the push-off force generation during terminal stance phase of gait) (Figure 3). The 
prototype animation uses AGRF data stream (recorded from the right leg during treadmill walking) as 
input. When the generated AGRF force exceeds the target threshold, the hammer strikes the candy 
and crushes it (indicator of success). Thus, the current visual effects game prototype is entertaining, 
game-based, and customized to match the rhythmic, repetitive movements during walking. However, 
several additions and enhancements are needed in the current design, as listed in table below. 
Summary of the design process (Aim 1): The design will focus on achieving the above sub-tasks. 
Fundamental principles of game design will be followed, including, but not limited to: focal point, 
anticipation, consequence of actions, believable events and behaviors, progression, environment, 
spacing, appeal (graphics/sound).  Dr. Fowler’s design team at SCAD will comprise design students 
from Visual Effects, Games, User Experience, and Sound Design. The current candy cracking game 
prototype was implemented in Unreal Engine. For the AR version, other platforms (HoloLens or Apple 
ARKit) will be explored, which will also facilitate development of the AR version in aim 2. 
User-evaluation of Aim 1: The pivotal point to this proposed design will be to attain a prototype that 
can be given to users (stroke survivors and physical therapists) so that feedback from users can 
contribute to game development.  As part of Aim 1, we will obtain iterative user feedback (i.e., play-
testing) about the game design from our investigative team, 10 stroke survivors, and 5 neuro-
rehabilitation clinicians. Consistent with the gaming-literature, user-interview questionnaires and user 
observations will be employed iteratively to guide game development58. 
Aim  2. Synchronize the gaming interface with GRF data recorded during treadmill walking to 
develop a real-time AGRF biofeedback gait training game, including an augmented reality (AR) 
game version.
Methods and sub-tasks for Aim 2: Dr. Gandy-Coleman, a computer scientist at Georgia Institute of 
Technology, will work in conjunction with the P.I. to accomplish this aim. Dr. Gandy has published 
studies on game-design for older adults, and the psychosocial, socio-economic59, and cognitive factors 
influencing game use by older adults
59, 60. Dr. Gandy’s team also developed an interactive video game 
for upper limb rehabilitation targeted for stroke survivors
61. 
The visual effects interface designed by the SCAD team (Aim 1) will use GRF data as input for 
the timing of animations in the visual effects game. The current prototype visual effect (Figure 3) is a 
standalone video file untethered to gait hardware. As part of Aim 2, we will develop the technological 
solutions needed to synchronize the visual effects interface with real-time GRF data recorded during 
treadmill walking. To accomplish this aim, Dr. Gandy Coleman and her team at Georgia Institute of 
Technology will develop a custom real-time biofeedback software program to enable integration of the 
visual effects animation with gait laboratory hardware (i.e. analog data derived from a force platform). 
Dr. Coleman’s team will conduct pilot tests at P.I. Kesar’s motion analysis laboratory. Anterior-posterior 
GRF analog data recorded from the force platform under the right leg will be streamed into the custom 
real-time biofeedback software platform developed by Dr. Gandy Coleman’s team. The biofeedback 
software program will input the AGRF data to the visual effects software. Our sub-goals for Aim 2 are 
listed in table below: 
(i) Game customization/individualization 
(ii) Game scoring system for providing knowledge of performance
Develop game versions or themes for different interests/hobbiesScore reflects cumulative % success (steps with target AGRF achieved)
         E.g. golf, gardening. Star Wars 
Determine score metrics, and dosage/frequency of display (faded feedback) 
(iii) Game Rewards
(iv) Game challenge levels 
For successful steps, develop visual & auditory reward 
Develop provision for clinician to calibrate error-tolerance, target AGRF 
e.g. sparks fly out from ball, pictures of grandchildren/family 
Develop game levels for advancing complexity/challenge as training progresses 
Sub-tasks for Specific Aim 1 

Investigator: Trisha Kesar 
  Draft Date: September 5, 2023
Summary of development process: Dr. Gandy Coleman, in conjunction with 2 computer scientists in 
her team, will lead the technology development. The teams will meet biweekly during the development 
phase (SCAD team will skype in). Aim 2 will culminate in the development of 2 versions of the game – 
(i) a game version that will be projected on a 2-dimensional (2D) display screen in front of the treadmill 
during gait training (projector-display); and (ii) a game version that will utilize AR technology to 
superimpose a simplified/minimized version of the game animation objects and effects onto the user’s 
real-world view (head-mounted AR display). We believe that the AR version will expand the potential 
clinical applications of the game, and facilitate translation to over ground, clinical, and community-
based settings. Additionally, having AR and non-AR (projector display) game versions can better cater 
to varied user preferences. 
