A Balance Training Game Tool for Seniors using Microsoft Kinect

and 3D Worlds

Michail Chartomatsidis and Christos Goumopoulos

Information and Communication Systems Engineering Department, University of the Aegean, Greece

Keywords: Exergaming, Seniors, Microsoft Kinect, Motion-based Exercise Game, Berg Balance Scale, Human Centered

Design, Ambient Assisted Living.

Abstract: Exercising through gaming (exergaming) based on commercial gaming platforms has been popular over the

past years for fitness improving and balance training both for healthy people and for mobility rehabilitation

purposes. For the elderly people, exergaming can provide motivation for increasing physical and cognitive

activity and as a result improvements on posture balance, physical strength and mental well-being may be

expected. However, an issue that arises is whether such gaming systems that are addressed to the general

population following a fit-for-all design approach are appropriate for seniors. In this paper we address this

issue by developing a new game based on the Microsoft Kinect sensor and creating an engaging three-

dimensional world by using a popular 3D game engine. A human centered design approach is followed by

involving main stakeholders in the development process for achieving an effective and user-friendly balance

training. An evaluation of the proposed game tool with twelve seniors has been performed assessing system

usability as well as the balance training efficacy measured by the Berg Balance Scale and the 30 seconds sit-

to-stand test. The analysis of the feedback provided by the users and the performance statistics exported by

the system indicates a positive acceptance and the potential for promoting health in older adults through

balance improvement.

1 INTRODUCTION

Exercise games (known as exergames) appear to be a

promising approach for home-based balance and

strength training for healthy elderly (Lamoth et al.,

2011). Exercising through games can encourage

people to train and by performing game scenario tasks

they can train both mental and physical skills. In this

direction, there is positive evidence that exercise

programs that combine balance training and muscle

strengthening and coordination can reduce falls and

fall risk in the elderly (Sherrington et al., 2011).

During the past years off-the-self gaming

consoles like Microsoft Kinect XBOX 360, Sony

Playstation Eyetoy and Nintendo Wii have been

pervasive. Such systems have introduced a new style

of physical interaction based on gestures and full

body motions and have been used for training balance

and improving fitness for healthy elderly (Van Diest

et al., 2013) as well as for medical purposes as

rehabilitation tools (Goble et al., 2014).

However, the issue that arises is whether such

gaming systems that are addressed to the general

population following a fit-for-all design approach are

appropriate for seniors. Usability studies with the

participation of seniors have found that popular

commercially available games are not necessarily

suitable for seniors due to their complex interface and

game structure (Gerling and Masuch, 2011). Negative

feedback when the players frequently fail to perform

game tasks because their movements are slower than

expected by the game have been also reported (Lange

et. al, 2009). Many exergames are inappropriate for

balance trainning because they are not properly

designed for controlled movements of seniors body

centre of gravity (Sugarman et al., 2009) and their use

can cause injuries (Bateni, 2012). Furthermore,

commercial gaming platforms are not flexible enough

to provide exergame personalization taking into

account the specific needs of an individual and their

cost is considerable.

In this paper we present the design and

development of a game tool that combines Microsoft

Kinect technology to capture body movements, Unity

graphics engine to create a 3D world for game scenes,

a software module to manage game logistics and

Chartomatsidis, M. and Goumopoulos, C.

A Balance Training Game Tool for Seniors using Microsoft Kinect and 3D Worlds.

DOI: 10.5220/0007759001350145

In Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), pages 135-145

ISBN: 978-989-758-368-1

135

special tailored games for active balance training of

seniors. Following a human centered design aproach

and a set of proper design guidelines that take into

account the special needs of seniors the aim was to

provide a gaming tool that is both enjoyable to use

and has a practical impact on improving seniors

balance. To assess the usefulness of the tool a pilot

study has been performed with the participation of 12

seniors for a period of five weeks using evaluation

metrics such as the Berg Balance Score (BBS), the 30

seconds sit-to-stand test and a questionaire for

assessing usability factors. The qualitative and

quantitative analysis of the pilot data shows that this

tool can be used to assist seniors in improving their

balance in an enjoyable and engaging manner.

The rest of the paper is organized as follows.

Section 2 presents related work. Sections 3 and 4

discuss the game design process and the details of

system development, respectively. Section 5

discusses game tool evaluation in terms of assessing

system usability and the balance training efficacy.

Finally, our conclusions and suggestions for future

work are given.

2 RELATED WORK

The development of special purpose computer games

to assist the elderly, mostly for memory training, can

be traced back in the 1980s (Weisman, 1983). When

the motion tracking sensors were introduced a new

wave of research was initiated. Exergames in the field

of Ambient Assisted Living (AAL) have been

targeted to enable the elderly to remain physically and

mentally fit through engaging game activities as well

as for rehabilitation purposes through specially

designed balance exercises (Korn et al., 2013).

Several studies have explored the appropriateness

of commercial game consoles for balance training of

seniors (Van Diest et al., 2013). In particular the

suitability of Microsoft Kinect sensor on enhancing

physical exercising and performing rehabilitation

protocols has been explored by Mousavi Hondori and

Khademi (2014). Their review indicates that Kinect

is an adequate device for balance exercising and

monitoring of the elderly.

A training intervention program was performed in

a quadruplet of care centers in Australia based on the

Wii Fit bowling game played in Nintendo Wii

(Chesler et al., 2015). Seniors were invited to play the

game twice a week for six weeks with the support of

a caregiver. The study indicated that exergaming can

have a positive impact both on physical and

psychological well-being assessed through relevant

scales. The training program provided opportunities

for social interactions between the participants when

the game was played in groups affecting positively

the sense of belonging.

Although the use of commercially available

games is promising for the balance training of seniors

there is a strong evidence that the development of

specially designed games in a process involving all

major stakeholders (i.e. elderly, caregivers,

physiotherapists and developers) can serve more

efficiently exercising intervention goals (Brox et al.,

2017).

A research study performed with elderly in Japan

designed and evaluated four exergames developed on

the Microsoft Kinect platform with a goal to improve

seniors’ strength, balance and mobility (Sato et. al.,

2015). The games entailed movements such as

grabbing virtual objects using both arms, placing the

feet along a straight line, bending knees and hips,

crouching and standing on one leg. The users had to

perform movements in the context of a game scenario

while tasks were becoming more complicated based

on the game’s level of difficulty. The intervention

brought an improvement in daily walking movements

as measured by the Berg Balance Scale.

Similar exergames in an AAL environment were

examined by Brauner and Ziefle (2015). Their study

focused on assessing factors affecting user

performance and technology acceptance. Various

factors were explored such as technology skills,

accomplishment orientation, playing frequency, age

and gender. The principal factor for performance

prediction was determined to be the participants’ age,

whereas for technology acceptance, in terms of

intention to use such exergames, was the playing

frequency. Playing frequency was defined based on

the frequency the elderly would play games (e.g.,

cards, board games, bowling, etc.) in their daily life.

The proposed approach in this paper shares

similar goals with the related work and embraces the

perspective of developing tailored exergames based

on a human centered design approach to identify

requirements that are closer to the motor and

cognitive abilities of seniors. On the sensor

technology side instead of the XBOX 360 device the

newer XBOX One Kinect device is employed.

Besides providing higher resolution more skeleton

joints can be tracked (e.g., thumb joints and hand tip)

which allows for identifying more movement

combinations. On the game side, further to the basic

movements that were used in previous studies, the

ability for the user to walk is provided. Moreover, a

three-dimensional world was created using Unity 3D

game engine embellished with narrative features

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through animations, sound and visuals for achieving

a more realistic game experience. Although the game

requires the use of the Kinect sensor to identify user’s

movements, there is no dependency on the

corresponding game console, thus keeping the cost of

the necessary hardware low.

3 HUMAN CENTERED DESIGN

The proposed game tool development was based on

the human centered design (HCD) approach (Wever

et al., 2008). Under the HCD principles the game

design followed an iterative process starting with

main stakeholders meetings and user focus groups to

identify possible interaction activities and basic game

mechanisms pertinent to the intervention goals of the

game. Face-to-face meetings between game

designers, seniors, medical professionals and

caregivers allowed for acquiring both qualitative data

through brainstorming activities and quantitative data

through usability questionnaires to refine the main

game characteristics and define the body movements

that will be trained throughout the game.

After the initial design was formed a rapid

prototyping of game ideas and mechanisms was

delivered that was evaluated by the users. User

feedback on the design and game concepts was

analyzed by the development team to determine

prototype appropriateness. A redesign cycle was

followed with refined game rules and interactions

before the final prototype development commenced.

The involvement of seniors under HCD

guidelines from game requirements analysis and

design to evaluation was essential in order to

adequately capture their preferences and needs. The

iterative user feedback expected to deliver a game

tool that will be both useful in terms of balance

training and enjoyable in terms of playing experience.

3.1 Design Guidelines

A number of design guidelines for exergames have

been gathered from reviewing related research

(Gerling et al., 2012; Planinc et al., 2013) and

feedback by the elderly people in the HCD process.

Physiological constraints: The design of the game

must take into account the target audience physical

capabilities. Ageing will often lead to both cognitive

and physical negative changes, like decline in

memory, balance and physical strength. In that sense,

the game structure must avoid unsuitable movements

that may cause injuries and should allow longer

reaction times. Also, it would be easier for the seniors

to deal with only a single task each time.

Game theme. The theme of the exercise game

should be related to real-life activities that are

familiar to elderly people. Themes that are associated

to natural life such as walking in a forest, picking

apples and fishing are more acceptable than artificial

settings found in commercial video games.

User interface. To concentrate on the actual

exercise the user interface should be simple and easy

to use. All instructions have to be clear and use

common language. The interface should have

diﬀerent alternatives for multimedia presentation,

such as, text, voice and images. For those who are

visually impaired, for example, an audio presentation

might be preferable.

Provide Instructions: Learning the game

movements before starting the actual game should be

provided as a choice to the users. Once those

instructions are not required any more, users should

have the option to avoid them. Furthermore, it should

not be expected that the user will recall the

instructions, so every time the user wants to start the

game an option to view the instructions should

appear.

Avoid small objects: It is easier for the elderly to

identify large objects rather than small or fast moving.

Positive feedback: Motivating feedback should be

given to encourage play. Constructive feedback

should be given to guide and correct exercises.

Information and feedback should be given when

appropriate, to not disturb the user.

Variety of difficulty levels: For users to keep their

motivation and continue playing, exergames should

include different levels of difficulty. With that users

will be able to test their skills and try to become

better. Also moving to a more difficult level will

make users feel that they accomplished something

good and the game in fact helps them.

User profile: There should be made a proﬁle

where the user’s progress and results can be saved.

3.2 Game Concept

Based on the guidelines mentioned in the previous

section a game called “Fruit Collector” was

conceptualized. The main purpose of the exergame is

to pick up objects that are scattered around the

environment and deliver them to appropriate spots.

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The movements involved in the game design target

improvements in balance and walking abilities.

Furthermore other cognitive properties could benefit

such as memory, attention and synchronization.

Since the exergaming is based on Microsoft

Kinect sensor, the game entailed the design of

gestures and body movements to interact with the

game’s environment. During the HCD process and

interviewing with seniors and domain experts

(orthopedics and physiotherapists) the decision was

to include four activities. Specifically, leaning left

and right to rotate to the corresponding direction, on

site walking to move forward and the hand gestures

open and closed to pick up and drop objects.

It is worth mentioning that in the first game design

cycle there were movements like bending knees and

hips and head leaning but after playtesting these

movements with seniors and given the experts’

feedback these movements were removed as they

could be unsafe for some seniors.

During game design the feedback of the user

focus group indicated a topic close to the seniors’

interests. Thus, the idea was to create a forest with

trees and flowers while in the centre of it a small

village was placed. As for the collected/scattered

objects the decision was to be baskets filled with

fruits of different type. Finally, the brainstorming

indicated that the places to deliver these baskets

should be the houses of the village.

Moreover, different levels of difficulty were

added to the game. Namely, there are three levels

(easy, normal and advanced) and in each level the

player must deliver different amount of baskets to

complete the game; easy requires only two baskets to

be delivered, normal requires four baskets and

advanced requires the complete set, meaning eight

baskets.

A tutorial was added providing simple

instructions on how to perform the body movements

and how to play the game. Every time the player starts

a new game a message is displayed asking whether

the player wants to see the tutorial before proceeding

on playing.

The game does not provide any negative feedback

to the user because as the design guidelines indicated

it is important for the seniors to feel confident while

playing the game and creating stress and anxiety has

to be avoided. On the contrary, when a basket is

delivered a positive message is displayed while an

appropriate sound is played.

After the design process was completed, the game

development was progressed in its final phase as will

be described in the next section.

4 GAME TOOL DEVELOPMENT

Game tool development discussion splits into three

segments. The first refers to the programming of the

Kinect sensor (Kinect SDK version 2, Microsoft), the

second to the creation of the game world using the

Unity graphics engine (release Unity 2017.3.1f1,

Unity Technologies SF Inc., San Francisco, CA) and

the third to the integration of these two technologies.

4.1 Microsoft Kinect

Microsoft Kinect is a motion tracking sensor based on

a depth camera recording technology for skeletal

tracking (Tashev, 2013). The device combines a

monochrome CMOS sensor with an infrared laser

projector allowing users to interact with Kinect

console or computer applications only with gestures,

movements and voice commands without the need of

explicitly handling a controller unit.

For the developed game tool the second version

of the sensor was used (Figure 1) which is equipped

with a richer SDK API, the ability to track more joints

to identify hand states (Figure 2) and tools to record

the motions. Furthermore, the Kinect SDK provides

two machine learning algorithms, AdaBoost and

Random Forest which can be trained to identify

complicated activities. Such activities are recorded

using the Visual Gesture Builder tool.

Figure 1: Kinect sensor v2.

Figure 2: Kinect hand classes.

4.1.1 Activities Recording

For game activities recognition a training process was

followed by using the recording tool and the machine

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learning algorithms of the Kinect SDK. For building

the activity model the Kinect Studio tool was

employed to observe the way the sensor is recording

the environment. This tool facilitated also the

recording of motions and gestures that were used in

the game mechanics. Figure 3 shows, for example, the

recording of leaning right and left motions. Such

motions are stored in the form of a sequence of

frames. A frame is a digitally coded static image

represented as a selected number of tracked body

parts (i.e. joints). A frame rate of 30 fps (a frame

every 0.033sec) was used during activity recording.

Figure 3: User leaning right and left.

During the training process one issue that required

attention was the fact that different seniors have

different body sizes. Thus, the system would not have

been able to identify the gestures and motions if it

would have been trained based on a single person.

Moreover, it is expected that the elderly would not

perform a specific motion in the same way. For

instance it is expected that seniors would not lean left

or right in the same way. To address this issue, five

seniors with different weight/height characteristics

were asked to perform every game activity for

recording and storing the movement patterns as

multiple samples.

4.1.2 Training with AdaBoost Algorithm

In order to start the training process, the pre-recorded

frame files stored during the recording stage were

used. The tuning of the training process entails the

selection of several options. These options depend on

which parts of the body the sensor will track (upper,

lower or both), whether the left and right side of the

user’s body motion differ and whether the activities

are classified as discrete or continuous.

A discrete activity is defined as a Boolean entity

linked with a confidence value of existence. On the

other hand, a continuous activity is associated with a

progress value which allows the tool to track its

progression optionally via several discrete activities.

A continuous activity is more complex and it is used

for motions like dancing or performing certain

exercises. The machine learning algorithm that is

used for discrete activities is the meta algorithm

AdaBoost, whereas for continuous activities the

Random Forest algorithm is used. For the goals of the

game tool it was decided to classify all activities as

discrete. Table 1 shows the selected options for the

basic game motions.

Table 1: Selected options per motion.

Option

Leaning

Walking

Rely on joints in the lower body

False

True

Rely on hand states

False

Right and left side are different

True

Discrete/Continuous

Discrete

The AdaBoost algorithm was used to train the

recognition model for leaning and walking motions.

After importing the recorded files the timestamps

where the user was performing the corresponding

motion were marked. Figure 4 shows the lean left and

walking motions performed by a user while the blue

lines represent the exact timestamp of that motion.

Figure 4: Training with AdaBoost for detecting leaning and

walking activities.

A validation of the trained activity recognition

model was performed in order ensure that the game

tool identifies correctly the user’s activities. The

Visual Gesture Builder live testing option was used.

Figure 5 shows the tool representation of a senior

performing the leaning left motion. The white lines

that are passing through the corresponding windows

indicate the level of confidence for the gesture.

Figure 5: Validation of tracking leaning left motion.

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4.2 Unity Engine – Creating the 3D

Game World

Even though many other similar games reported in

the literature are using a two-dimensional world a

more engaging and realistic experience for the user is

anticipated by playing in a three-dimensional world.

In this section the steps taken to create the 3D game

world are described based on the Unity cross platform

game engine.

The game concept included the creation of a small

village inside a forest. Firstly, the terrain on which all

the game would take place was created. The terrain’s

main color is green embellished with some trees and

flowers in order to create the forest. At the center of

the forest some houses were placed to create the

village. Figure 6 illustrates the final view of the

village and Figure 7 depicts a view of terrain textures.

Figure 6: Overall view of the terrain.

Figure 7: Textures details of the terrain.

The game scenery included a number of game

objects called prefabs in Unity’s terminology. A prefab

can be created by the designer with random game

objects or can be downloaded from the Unity’s asset

store (https://assetstore.unity.com/). Two assets were

used to create the baskets and the fruits respectively

and by combining these two, one composite game

object was created, the basket filled with fruits (Figure

8). In addition, colors were added to the baskets so that

the player can identify them easily.

The game concept and mechanics use Unity’s

particle systems. Particle systems represent the

effects that take place in a game such as explosions or

indicators that help the player move forward in the

game. Following the HCD design guidelines,

indicators were provided in the game scenario so that

the player could identify straightforwardly the objects

that were supposed to interact.

Figure 8: Fruit baskets as game objects.

For example, in order the player to know where to

place each basket a lightning spot was created outside

of a house (Figure 9). Furthermore, each spot was

painted with a specific color to create a bonus option

for the player. When the fruit basket and the spot had

the same color the player would take double points.

Therefore players had a motivation to place each

basket in the same color spot and so the gaming time

could be increased resulting in more user training.

Figure 9: Spot indicators to place the baskets.

Game scenery included also colliders which

helped making the game more realistic. By adding

colliders in the houses, trees and fences, for example,

the player could not pass through a tree or a house

giving to the player the feeling of a real world.

An important step was to create the avatar that

would be controlled by the user. Unity provides the

prefab “First Person character” which has all the

utilities needed to complete this task. Furthermore, a

target in front of the avatar was added (Figure 10).

The purpose of this was to provide an indication to

the player on when the basket could be picked up.

Thus, when the avatar is close to the basket the target

becomes green otherwise the target remains white.

The game world was also enhanced with other

details that, for example, would show the user

information about the time and the scoring. Motivated

messages and sounds were added and played each

time the player was delivering a basket to the house.

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Figure 10: Player’s view and target.

4.3 Integration of Kinect with Unity

Microsoft provides a library with various scripts in

order to combine Unity projects with the Kinect sensor.

The game tool is a combination of scripts from this

library, customized scripts created and C# code added

in order to retrieve data from the activities stored

during the training stage and to interact with the game.

The flow of control starts with the “Body Source

Manager” script which is responsible for activating

and deactivating the sensor and tracking the user. This

script was customized so that the on-line data from

the user’s activities are received and processed.

Processing is done by the “Activity Detector” script.

Activity Detector receives the output from the

“Body Source Manager” script and evaluates it based

on the activities included in the AdaBoost’s training

files. A local database contains all the user’s activities

required to interact with the game. If the user is

performing one of the stored activities a message is

sent to “Kinect Manager” script.

Kinect Manager receives data indicating if the

user is performing a known activity. Specifically, it

evaluates the detection confidence and if this is above

a defined threshold, it allows the activity to be

performed inside the game. For example, if the user

is leaning right and the detection confidence is above

60% then the game avatar is rotating to the right.

5 EVALUATION

The evaluation of the game tool took place at the

Elderly Protection Center in the city of Ptolemaida,

Greece. A group of twelve healthy seniors with an age

between 61 and 85 years participated in the

evaluation study (n=12, 6 male and 6 female,

mean=73±6.3 years). Once informed consent was

obtained the seniors were asked to play the game

twice per week for a period of five weeks. In every

session each senior was playing the game on all

different levels for 25-35 mins. The evaluation was

organized into four stages and during the whole

process a physiotherapist was present for domain-

specific support and a researcher for administration

and technical support. There were no dropouts.

5.1 Data Collection

In the first stage (introduction) an overview of the

technology was given and a presentation of the game

tool was performed (Figure 11). The participants

received information about the research goals and the

scheduled tasks. They were informed about the

Kinect device and its applications. From the

beginning the seniors showed a great interest in the

exergames concept and the supporting technology

although they had no relevant experience in the past.

Figure 11: Introduction to the technology.

In the second stage (baseline balance

assessment) the physical condition of the participants

was evaluated in order to have a baseline before

starting the exergaming. For this purpose, two widely

accepted tests were used: the Berg Balance Scale

(BBS) (Berg et al., 1992) and the 30 Seconds Sit to

Stand Test (30SST).

The BBS test takes about 15 minutes and consists

of fourteen exercises in order to examine and evaluate

balance control. Examples of the test challenges

include (Figure 12): standing for two minutes,

standing unsupported with one foot in front, standing

in one leg, picking up an object from standing

position and moving from sit down to standup. Based

on the participants’ performance for each exercise a

grade between 0 and 4 is given. The total score

determines the balance condition as follows: a score

below 20 indicates poor balance, a score between 21

and 40 indicates fair balance and a score over 40 is

considered good. Table 2 provides the BBS scores per

participant for the baseline stage. The average BBS

score was 49.8 (SD ±0.9) which indicates a good

baseline balance for the study sample.

The 30SST is a simple exercise to assess the

muscle strength of the participants. The senior is

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asked to sit in a chair and stand up as many times as

possible in 30 seconds without any help. Table 3

provides the 30SST scores per participant for the

baseline stage. The averagescore was 13 (SD ±1.7).

Figure 12: Seniors standing on one leg (left photo) and with

one foot in front (right photo) during BBS test.

The overall outcome of the baseline physical

condition assessment was that all participants had

relatively high scores and therefore there was no high

risk of falling during the game.

In the third stage (exergaming) the participants

played the game (Figure 13). In particular, each

senior started with the easy level and continued to the

next one up to the advanced level. During exergaming

various parameters were recorded like the playing

time, the collected points and whether the participant

completed the level or stopped and quit. In the initial

sessions, while all the participants completed the first

and second level, many of them had to stop

prematurely the advanced level due to tiredness. After

some sessions however they were able to complete all

game levels. It is worth mentioning that during this

stage there were requests by more seniors of the

Elderly Center to play the game. They had the

opportunity to play sometimes the game but their

statistics were not recorded because they didn’t

participate in the study from the beginning.

In the fourth stage (post exergaming balance

and usability assessment) the participants repeated

the two balance tests to evaluate the effect of

exergaming in their performance. Table 2 and Table

3 summarize the results by comparing the baseline

and post exergaming scores. The average BBS score

after exergaming was improved to 50.3 (SD ±0.8)

(50% of the participants experienced an improvement

in their balance) while the average 30SST score was

slightly improved to 13.4 (SD ±1.2). Given the good

scores from the baseline stage and the limited

timeframe of the study the overall balance

improvements attained were considered positive.

Figure 13: Seniors playing the game.

Table 2: BBS scores pre and post exergaming.

Participant

Baseline

Score

Post exergaming

Score

Difference

Table 3: 30SST scores pre and post exergaming.

Participant

Baseline

Score

Post exergaming

Score

Difference

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The usability of the game tool and the exergaming

experience and satisfaction of seniors were assessed

using the system usability scale (SUS) and a semi-

structured interview. SUS is a 10-item questionnaire

with five response options from strongly disagree to

strongly agree (Brooke, 1996). SUS items were

adapted to make questions relevant for this study.

Examples of items used are the following:

I think that I would like to use this exergame

frequently;

I thought the exergame was easy to use;

I found that the various functions in this exergame

were well integrated;

I thought there was too much inconsistency in this

exergame.

The participant’s grades for each item were

processed so that the original scores of 0-40 are

converted to 0-100. Except from three seniors who

rated the exergame tool as acceptable (scores 75-78)

all the other scores were above 80. The average SUS

score was 84.3 out of 100, suggesting high user

acceptance (Bangor et al., 2008).

5.2 Data Analysis

The quantitative and qualitative data collected during

the study were analyzed to identify the impact of the

proposed exergame.

Statistical analysis using Wilcoxon signed-ranks

test (due to non-normality of the data) for paired

samples and a level of significance (a=0.05) was

applied to compare the BBS and 30SST scores

between pre and post exergaming. The results shown

in Table 4 indicate that the BBS score improvement

between the pre and post exergaming periods is

statistically significant (p < 0.05), whereas the 30SST

score improvement is statistically marginally

significant (p=0.05). The walking and leaning

motions included in the exergame design could

explain the improvements as these movements

contribute in maintaining both motor and balance

function.

Table 4: Statistics of BBS and 30SST.

Metric

Pre

Post

Diff

p-value

BBS

49.8 (±0.9)

50.3 (±0.8)

0.5

0.007

30SST

13 (±1.7)

13.4 (±1.2)

0.4

0.05

The interview with the seniors showed that 100%

of the participants found the exergame to be

enjoyable, 80% thought that the movements were not

complicated but easy to remember and 90% of them

expressed their expectation to use the exergame after

the study. Positive comments were provided for the

game theme and the extensionality of the 3D textures

as well as the seamless navigation of the game

through the player avatar. Comments also provided

that their confidence on the technology increased due

to their experience with the game tool. The interview

with the expert gave the feedback that the use of the

game not only helped the seniors to improve their

physical state but contributed to the improvement of

their psychological and emotional state as they were

happy when playing the game and throughout the

duration of the study there was a positive feeling and

anticipation towards the planned activities.

The performance statistics exported by the system

indicated an improvement on the game completion

time per level throughout the timeframe of the study.

In particular, for the easy level the average

completion time for all participants was reduced from

358 to 254 secs (29.1%), for the normal level from

540 to 485 secs (10.2%) and for the advanced level

from 1000 to 917 secs (8.3%). Figure 14 illustrates

the progress of the average game completion time

throughout the study for the three game levels.

Figure 14: Average game completion time progress.

All the participants expressed high willingness to

use the exergame for improving their physical

condition. This situation was reflected both in their

SUS score and during the interview discussions.

Another indicator of participant’s interest towards the

game is given in Figure 15 which shows the number

of the baskets the players delivered in the advanced

level. In the first two weeks due to physical tiredness

the average baskets delivered was less than the

threshold to complete the level. However, from week

3 until the end of the study all the participants were

able to complete the advanced level.

Limitations of the current study are

acknowledged. The limited number of participants

and the short evaluation timeframe prevent the

justification of more sound results. Achieving

substantial balance improvements in elderly requires

playing the games over 3 times per week for at least

3 months (Agmon et al., 2011). In comparison, the

200

300

400

500

600

700

800

900

1000

Completion time (secs)

Easy Normal Advanced

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143

duration of this study was 5 weeks with 2 game

sessions per week. