# ReHabgame: A non-immersive virtual reality rehabilitation system with
applications in neuroscience

Shabnam Sadeghi Esfahlani<EMAIL_ADDRESS>Tommy
Thompson<EMAIL_ADDRESS>Ali Davod Parsa<EMAIL_ADDRESS>Ian
Brown<EMAIL_ADDRESS>Silvia Cirstea<EMAIL_ADDRESS>Anglia Ruskin University, Department of Computing and Technology, CB5 8DZ,
Cambridge, United Kingdom  Anglia Ruskin University, Department of Medical
Science, CB5 8DZ, Cambridge, United Kingdom

###### Abstract

This paper proposes the use of a non-immersive virtual reality rehabilitation
system ”ReHabgame” developed using Microsoft KinectTM and the ThalmicTM Labs
Myo gesture control armband. The ReHabgame was developed based on two third-
person video games that provide a feasible possibility of assessing postural
control and functional reach tests. It accurately quantifies specific postural
control mechanisms including timed standing balance, functional reach tests
using real-time anatomical landmark orientation, joint velocity, and
acceleration while end trajectories were calculated using an inverse
kinematics algorithm. The game was designed to help patients with neurological
impairment to be subjected to physiotherapy activity and practice postures of
daily activities. The subjective experience of the ReHabgame was studied
through the development of an Engagement Questionnaire (EQ) for qualitative,
quantitative and Rasch model.
The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator
algorithms were used to adapt the physical and gameplay intensity in the
ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring
System (HSS). Rasch analysis was conducted to assess the psychometric
characteristics of the ReHabgame and to identify if these are any misfitting
items in the game.
Rasch rating scale model (RSM) was used to assess the engagement of players in
the ReHabgame and evaluate the effectiveness and attractiveness of the game.
The results showed that the scales assessing the rehabilitation process met
Rasch expectations of reliability, and unidimensionality. Infit and outfit
mean squares values are in the range of $(0.68-1.52)$ for all considered 16
items. The Root Mean Square Residual (RMSR) and the person separation
reliability were acceptable. The item/person map showed that the persons and
items were clustered symmetrically.

##  1 Introduction

Rehabilitation is a complicated and active process by which those affected by
injury or disease achieve a full recovery or, if a full recovery is not
possible, realise their optimal physical, mental and social potential that
allows them to regain much of their independence and quality of life [29]. It
is agreed that intensive, repetitive, and goal-oriented rehabilitation process
improves motor function and cortical reorganisation in stroke patients with
both acute and long-term (chronic) impairments. However, this recovery process
is typically slow and labour-intensive, usually involving extensive
interaction between one or more therapists and one patient [19]. Motor
disability does not only cause limitations in functional motor control, muscle
strength, and range of motion (ROM) but can limit the ability to perform daily
tasks, that could cause isolation, and reduce participation in community
activities [27] and [36]. Participating in repetitive exercises can help these
people to overcome the limitations they experience, but lack of action and
isolation stops them to perform recommended practices thus, become weaker, and
cause obesity-related chronic health conditions and so on. Presumably, every
person participating in rehabilitation hopes to be happily situated,
productively occupied, and efficiently supported throughout the process [23].
A lack of motivation is another impediment that stops them to participate in
physiotherapy sessions regularly.
Studies in computational neuroscience have shown that virtual reality (VR)
rehabilitation techniques could create an environment that recovers health
conditions as well as community integration [50] and [36]. A study by [50]
among post-stroke patients was conducted to investigate the effects of the two
treatment methods: (I) combined VR and conventional therapy and (II) standard
therapy alone. Their findings showed that the first process (combined VR and
traditional treatment) was more efficient than the other. The past decade has
witnessed the creation of a vast number of successful VR based rehabilitation,
educational and commercial video games. They use new technology to simulate
realistic features of the real world, [26], [54], [25], [28], [51], [21],
[43], [8], [49], [46], [30] and [16]. [33] conducted the application of VR in
psychology as an assessment tool considered as a highly sophisticated form of
adaptive testing. [15] reviewed the applications of VR in the field of motor
rehabilitation. They reported that the people with disabilities appear capable
of motor learning within VR environments and the movements learned in VR are
transferable to real-world equivalent. [25] investigated the effects of
training of the lower extremity using a robot coupled with virtual reality
(VR). They also showed that movements learned in a VR environment are
transferable to real-world similar motor tasks. [52] have used interesting VR
scenarios to reshape cortical networks in patients which resulted in
significant improvement in controlling the body, a decrease in neuropathic
pain, and an increase in muscle strength.

Figure 1: The ReHabgame diagram and its components with MCTS algorithm.

The ”ReHabgame” was developed based on the neuromuscular rehabilitation system
that assists players to combat the physical impairment and improve the upper
limb performance. It is a flexible, non-immersive, low-cost VR system that
does not use any head mounted devices but simulates the VR features
integrating off-the-shelf devices such as the Kinect V2 (Microsoft in
2014)111https://en.wikipedia.org/wiki/Kinect and Myo armband (Thalmic Labs in
2016). This system encourages motor impaired participants to interact with
virtual objects which are spawned in the scene. This system provides an easy
and engaging rehabilitation process compared to using other haptic devices. It
could be used by patients for long-term recovery which leads to better
rehabilitation outcome. It was developed in Unity game engine that integrates
the motion/gesture capture, image processing, skeleton tracking, IR emitter,
inertial measurement units and EMG sensors capabilities into the games. The
postural control/balance, gestures and range of motion (ROM) of the upper body
was tracked through this marker less system. It facilitates the calibration of
joints orientation, position of the leading joints and muscle activities
throughout the gameplay. The ReHabgame was built as a single-player (the
clinician’s avatar instructs the patient’s avatar to copy the exercises) and
two-player game (two patients play at the same time and compete against one
another). All the data were collected and saved in the background (hard drive)
through the devices and were used for qualitative and quantitative analysis.
In this study, we define VR as a computer-based, interactive, multi-sensory
environment that occurs in real-time. It provides enhanced feedback about
movement characteristics, and advanced motor task learning and execution. Our
study shows that the use of interactive VR system improves upper limb
rehabilitation and habilitation which concurs with findings in literature
including [33, 34, 15, 35, 12]. Fig 1 illustrates the architecture of the
system with its components. Two algorithms were adapted to acquire the
gameplay to players’ ability using MCTS algorithm and Random Object Generator
(ROG). Players interact with the virtual objects in the virtual park through
three scenarios (case studies) such as; (I) ”Reach-Grasp-Release fruits game”,
(II) ”Reach-Press-Hold buttons game” and (III) ”Reach-Press buttons game”
which are analysed together as the ReHabgame. An Engagement Questionnaire (EQ)
was developed and administered to a group of 20 subjects to assess
subjectively the effectiveness and efficiency of the ReHabgame using item
response theory (IRT). The results of the Rasch analysis of the outcomes of
this experiment are presented in this paper. To our knowledge, this is the
first study that proposes an instrument to measure the subjective experience
and employs Rasch analysis to evaluate the engagement in a motor
rehabilitation game. Three features of game engagement were considered: flow,
presence and absorption [2, 40]. The Rasch model analysis of the gameplay
shows that there is an appropriate balance between skill and challenge (flow).
Participants feel present in the game and perceive a high-level of control
(presence), which in turn offers a raised degree of engagement, thus,
distracting them from the pain associated with the impairment (absorption).

##  2 Design

###  2.1 ReHabgame principles, design and operation

The player reaches and grasps virtual fruits generated in the 3D world and
releases them above the virtual basket [10, 11]. The system is designed for
one or two players to play at the same time independently as illustrated in
Fig 2. It can be set up to rehabilitate the upper limbs (right hand, the left
hand or both hands at the same time) and postural balance. The system combines
the abduction, adduction, flexion, extension, internal/external rotation and
horizontal abduction/adduction. The two-player game may be arranged as a
clinician (first avatar/ instructor) shows the movements of the patient
(second avatar) in that the patient follows the actions of the clinician and
accordingly the patient’s ROM is recorded by the system. These data are used
by the artificial intelligence (AI) control system to generate virtual objects
within patient’s comfort zone initially and then gradually push them further
out of the comfort zone. The algorithm is designed based on inverse kinematic
in that the joints (such as head, neck, shoulder, elbow, wrist, etc.) location
are monitored through the Kinect for further analysis. The ReHabgame was
developed as follows:

  1. 1.

”Reach-Grasp-Release”: The players gather the fruits spawned in the scene and
release them above the virtual baskets to gain points. As soon as the fruit is
collected it must be released above the basket to hit the bottom of the basket
to collect points. It was developed using the gravity component and raycast
hit function in Unity. Fig 2(a) shows the female avatar who has collected the
orange with the left hand and aims to release it above the basket. The
downward red circles indicate that it shouldn’t be released unless the
downward circles changed to green colour. Thus, the player should adjust her
arm to receive positive visual feedback (the colour changes from red circles
to green). The male character’s shoulder joint follows a horizontal abduction
(extension), the elbow joint is in a flexion mode and the hand is extended.
The male avatar’s head and neck are tilted towards right shoulder that is
detected as a problem in the postural control system. The streamed video and
joint locations collected via the Kinect are used for further analysis. Fig
2(b) illustrates the female avatar who aims to reach the kiwi while the right
shoulder’s joint is abducted. The male character in this figure has picked the
peach and wants to release it above the basket but the visual feedback
indicating the red downward circles which means it is not safe to release it.
In Fig 2(c) the female avatar collected the strawberry but cannot release
unless the circles indicating green. The male character is idle in this
figure. Fig 2(d) shows the female avatar has collected the strawberry and
wants to release it since the visual feedback specifies the green circles
meaning that she can release the fruit to gain points. The male’s avatar hand
is extended and the elbow join is flexed.

  2. 2.

”Reach-Press-Hold”: In the reach-and-press button game, the player reaches out
to a button to press it; the latter task can be configured to require a press-
and-hold action or a press-for-specific-time action from the player in that
the steadiness and the vibration of the hand are recorded based on $30$ fps.
Fig 3 illustrates the player & avatar in button Reach-Press-Hold game. Buttons
are generated in a matrix format defined in the system based on patient’s
range of movements (ROM). Fig 3(a) shows the button generated around the
player’s head height in that the arm is abducted (away from the midline of the
body). The player should raise the hand above the shoulder’s height to reach
the button which requires higher ROM. Fig 3(b) illustrates the button
generated in an area around the player’s heap height with arm abduction. Fig
3(c) shows the arm’s flexion (horizontal adduction) to reach the button
generated at the shoulder’s height. The player reached the button and pressed
it for one second with a visual feedback via the green dot around the white
circle. Fig 3(d) illustrates the red part of the button is pressed for three
seconds with three green visual feedbacks appeared around the button. If the
player’s press and hold is not steady enough, the timer restarts itself.

(a)

(b)

(c)

(d)

Figure 2: Screen shots of a male and a female players with the representative
avatars in the “Reach-Grasp-Release” fruits game. (a) shows the female avatar
collected the peach and aiming to release it above the basket, and the male
avatar collected a kiwi, (b) displays the female avatar aiming to collect the
kiwi and the male avatar has collected the peach, (c) the female has collected
the fruit and the male avatar is idle and (d) demonstrations the female avatar
released the strawberry above the basket with green dots provided the positive
feedback.

(a)

(b)

(c)

(d)

Figure 3: Screen shots of a player in the “Reach-Press-Hold” button game. (a)
the avatar’s right hand is engaged with the game which aiming towards the
button to press its red part, (b) illustrates the avatar pressed the red
button for three sec with three green dots appeared next to it which
illustrates the visual feedback, (c) displays the avatar reached and pressed
another button and holds it for one sec and (d) demonstrates the avatar with
three seconds holding with its visual feedback.

###  2.2 Virtual object generator algorithms

Monte Carlo tree search algorithm (MCTS) has revolutionised the game’s
performance where partial information requires decision making under
uncertainties [4]. MCTS is used as a method of finding optimal choices in a
given domain by taking random samples in the decision space and building a
search tree accordingly. The primary mechanism of generating adaptive patient-
specific tasks in the ReHabgame resides in the object generator algorithm,
which employs the MCTS procedure and Random Object Generator (ROG). To explore
the effects of physical and gameplay intensity the MCTS object generation
algorithm was used versus ROG.
MCTS was based on Monte Carlo sampling with a large number of random simulated
games. MCTS makes the computation converge to the right value much more
quickly than simple Monte Carlo itself [5]. This probabilistic algorithm uses
lightweight random simulations of a problem space to grow a search tree that
represents the unique states of the problem domain, with child nodes
representing the outcome of specific actions [3]. In the ReHabgame the
algorithm was broken down into four stages as highlighted below:

  1. 1.

Selection: The algorithm starts at the root node, builds a tree of possible
future game states. Each node in the tree represents a state and each link
represents an action taken in that state and leads to a new state. While the
state is built in the tree, the next action is chosen according to the
statistics stored, in a way that balances between exploitation and
exploration. The task is either selecting an action that leads to the best
results so far called exploitation or a less likely action still needs to
explore (known as exploration due to the uncertainty of the evaluation).

  2. 2.

Expansion: More state (child nodes) needs to be added as new nodes to expand
the tree. The tree is grown by one node for each simulated game according to
the available actions. The first action is chosen by an expansion strategy and
subsequently simulated. This results in a new game state, for which a new node
is created.

  3. 3.

Simulation: After expansion, a rollout is done from the new node, which means
that a simulation is run from the new node applying random actions until a
pre-defined stop criterion is met or the game ends. The satisfactory weighting
of action selection probabilities has a significant effect on the level of
play. If all legal actions are selected with equal probability, then the
strategy played is often weak, and the level of the Monte-Carlo program is
sub-optimal [4].

  4. 4.

Backpropagation (backup): The score difference resulting from the rollout is
backed up to the root node, which means that the reward is saved to the
visited nodes then a new iteration starts. Backpropagation updates node
statistics that are used for future decisions and ultimately inform the final
decision made by the system for the root node.

An effective variant and selection strategy is the modified version of MCTS
called upper confidence bound for trees (UCT) controller that construct
statistical confidence intervals, [7, 13]. UCB is efficient and guaranteed to
be within the best possible bound to address the exploration-exploitation in
MCTS [3] as illustrated in Eqn. 1. A child node $j$ is selected to maximise
UCT, where $n$ is the number of times the current node (parent) has been
visited, $n_{j}$ is the number of times child $j$ has been visited and
$C_{p}>0$ is a constant that shifts priority from exploration to exploitation.

$UCT=\bar{X}_{j}+C_{p}\sqrt{\frac{\ln n}{n_{j}}}$ $None$

By increasing $C_{p}$, the priority is shifted to exploration which means;
states that have been visited less will be visited with a higher priority than
states that have been visited more. A decrease changes priority to
exploitation means that; states which have a great value are visited more to
maximise reward. If more than one child node has the same maximal value, the
tie is usually broken randomly. The estimated value for every node that has
been visited in the iteration is updated with the reward. The estimated value
of a node is the average of all rewards backed up to that node.

##  3 Model

###  3.1 Motor Assessment Scale

The Motor Assessment Scale (MAS) [41, 42] is the standardised assessment tool
in the clinical evaluation and outcome studies of stroke rehabilitation. It
consists of 8 items representing different areas of motor behaviour: supine-
to-side lying onto entire side; supine to sitting over a side of the bed;
balanced sitting; sitting to standing; walking; upper-arm function; hand
movements; and advanced hand activities. The four items of MAS was used in
this study for the design of VR system. The postural balance (increments of
displacement of the person’s centre of mass and upper body joints) were
monitored by the Kinect, upper-arm function; hand movements; and advanced hand
activities data were assessed via both the Kinect and Myo armband devices.
A hierarchical scoring system (HSS) was used in ROG in that when the player
achieves the criterion for a score of more difficult level the easier steps do
not need to be tested. The study considers that the force of gravity and
higher degrees of freedom of joints require greater control thus considered as
advanced steps in the game level design [41]

  * 1.

Upper-Arm Function: Shoulder’s depression, elevation, protraction, and
retraction while placing elbow in a predefined position. Add elbow’s Flexion
and extension at different angles. Take hand to an area around a head
position, and behind it. Fig 4a shows the elbows’ fixed position that was
selected by the virtual purple button, and Fig 4b illustrates the hands’
orientation that was picked by the player. The fruit cannot be collected
unless the purple button was pressed by the elbow (which was fixed to this
location) and changed to green colour as an indicator of a positive feedback.

  * 2.

Hand Movements: Radial deviation of the wrist, lift the hand, reach forward
(extend arms fully/ partially) in a pickup shape. Show palm to the screen
while shoulder and elbow are in different positions (i.e. shoulder protracted,
elbow extended, wrist neutral or extended and palm in contact with virtual
objects).

  * 3.

Advanced Hand Activities: Grasp and releasing an object (I) the hand transport
for carrying the hand toward the purpose considering shoulder, elbow and wrist
movements and (II) the grasp component for shaping hand (fingers and thumb)
for collection [18] and [27]. Hand gestures to pick up, and release open/close
fingers and the thumb.

Figure 4: Avatar in “Reach-Grasp-Release” fruits game with two steps elbow and
hand position; (a) shows step 1: place the elbow on purple button in that the
colour changes to green as shown in (b), it also illustrates the step 2 in
that the avatar moved the hand towards the virtual lemon’s location.

The scoring system was designed based on these components. If the attempt was
successful the score is one: $+1$, if partially completed is half: $0.5$, if
was unsuccessful is zero: $0.0$, as defined in Eqn. 2:

$M_{Score}^{i}=\left\\{\begin{array}[]{lll}M_{successful}^{i}&\ Score\ is\
+1.0\\\ M_{PartiallySuccessful}^{i}&Score\ is\ +0.5\\\ M_{NotSuccessful}^{i}&\
Score\ is\ ~{}0.0\\\ \end{array}\right.$ $None$

$M_{Score}^{i}$ is the score of the $i^{th}$ play where the setting could be
graded for implementing the time as another factor to measure the count. It
was determined by confronting the player’s time with a linear interpolation
between a best possible time (max score) and a maximum period allowed (0
counts).

Figure 5: (a) is the game engine with the MCTS generator script that is
attached to the generator object. (b) Illustrates the main menu of the
ReHabgame. (c) shows the virtual peach generated in the player’s neck high
with the shoulder (sh-yaw, sh-pitch, sh-roll) and Elbow orientation values
next to the player (these orientation are based on the red dots shown on the
screen on avatar’s right-hand side) and (d) illustrates the orange generated
above player’s head height.

The choice of the MCTS/ROG algorithms and other parameters including the
number of iterations left/right-handed gameplay, type of the game and
single/double player game can be defined through the main menu. Fig 5a and Fig
5b, illustrates the Game Engine and the main menu of the ReHabgame,
respectively. The maximum and minimum range of orientation and movement are
predefined (i.e. in the MCTS the shoulder’s yaw is in the range $[0,90]$. The
pitch is $[-90,90]$, the roll of the shoulder is between $[-90,0]$ and the
elbow is within $[0,120]$. Each range is given a density based on the number
of samples selected from the menu. This limited number of options allows for a
non-infinite search-space. Fig 5c, Fig 5d shows the virtual fruit that is
spawned in the 3D-world based on the confidence of the search-tree in the pre-
established range of the upper limb orientation. The black and red dots, next
to the avatar in Fig 5, show the effects of the parameters picked for shoulder
and elbow freedom of movement. $10$ samples were selected for shoulder’s yaw
range (Sh yaw: first row of the black and red dots), $19$ samples for its
pitch (Sh pitch: second row of the black and red dots), $10$ samples for its
roll (Sh roll: third row of the black and red dots) and $13$ for the elbow
(Elbow: fourth row of the black and red dots). The red dots in each picture
corresponds to the orientation sample picked by the exploration tree to spawn
the fruit. In Fig 5c the red dot in the ”sh yaw” row represents the desired
rotation of $38^{\circ}$ needed to be explored. Likewise, the ”sh pitch”, ”sh
roll” and ”elbow” are determined based on their range, samples and confidence.

###  3.2 Inverse Kinematics (IK)

Kinematics refers to the mathematical description of motion without
considering the underlying physical forces. The kinematics of the human body
is specifically concerned with formulating and solving for the translation,
rotation, position, and velocity of each body segment in real-world motions.
The movement of a kinematic chain, whether it is a robot or an animated
character, is modelled by the kinematic equations of the chain [31, 48, 22].
The IK determines the typical parameters that provide a desired position of
the end-effector to achieve the task and is known as action planning [22]. IK
techniques provide direct control over the placement of an end effector object
at the end of a kinematic chain of joints, solving for the joint rotations
which place the object at the desired location. IK is adapted for calculating
the end effector for the virtual object’s location. The algorithm that was
used to calculate the joint angles required to collect the objects is defined
in Eqn. 3:

$\begin{array}[]{llllll}\theta_{1}\leftarrow\arctan 2(y,x)\\\
\theta_{2}\leftarrow\arctan 2(z-l_{1},\sqrt[]{x^{2}+y^{2}})-\arctan
2(l_{3}s_{3},l_{2}+l_{3}c_{3})\\\ \theta_{3}\leftarrow\arctan
2(s_{3},c_{3})\\\ \\\
where:\left\\{\begin{array}[]{ll}c_{3}\leftarrow\frac{x^{2}+y^{2}+(z-l_{1})^{2}-l_{2}^{2}-l_{3}^{2}}{2l_{2}l_{3}}\\\
\\\ s_{3}\leftarrow\sqrt[]{1-c_{3}^{2}}\end{array}\right.\end{array}$ $None$

Where $\theta_{i}$ are orientation around each axis and $l_{i}$ are each
segment’s length.

##  4 Methodology

###  4.1 Design of the EQ

Deep engagement in game-playing has the potential to be an important factor to
predict the impact of using video games for rehabilitation purposes. Three key
concepts are used in the literature to describe subjective experience relevant
to engagement in a game: flow, presence and absorption [2, 40].
Flow describes the enjoyment feelings due to a balance between skill and
challenge in the process of performing an exercise [6, 44]. The dual flow
model measures a combination of attractiveness in terms of compelling gameplay
and effectiveness in terms of physical outcomes [44]. Defining a specific goal
and an immediate performance feedback increase the likelihood of flow, and
being in a flow state seems to enhance learning. Flow states also include a
feeling of being in control, being one doing the activity, and experiencing
time distortions.
Presence is another intrinsic motivation in gaming contexts which is the sense
of being within the game world by controlling the game, a need for challenge
and feeling of effectence (the feeling of efficacy [55]). Concepts of presence
are widely discussed by game designers, who attempt to make the experience of
virtual worlds feel real and authentic [40].
Absorption defined as the total engagement in the present experience [2]. In
contrast to presence and in common with the flow, being in a state of
psychological absorption induces an altered state of consciousness [39]. In
this altered state, there is a separation of thoughts, feelings, experiences
and effect are less accessible to consciousness [14, 45]. While this feature
is perceived as having a negative psychological effect, we are looking at its
potential to detach and distract the players from feeling the discomfort
associated with their impairment and, thus, avoid frustration and giving up.
An Engagement Questionnaire (EQ) was developed to evaluate user involvement in
the ReHabgame using a self-report measure. The questionnaire has two parts, in
part 1, a set of 16 questions require the user to rate on a Likert scale their
experience in terms of flow (questions 3, 6, 8, 9, 16), presence (questions 5,
12, 13, 14, 15) and absorption (questions 1, 2, 4, 7, 10, 11). In the second
part of the questionnaire, open-ended questions were asked, so that the
players could explain their experience and provide feedback. The 5-point
numeric rating scale is defined in that; 4 is ”always agree”, three is
”sometimes agree”, two is ”neutral”, one is ”sometimes disagree”, and 0 is
”always disagree”. The questions were listed in order from less engaged to
more engaged ones. The answers provided for part 1 of the questionnaire were
used to perform a psychometric analysis to determine the validity of the
measurements. The part 2 of the questionnaire will be analysed and used to
develop a beta version of the game in the future work.
Part one of the Engagement Questionnaire (EQ):

  1. 1.

I feel spaced out (Absorption)

  2. 2.

I feel intimidated (Absorption)

  3. 3.

I get wound up (Flow)

  4. 4.

The game kept me on my toes but did not overwhelm me (Absorption)

  5. 5.

My actions seem to happen automatically (Presence)

  6. 6.

I play without thinking about how to play (Flow)

  7. 7.

I get into the game (Absorption)

  8. 8.

I am so involved in what I am doing and cannot see myself separate from what I
am doing (Flow)

  9. 9.

If someone talks to me, I don’t hear them (Flow)

  10. 10.

I lose track of time (Absorption)

  11. 11.

I feel different (Absorption)

  12. 12.

When moving through the game world, I feel as if I am there (presence)

  13. 13.

I experience feelings as deeply in the game as I have in real life (Presence)

  14. 14.

When playing the game, I feel as if I am an important participant (Presence)

  15. 15.

I felt competent and effective (Presence)

  16. 16.

Playing makes me feel calm (Flow)

EQ-part two, answer the questions below in your terms:

  1. 1.

What do you think about the appropriateness of the length of time the
rehabilitation took?

  2. 2.

During the rehabilitation session, did you focus on the remaining time?

  3. 3.

How exhausted do you feel?

  4. 4.

How difficult did you feel today’s session was?

  5. 5.

How interesting did you find the game?

  6. 6.

How quick did the time pass during the session?

  7. 7.

How difficult was it to focus on the game?

  8. 8.

How difficult was it to play the game?

  9. 9.

Do you ever become so involved in the game as if you are inside the game
rather than moving your limbs?

  10. 10.

Please, tell us how we can improve the therapy sessions or the ReHabgame;

###  4.2 Subjects

The pilot testing was conducted with $20$ post-stroke individuals (8 women, 12
men; mean age 54; range 41$-$63 years) who were recruited voluntarily. All
participants were discharged from a hospital, $5.0\mp 1.5$ years ago. The
ethical approval was obtained before starting the study, from the University
Computing and Technology Department’s Research Ethics committee. First, we
described the study procedure and pattern of engagement and invited
participants to read and sign an informed consent form. The study was
continued for eight consecutive weeks in that player’s joint velocity,
acceleration, a ROM, head and spine tilt and hand gestures were collected and
saved on the hard disk. The EQ was designed and administered post-play to
determine the player’s engagement in the game.

###  4.3 Procedure

The upper limbs (arm/hand) are designed to perform very skilled movements
which allow us to do day to day activities. According to the therapist, to
allow us to perform all of the intricate activities, the brain is wired to the
upper limb for speed and accuracy [53]. This process makes it vulnerable to an
injured person, and consequently, rehabilitation can be slow [37]. The
rehabilitation system has adapted an algorithm to allow the patients to use
their arm and hand in ways that they would otherwise be unable to do so. The
games in this study are the computer-generated simulation of 3D images and
environments that can be interacted with in a seemingly real and physical way
by a player. It does not use any head-mounted equipment such as Oculus/Google
to avoid any motion sickness or discomfort for players (patients). The
player’s action partially determines what happens in the environment. The
games were developed with direct collaboration and consultancy with
physiotherapists and literature [32, 17, 12, 29, 20]. The consultancy was
performed throughout the development process as well as before starting to
build the game. The information was gathered by asking the therapists what is
important to be considered when we design our game and what elements must be
implemented? Why don’t people attend their physiotherapy sessions? Why do they
find it boring? etc. We use the virtual rehabilitation system to promote
rehabilitation and maximise the outcomes of the therapy. It aims to keep the
muscles and joints of the hand and arm as healthy as possible, and this
remains a key focus throughout the rehabilitation. This physiotherapy seeks to
achieve as much function as possible and work towards an individual’s own
goals.

Figure 6: (a) Illustrates the screen shot of the video footage taken from the
player while standing behind the predefined line within $1.8~{}m$ from the
screen and playing the fruit “Reach-Grasp-Collect” game, (b) shows the same
player with the Myo armband in her arm and the video streamed from behind the
player.

Three/four minutes before starting the sessions the Myo armband was fitted to
the participant’s arm to warm-up and synchronise with the game and computer.
Patients then were asked to stand within 1.5$-$2.0 m from the screen facing
the Kinect as illustrated in Fig 6. When the player stands in the Kinect’s
field-of-view, the avatar figures are automatically generated. The
participants were informed that the task is to interact with virtual objects
generated on the screen. The virtual object’s trajectories were stored on the
hard disk which enables the estimation of joint centres using IK. The postural
control or balance was monitored while interacting with the virtual objects
through the avatar and player acts as a controller. The reach tests were
conducted with identical instructions, with the subject advised that the
movement of the player won’t help in reaching object (the avatar’s spine is
locked in a global coordinate system). The players must keep their body and
shoulders straight, show palm to the screen and close fingers for collection.
Both games continued for one hour under the supervision of a medical scientist
and a psychologist to assess the usability of the games for the target group.

##  5 Results and Data Analysis

The ROM and physical parameters, such as joint positions, orientations and
relative angles were captured by the Kinect. The Kinect was used due to its
possibility to enable an efficient and continuing rehabilitation programme in
the home environment. It makes treatment more enjoyable thus increasing
motivation and therefore adherence. The system has been developed so that the
physical presence of a therapist is not required during the session and there
is no need for the player to wear any marker or wires to be connected to
sensors. The inertial measurement unit (IMU) and muscle activity were
collected via the Myo with the blue tooth connection. The data were used by
the algorithm and also saved for off-line analysis. The player receives real-
time feedback on performance as the MCTS simulation incorporates data
efficiently from the player’s previous performances for a subsequent
generation in the game. The motion data from the Kinect and Myo undergo a
process of smoothing to achieve higher accuracy and efficiency. A smoothing
algorithm was used to remove any noise from the signals collected by the
sensors. The angular measurements and accurate linear motions were obtained by
the IMU (gyroscope, accelerometer and magnetometer) sensors to distinguish
precise linear movement from the accelerometer readings. The EQ and ROM data
were collected from each of the study participants. The participants played
the ReHabgame with small breaks between each one to prevent fatigue. All
feedback, suggestions and observations were collected to improve the game and
tested again by the same persons and using the same EQ. The data gathered from
the various stages of the therapy sessions were used for qualitative analysis.
Rasch analysis was implemented using Ministep $4.0.1$ (Winsteps)
software222http://www.winsteps.com/ministep.htm. In 1969, Georg
Rasch333https://www.rasch.org/ proposed a statistical model that complied with
the fundamental assumptions made in measurements in neuroscience and has been
used to aid in the construction and validation of health status questionnaire
in neurology [38, 47, 9].

Table 1: The Rasch analysis of Engagement Questionnaire (EQ) results (part
one). Results of the Rasch rating scale model (RSM)
---
Item |  Item difficulty |  Item Fit (mean squares) | RMSR (root-mean-square residual)
No. |  (Logit) | Infit Outfit |  (observation - expectation)
(3) |  1.13 | 1,48 1.52 |  1.16
(6) |  0.98 | 1.47 1.44 |  1.20
(12) |  -0.37 | 1.31 1.28 |  1.05
(15) |  -0.85 | 1.16 1.23 |  0.85
(4) |  -0.05 | 1.15 1.11 |  1.08
(9) |  -0.05 | 1.08 1.06 |  1.04
(8) |  -0.31 | 0.98 1.04 |  0.93
(16) |  -1.02 | 1.00 0.94 |  0.75
(5) |  0.88 | 0.99 0.92 |  1.01
(1) |  1.25 | 0.97 0.90 |  0.91
(7) |  -0.37 | 0.95 0.93 |  0.90
(2) |  0.64 | 0.88 0.85 |  0.99
(11) |  -1.02 | 0.88 0.86 |  0.70
(14) |  -0.43 | 0.81 0.80 |  0.81
(13) |  -0.31 | 0.79 0.74 |  0.83
(10) |  -0.10 | 0.68 0.68 |  0.82
Item Mean (SD): 45.8 (14.1), and Person Mean (SD): 36.6 (7.3)

In EQ, 16 multiple choice questions were developed with 5-point numeric rating
scale answers from $0$ (always disagree) to $4$ (always agree). Due to the
items’ equal rating scale structure, the Rash Rating Scale Model (RSM) [1] was
used to determine the participants’ engagement in the ReHabgame
444https://www.rasch.org/rmt/rmt94n.htm. Unidimensionality, reliability,
targeting and response categories were examined.
To assess the unidimensionality (i.e. fitness for the Rasch model), a
principal component analysis of Rasch residuals was used in which the observed
response is compared with the predicted response of the model. Under good fit
conditions for rating surveys, infit and outfit mean squares must be within
the range of $(0.6$ to $1.5)$ [24]. The internal consistency of person and
item performance were studied using separation reliability estimates and
separation ratios. Separation reliability for person refers to the consistency
of a person′s responses across items. The item′s separation reliability refers
to the consistency of item performances across persons. Separation ratios
$>(1.5$ to $2)$ provide evidence of internal consistency. Targeting was done
using an examination of the item/person map in which the distribution and
spread of items and persons along an ordinary logit were compared. Targeting
is measured as a tool for participants’ engagement and is defined as the
extent to which items are of appropriate difficulty for the sample or item
endurability to a person’s level of engagement. A logit is a Log-odds unit or
a logarithmic transformation of the ratio of the probabilities of a correct
and incorrect response scale of the latent construct or the probabilities of
adjacent categories on a rating scale. Cronbachs’ alpha (Rasch Person
Reliability) was used to measure the internal consistency between items and
its strength [9]. Item Response Theory (IRT) model or the probability of an
individuals’ response to an item and model item characteristic curve (ICC) or
the relationship between the probability of success to an item were assessed.

###  5.1 Results

The Rasch analysis and RSM are outlined in Table 1. It shows the item numbers
which correspond to the question numbers, the items’ difficulty (Logit), infit
and outfit mean squares and RMSR for each item. Infit and outfit mean squares
values are in the range of (0.68$-$1.52) for all 16 items, which satisfy the
unidimensionality condition. The initial fit analysis statistics for the
individual items show that the easiest items were ”(11): I feel different
(Absorption)”, and ”(16): Playing makes me feel calm (Flow)” with the logits
equal to $-1.02$. The most difficult item was item number ”(1): I feel spaced
out” with the highest logit value $1.25$. RMSR has its highest value of $1.2$
for item ”(6)” and the lowest value of $0.7$ for item ”(11)”. The Ministeps
item/person map is shown in Fig 7. Item/person map would have a good targeting
when the items and persons are clustered symmetrically all around the vertical
axis. The item measures ranged from $-1.02$ to $1.25$ logit and persons’ logit
was in the range of $-1.25$ to $1.64$. Therefore, the Item/person map has a
good targeting of items. The left side of the Fig 7 represented by $"X"$
illustrates the items and the right side are the participants ($P1$-$P20$)
with good distribution to characterise the participants’ engagement.
The Rasch expectation is that individuals with higher engagement in a game
would have a higher likelihood of agreeing with more engaged items in the
categories (higher response category). Fig 8 sketched out the category and
conditional probability curves relative to item difficulty. The peaks of the
curves for the $5$ categories (”always disagree = 0” to ”always agree = 4”)
are always in ascending order along the latent variable. The crossover point
for the category $2$ is within category $1$ and $3$, which means this category
could be collapsed into category $1$ and $3$. The person/item threshold
distribution map is outlined in Fig 9(b) with the x-axes displaying location
or difficulty of item thresholds and the y-axes display the frequencies of
item thresholds and participants. It can be seen that the offset of person to
item suggests targeted scale.
Some of the important findings taken from the second part of the EQ are listed
below. The majority of participants enjoyed the rehabilitation sessions and
the ReHabgame activities. Overall, the participants commented very positively
and reported that the activities covered a good ROM for the upper body. They
suggested that audio feedback could be useful to create a more exciting
environment in the game.
Fig 10a, Fig 10b illustrate the muscle activity during the gameplay collected
by one of the EMG sensors of Myo from two participants while playing the same
game. Fig 10c, Fig 10d shows the elbow rotation data generated in the hard
disk and collected from the Kinect.

Figure 7: Participants’ engagement scores (each x represents one person),
centred at a mean of zero with standard deviations of $1.0$. Items are
arranged in engagement order with $4$ referring to ”always agree” and $0$ to
”always disagree”. Figure 8: The category probability curves for items test
information for engagement in all categories (a) and their probabilities (b).

(a)

(b)

Figure 9: The threshold map with the distribution of person in figure (a) and
item in figure (b).

Some of the feedback & suggestions received from players are listed below:

  1. 1.

”I like to play longer, and I feel it helps me to score better”

  2. 2.

”The game becomes intense occasionally but still motivating, and I like to
improve my score each time”

  3. 3.

”The ReHabgame makes my muscles to be used [and] without realising they are
getting stronger”

  4. 4.

”The positive impact of the game was noticed by third parties (family members
and my physiotherapist)”

  5. 5.

”I found it very interesting. I feel the positive effects of the exercise on
my whole body”

  6. 6.

”My whole body was engaged with the game, and I feel I have more control over
my posture”

  7. 7.

”The difficulty level of the game seemed to match my ability, and I score
better each session”

  8. 8.

”The more I play I find that my finger movements are improving”

  9. 9.

”I like that the gameplay was challenging but not exhaustive”

  10. 10.

”I needed to concentrate a lot, and I couldn’t hear what people were saying
around me”

  11. 11.

”The game helps and improves the upper limb reaction and reduces its
rigidity.”

  12. 12.

”I found the visual feedback very instructive, but adding sound to the game
would make it more stimulating”

  13. 13.

”I would like to be able to compare my performance and score with previous
sessions on graphs”

  14. 14.

”I prefer VR games like the ReHabgame since head-mounted displays in other VR
systems cause motion sickness”

Figure 10: (a) and (b) show data collected from Myo armband of the
electromyography (EMG 1) generated through “Reach-Press-Hold” buttons game”
from two players, (c) and (d) illustrates the elbow joint orientation
collected by the Kinect from the same players.

##  6 Discussion

###  6.1 Summary of main findings

To our knowledge, our study is the first to develop the rehabilitation game
with the capabilities build in the ReHabgame. A Rasch model was developed to
evaluate the participant engagement in the game as well as the system
effective and attractiveness. The developed system enables a therapist to
present a variety of controlled stimuli to measure and monitor the progress of
the participants. The finding showed that the therapy provides an adequate
physical and mental engagement through sufficient visual feedback throughout
the sessions. It gives the opportunity for patients to perform additional
exercises independently. It offers the possibility for a clinician to
recommend an appropriate and individually aligned exercise. The attractiveness
of the ReHabgame was measured by the standard flow, presence and absorption
(psychological model balancing the player’s perceived skill with the perceived
challenge). The effectiveness was assessed to evaluate the physical balance
between fitness (the body’s ability in tolerating exercise), and intensity
(the difficulty of the training on the body). The Rasch analysis in this study
showed that a unidimensional construct was created and fit statistics were
obtained for the measured properties. The ReHabgame successfully calibrated
the ROM of the players, muscle activities, linear and angular motions. The
required movement or exercise level was adopted through the MCTS and the ROG
based on HSS and MAS. In that skill level and game difficulty were gradually
increased to challenge the player.

###  6.2 Strengths and limitations

Our study had some limitations in that the sample of participants were not
fully representative of the diverse population of stroke patients (41 $-$ 63)
years old which was not wide enough to include younger or older people. The
percentage of female participants to male was $0.4$. Additionally, the ethics
approval process to have access to the NHS was cumbersome and time-consuming
therefore we decided to limit our study to the volunteers of communities.

##  7 Conclusion

The feasibility and effectiveness of the ReHabgame were studied as stimuli for
improving the motor performance in a post-stroke participant. It facilitates
autonomous physical rehabilitation at patient’s state as prescribed by the
therapist. This system provided an environment that encourages patients to
build greater strength and endurance on tasks that increase independence and
enrich daily activity. The system monitors the trajectory of the hand during
movements about the shoulder, arm, and elbow. The data taken from the devices
are saved and accessed for further analysis as well as to generate the best
game suitable for a patient with a certain disability through the intelligent
computing system. The algorithm generates objects randomly and monitors the
execution of the exercise, and the system provides direct feedback in real-
time accordingly. The therapy provided an adequate physical and mental
engagement that offered sufficient visual feedback throughout treatment
sessions. The MCTS and ROG algorithms were used to control the intensity of
the physical activity that happened gradually based on the player’s
performance. The data were obtained at devices local sampling frequency
followed by performing linear interpolations to create a constant sampling
interval while maintaining time and frequency domain signal integrity to
achieve an identical sampling frequency to the kinematic data.
The subjective experience was conducted through the EQ to gather further
information from the subjects about their experience of game playing. Some
positive elements of the ReHabgame that was claimed by the player were the
beautiful scenery and appealing feedbacks they received which had a direct
relation to the engagement of a player. It aimed to distract the player’s
attention towards the exciting aspects of the game to avoid fatigue. The
training engages users to allow for the constant intensive practice required
for motor plasticity. It monitors and progressively corrects the abnormalities
of the kinematic movement, coordination and assess the transmitted electrical
signals as the muscles strain. The subjective experience was performed through
the EQ to gather further information from the subjects about their experience
in playing the games. Future work includes experiments involving individuals
with motor impairment, with a specific focus on gait and balance. It also aims
to incorporate the possibility of implementing social interactions through
online gameplay at patient’s home or could be arranged in social communities
by patients carer or charity organisation. That increases the motivation by
gathering more patients to cooperatively (or competitively) interact and play
the game. It could enable a long-term recovery and could lead to better
rehabilitation outcome [17, 32]. The open-ended questionnaire also will be
used for developing the beta version of the ReHabgame in the future work.

## Acknowledgement

The authors acknowledge Anglia Ruskin University for funding, organising and
hosting the pilot study and testing interventions.

## Reference

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