User-testing as part of Aim 2: Game pilot testing will be performed on 5 able-bodied individuals in the 
P.I.’s laboratory at Emory. The users will provide scores on a 10-point scale rating the game’s 
accuracy, entertainment, and quality of feedback. The users will also provide comments about whether 
the game animation matched the motion of their targeted leg, whether the timing of the hammer striking 
the candy ball matched the timing of the target leg’s push-off generation. Our goal is to minimize the 
perceptible lag between AGRF generation by the targeted leg and the timing of the pertinent visual 
effect animation. We will also develop the training report to summarize the values of pertinent gait 
biomechanics variables (AGRF, trailing limb angle, step length, circumduction) for the paretic and non-
paretic leg before and after each training session. The patient report will guide therapist’s decisions 
related to determining the dosage, challenge, and progression of subsequent training sessions, as well 
as aid with clinical documentation.
Specific Aim 3. To obtain user data from stroke survivors and neuro-rehabilitation clinicians 
regarding our newly developed real-time AGRF biofeedback gait training game.  
Aim 3 will focus on evaluating the newly developed AGRF biofeedback game (both AR and non-AR 
versions).
Methods for evaluation on stroke survivors: The newly developed games will be tested on 12 
individuals with chronic post-stroke hemiparesis and 12 able-bodied adults. Inclusion criteria for stroke 
participants will include ≥6-months elapsed since a cortical or subcortical stroke, ability to walk on a 
treadmill for ≥2-minutes, ability to communicate with study team, stroke affecting the right leg (to match 
control participants’ biofeedback targeted leg and due to constraints with the game design prototype). 
Exclusion criteria include orthopedic or neurological conditions preventing walking, neurologic diagnosis 
other than stroke, cerebellar signs, and cognitive impairments preventing communication during the 
experiment62. Inclusion criteria for able-bodied will be ability to walk, and absence of orthopedic, 
neurological, or medical conditions that limit walking. Our proposed sample size is based on our studies 
(N=7 stroke, N=7 able-bodied) where we detected significant increase in targeted leg peak AGRF with 
power ≥0.80, with additional participants included to account for the self-report and survey-based 
game-evaluation outcomes.

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
 Study participants will complete one session comprising exposure to gait biofeedback systems 
in an order determined by block randomization (3 blocks). Participants will be exposed to 2-3 types of 
biofeedback interfaces – (i) the AGRF biofeedback game (projector-display, non-VR), and the (ii) 
traditional, non-game-based, interface (currently used, shown in  Figure 2). If we cannot test all 3 gait 
feedback conditions in 1 session, participants may be asked to complete a 2
nd experimental session 
when 2-3 feedback conditions (and control walk) will be evaluated. In a separate session, some 
participants may complete 
preliminary or exploratory testing 
of the VR version of the 
biofeedback game (head-mounted 
AR or VR display), which will be 
used to determine feasibility and 
preliminary effects of VR-based 
feedback on gait. The participants 
will be exposed to each of the 2 
types of biofeedback for a 
matched duration (2 to 6-minutes).  
Kinematic and GRF data will be 
collected as participants walk on a 
split-belt instrumented treadmill at a self-selected speed. Consistent with our preliminary stroke 
biofeedback studies, target AGRF will be calculated as 10-20% above the paretic AGRF. The same % 
target value for paretic AGRF will be used for all 3 types of biofeedback (i.e. similar challenge level). At 
the start of each walking task, participants will be provided scripted, verbal instructions regarding the 
training task (i.e. generate greater push-off force with the paretic leg) and the biofeedback interface (i.e. 
how the game works).  Participant will be told to complete the biofeedback task for as many minutes as 
possible without a break. Participants can request a break during the biofeedback, as needed. During 
and after completion of the gait evaluation session, we may obtain outcomes as listed in the table. To 
assess participant engagement during each biofeedback bout, we may use 2 published scales – the 
Hopkins Rehabilitation Engagement Rating Scale 63 and the Pittsburgh rehabilitation participation scale 
64. We will also ask the participant to self-report on how much fatigue and engagement they felt while 
walking with each feedback interface, using a Likert scale, a user-experience questionnaire, and/or a 
NASA-task load index (NASA-TLX) scale. Qualitative comments or feedback will also be gathered. Sex 
and age as biological variables may influence these outcomes, and will be addressed in our study 
design. 
Methods for additional evaluation on neuro-rehabilitation clinicians: We may obtain feedback 
from 5 clinicians specializing in neuro-rehabilitation, and with ≥2 years clinical experience. The 
clinicians will participate in 1-2 sessions comprising exposure to the 2-3 biofeedback systems (similar to 
above). At the end of the session, we will demonstrate to the clinicians how the game can be calibrated 
for each patient by modifying the target, challenge, and sensitivity. We will obtain self-report scores and 
questionnaire responses regarding game ease-of-use, technological complexity, customization, game 
aesthetics, and potential challenges limiting clinical use. Additionally, we may obtain clinicians’ 
feedback about the format and comprehensibility of a gait metric report. 
Detailed study procedures related to specific measurements are listed below. 
1) Procedures for Clinical Assessment of Walking Function: 
All subjects will review and sign consent forms before clinical testing. Clinical testing will be 
used to obtain the demographic and clinical characteristics of the study participants. 
Clinical testing may comprise all or a subset of the following: (1) measurement of subjects’ 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
over ground self-selected walking speed (10-meter walk test) at self-selected (SS) and fast 
speeds;  (2) over ground walking endurance measured by the distance ambulated during 
the 2-minute (for post-stroke individuals who are unable to walk for 6-minutes) or 6-minute 
walk test; (3) assessment of static and dynamic balance (measured using the dynamic gait 
index and the Berg balance score); (4) lower extremity portion of the Fugl-Meyer score
65; 
(5) step activity monitoring; (6) lower extremity proprioception (in which each limb segment 
will be flexed or extended approximately 10 degrees and the subject will be asked to 
identify whether the limb is moving and in what direction); (7) Lower extremity sensation 
utilizing monofilaments.  For monofilament testing, each limb segment will be tested and 
the smallest diameter filament that can be felt reliably (4 out 5 trials) will be recorded. 
Additional clinical measures may include measures of cognitive impairments and self-
reports (e.g. Stroke Impact Scale, Walk-12 questionnaire, Activity-specific Balance 
Confidence Scale, reports of physical activity levels). All these measurements will provide a 
characterization of the severity of subject’s gait function and gait impairments. For able-
bodied subjects, the clinical testing may not be performed or may only comprise a subset of 
the outcomes listed above.   
In addition, during this session, after completion of clinical testing, assessment of muscle 
strength and range of motion testing may be performed using standard clinical procedures 
for bilateral ankle and hip extensor and flexor muscles.  
2) Procedures for Gait Analysis during game-evaluation: 
Each participant will participate in 1 to 3 gait analysis sessions. All gait analysis 
and training procedures will be performed in the Motion Analysis Laboratory at Emory’s 
Rehabilitation Hospital. The lab is equipped with a 7-camera motion analysis system 
and a split-belt treadmill. The participants will walk on a split-belt treadmill instrumented 
with force platforms within each belt. For stroke survivors, to ensure safety, subjects will 
be able to hold on to a handrail as well as wear a harness supported from an overhead 
beam during treadmill walking. For additional safety, a physical therapist will be present 
during the session(s) and (if needed) will guard the subject during walking. For able-
bodied individuals, either the left or right leg will be the targeted leg during testing and 
biofeedback training. For stroke participants, the leg affected by the stroke (paretic leg) 
will be the targeted leg during biofeedback training. The procedures during the gait 
session(s) are listed below in chronological order. 
(i)Subject setup: To setup for motion analysis, we will attach biomechanical 
retroreflective markers to participant’s bilateral hip, knee, ankle, and foot segments 
as well as the pelvis and trunk segments. The materials and procedures used for 
marker setup will be similar to ongoing study protocols in our lab and our previous 
publications. Motion analysis procedures are described in detail in the next section. 
(ii)Subject familiarization: Next, the subject will be familiarized to the laboratory setup 
and treadmill walking at a slow speed
(iii)Determination of treadmill speeds: We will determine the participant’s comfortable 
self-selected (SS) and fastest comfortable (Fast) treadmill walking speeds during 
short 30-second walking trials, with standing rest breaks interspersed.  

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
(iv)Collection of baseline gait data: Baseline motion analysis data (marker positions 
using the 7-camera system and ground reaction forces using the treadmill-
embedded force platforms) will be collected as the participant completes short (10 to 
30-second) treadmill walking trials at 2-7 different walking speeds (ranging from SS 
speed to the Fast speed). These trials will provide information about the subject’s 
capacity for safe speed-modulation as well as the peak AGRFs and other 
biomechanical parameters at each speed for the paretic and non-paretic legs. 
(v)Calculation of AGRF and speed modulation ranges: Next, while the participant is 
provided a rest break, the experimenters will calculate the participant’s minimum and 
maximum peak AGRF to be used as a target AGRF during biofeedback for the 
study. The minimum AGRF (AGRFmin) will be set as the AGRF generated at the SS 
speed. The maximum AGRF (AGRFmax) will be set as approximately 25% greater 
than the AGRF generated at the Fast speed. The SS and Fast speeds determined 
and evaluated in step (iv) above will be used as the minimum and maximum speeds 
for the study. By setting these lower and upper limits for speed and AGRF 
parameters using each participant’s baseline walking trials, we will ensure that the 
biofeedback procedures use safe parameters customized to each participant’s 
walking capacity.  
(vi)Collection of short (30-60 seconds) gait trials to obtain data for game 
calibration: After providing the participant a seated or standing break (as 
requested), we will conduct 5-10 short gait trials (30-120 seconds). These data will 
be used to calibrate the internal settings for the gait-game and obtain estimates of 
the appropriate AGRF target.
(vii) Collection of two to three 2-6 minute biofeedback training bouts 
comprising exposure to the gait-game interfaces: During this part of the session, 
2 types of AGRF biofeedback training bouts (2-6 minutes long) will be implemented 
(bout order randomized across participants). The bouts will involve exposure to each 
of the 2 types of gait biofeedback interfaces – conventional feedback and game-
based feedback. 
(viii) Collection of post-training and retention gait data: Before (pre), during, and 
immediately after (post) each of the biofeedback bouts (as well as control bout 
without feedback), gait biomechanics may be recorded during short 30-second trials 
at SS and/or Fast speeds. To measure short-term retention or recall of motor 
learning, additional post-tests may be recorded after a 2-minute standing break. 
Self-reported measures of exertion, engagement, fatigue may also be obtained for 
each bout. 
(ix)Collection of self-report data regarding engagement, adverse effects, 
challenge, muscle soreness/fatigue during biofeedback: In order to evaluate the 
feasibility and subject perceptions in response to biofeedback training, we may ask 
the participant to rate their engagement or motivation, level of challenge 
encountered during biofeedback, and amount of fatigue on a 0 to 10 visual analog 
scale. Any other feedback, comments, and adverse effects reported by the subject 
will also be recorded. 
3) Procedures for gait biofeedback: During biofeedback, auditory and visual 
biofeedback will be provided using a visual display screen and a speaker pointed 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
toward the participant. The visual feedback display will vary according to the type of 
biofeedback interface being evaluated. For the traditional non-game biofeedback, the 
visual display comprises a horizontal line graph with a moveable cursor that represents 
the current measured value of antero-posterior ground reaction force for the targeted 
leg (The MotionMonitor, Innovative Sports Training Inc., Illinois, USA). The targeted 
peak AGRF range appears as a target line with vertical bars on either end, representing 
a 6-Newton error-tolerance range centered around the target AGRF39. The auditory 
feedback comprises an audible “beep” produced every time the cursor entered the 
target range, i.e. the participant achieves the targeted peak GRF value during their gait 
cycle39, 66. This same AGRF biofeedback audio-visual system has been successfully 
used in previous studies in both able-bodied and stroke survivors, showing the 
feasibility of the biofeedback setup
39, 66. For game-based biofeedback, the newly-
developed game-based interfaces will be used. The game-based biofeedback may be 
displayed on a projected screen or with VR goggles. 
4) Subject safety and monitoring during gait testing: During the treadmill walking, 
stroke subjects will wear a harness suspended from the ceiling (no body-weight 
support) for safety. In addition, if needed, the stroke subjects will be allowed to hold on 
to the hand rails during walking. Heart rate, Borg rate of perceived exertion, and blood 
pressure will be recorded at baseline. Additionally, heart rate will be monitored 
throughout the session with a heart rate sensor that is placed on the chest under 
clothing (Polar USA, Lake Success, NY).  If heart rate exceeds 80% of the age 
predicted heart rate maximum, walking will be stopped until it returns to baseline. In 
addition, blood pressure will be monitored at each rest break. If a subject’s blood 
pressure exceeds 190/100 mmHg, the session will be stopped and their blood pressure 
will be continually monitored until it returns to baseline. We may also measure skin 
resistance using a small sensor wrapped around the finger. Subject’s rating of 
perceived exertion on the Borg Scale of Perceived Exertion will also be monitored 
intermittently throughout the session.  If a subject reaches level 16 on the Borg scale or 
appear too tired, they will be given a rest break67.
5) Procedures for Motion Analysis: 
As stated above, retro-reflective markers will be attached to the subjects’ lower 
extremities68, 69. Elastic bands (Fabrifoam, USA) will be wrapped around the thighs, 
calves and pelvis to which small, thermoplastic shells containing reflective markers will 
be attached.  Additional markers will be taped to the subjects’ shoes and on the upper 
back, shoulder, hip, knee, and ankle joints with adhesive skin tape.  Marker data will be 
collected using a 7-camera motion analyses system (Vicon, Oxford, UK). We may also 
attach non-reflective adhesive markers to a few anatomical locations. During treadmill 
walking, ground reaction forces during treadmill walking will be collected using a 
treadmill instrumented with two 6-component force platforms under each belt (Bertec, 
USA). During over ground walking, ground reaction forces will be collected using a force 
plate embedded within the lab floor (AMTI, USA). In addition, in order to record muscle 
activity, small electromyography (EMG) sensors may be attached to various muscles. 
The EMG sensors will be attached using hypo-allergenic adhesive. EMG signals may 
be recorded from the following muscles: tibialis anterior, soleus, gastrocnemius, 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
quadriceps femoris, hamstrings, gluteus medius, and erector spinae. All analog data 
(force platform, EMG, footswitch, and stimulation channels) will be collected at 2400-Hz.
In addition to the walking trials described above, motion analysis data may be 
collected during 1- to 10-second long static postures (standing and sitting). During 
treadmill walking, subjects will wear a ceiling-mounted safety harness during all trials. 
An emergency shut-off switch will be positioned within arm’s reach of the experimenter 
and can be used at any time by the experimenter to stop the treadmill. Subjects will be 
allowed rest breaks as often as requested.  Similar to the described above, subjects’ 
heart rate, perceived exertion, and blood pressure will be monitored during the session. 
Risks 
Risks associated with clinical testing, gait training, and motion analyses include falling, fatigue, 
poor heart rate and blood pressure response to walking and minor skin irritation from the 
adhesive tape. To minimize risk, subjects will wear a safety harness during the treadmill testing 
and will be given a rest break whenever requested.  Heart rate will be monitored continuously 
and blood pressure will be monitored at rest breaks. Throughout the experiment, the 
experimenter has access to an emergency safety switch that can be used to stop the treadmill 
immediately. 
Risks associated with gait biofeedback: The safety, feasibility, and effects of AGRF 
biofeedback have been evaluated in able-bodied and post-stroke individuals39, 66. Here, we 
evaluate the short-term effects of a short, 2 to 6-minute AGRF biofeedback bouts with different 
biofeedback interfaces. The speed ranges (SS and Fast) and AGRF ranges (AGRFmin and 
AGRFmax) will be planned using each individual’s own baseline walking data, making them 
customized to their own walking capacity. Potential risks during biofeedback bout training may 
be similar to those encountered during strenuous exercise – tiredness, muscle fatigue, muscle 
soreness, and potential for joint discomfort. Potential risks from the VR versions of the game 
include dizziness, nausea, and sense of disorientation. 
 
Potential Benefits 
These procedures are experimental, and the responses of individual subjects to the gait 
training sessions may vary widely. We do not anticipate that the participants will gain any 
benefits from study participation. The long-term findings of this study can help us better 
understand the effects of and guide the design of clinical gait rehabilitation which can benefit 
other stroke survivors in the future.  
Type of Information Collected 
During clinical testing, to help characterize the clinical characteristics of the subject group, 
information such as age, time since the stroke, height, weight, side of hemiparesis, etc. will be 
collected. To help characterize the level of impairment of the subject group, information such 
as walking speed, endurance, walking function score, lower extremity sensation, etc. will be 
collected. During motion analysis and gait training, the 3-dimensional camera system will be 
used to track the 3-D positions of the subject’s segments; these data will be used to compute 
joint angles, ground reaction forces, joint moments, joint powers, etc. All subject data will be 
de-identified and then compiled on an excel spreadsheet and imported into SPSS 21 (SPSS, 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
Chicago, IL) for statistical analysis. De-identified data will be disseminated through group 
discussions, presentations and publications.
Management of Subject Data 
Subjects will not be anonymous to the researchers.  As a first step during data management, 
subject identities will be de-identified by assigning each subject a number.  Any computer files 
containing information that link the identifiable subject data with the de-identified subject 
number will be password protected and stored on a secure Emory University computer. Only 
de-identified data will be used to compile spreadsheets of the various outcome measures 
collected as part of this study (joint angles, clinical impairment scores, EMG, etc.). De-
identified data may be stored for future use until 5-years after completion of the study. All 
signed consent forms will be stored in the subject’s folder (organized by de-identified subject 
numbers)  in a locked file cabinet at the University. Participants will not be audiotaped, 
photographed or videotaped without their permission during this study. 
6. Participant selection
For Aim 1 and 2, for preliminary testing of the game design and to get user-feedback on game 
usability, we plan to collect user-survey and feedback data on 10 stroke survivors and 10 able-
bodied individuals.  For Aim 3, which is the clinical trial portion of the project, we plan to collect 
data on a sample of 12 individuals post-stroke and 8 able-bodied neurorehabilitation clinicians. 
Inclusion Criteria for Post-Stroke Subjects are: 1) age 30-90 years, 2) chronic stroke (>6 
months post stroke) affecting the left leg, 3) ambulatory with or without the use of a cane or 
walker, 4) able to walk for 2 minutes at the self-selected speed without an orthoses, 5) resting 
heart rate 40-100 beats per minute.  
Exclusion Criteria for Post-Stroke Subjects are: 1) cerebellar signs (ataxic (“drunken”) gait 
or decreased coordination during rapid alternating hand or foot movements, 2) history of lower 
extremity joint replacement, 3) inability to communicate with investigators, 4) 
neglect/hemianopia, or unexplained dizziness in last 6 months,  5) neurologic conditions other 
than stroke, 6) orthopedic problems in the lower limbs or spine (or other medical conditions) 
that limit walking or cause pain during walking. 
Inclusion Criteria for Able-Bodied Individuals are: 1) age 18 to 90 years, 2) no history of 
neurologic disease, 3) no history of orthopedic disease or injury affecting the lower extremity. 
Exclusion Criteria for Able-Bodied Individuals are: 1) history of neurologic disease, 2) 
history of orthopedic disease or injury to the lower extremity in the past 6 months, 3) pain or 
discomfort during walking, 4) cardiovascular or medical condition affecting ability to exercise or 
walk.  
Subject Recruitment. Study participants will be recruited from the general community within 
and outside Emory University. 

Investigator: Trisha Kesar
  Draft Date: September 5, 2023
Withdrawal from study. Participation in the present study will be voluntary and subjects will 
be able to opt out of the study at any time. 
Inclusion of children: Our subject population will be representative of adults with stroke and 
healthy adults. The incidence of stroke is considered rare in children, estimated to be less than 
13 cases per 100,000 children70  and their inclusion would not provide additional information 
compared with the data gathered in adults.
7. Statistical analysis
Hypotheses testing will be done with a p-value set at 0.05. The primary biomechanical 
outcome variables will be peak AGRF and trailing limb angle. Secondary variables will be user 
self-reports regarding game engagement, motivation, and fatigue. 
Power Analysis: The current protocol is designed as a preliminary study, and will be used to 
conduct systematic power analysis, effect-size and sample-size calculation, which will be 
utilized to design a larger-scale future study. 
8. Adverse event reporting
We will record and notify the IRB of any adverse events that occur during the protocol. 
10. Device information
Equipment for Motion Analysis and Gait Retraining: A 7-camera system will be used to collect 
motion analysis data (Vicon, Oxford, UK). Ground reaction forces during treadmill walking will 
be collected using a treadmill instrumented with two 6-component force platforms under each 
belt (Bertec, USA). During over ground walking, ground reaction forces will be collected using 
a force plate embedded within the lab floor (AMTI, USA). In addition, small electromyography 
(EMG) sensors will be attached to various muscles to collect EMG data (Noraxon Inc., Arizona, 
USA). The EMG sensors will be attached using hypo-allergenic adhesive. Pressure-sensitive 
foot switches (25-mm diameter MA-153, Motion Lab Systems, LA) will be attached to the 
underside of the subjects’ shoes; the foot switch data will be used to record gait events 
(Noraxon Inc, Arizona, USA). All these devices widely used for measurement of human 
movements. 
Equipment for gait biofeedback: The auditory and visual biofeedback will be provided using a 
visual display projector screen and a speaker pointed toward the participant. The visual 
feedback display comprises a horizontal line graph with a moveable cursor that represents the 
current measured value of antero-posterior ground reaction force for the right leg. The 
biofeedback software is a plugin provided by The MotionMonitor, Innovative Sports Training 
Inc., Illinois, USA. The game-based biofeedback interfaced will be designed and developed as 
part of this current project, with help from our collaborators at Savannah College of Arts and 
Design (SCAD) and Georgia Institute of Technology. We may use a commercial off-the-shelf 
VR headset such as the Oculus for the VR display. 
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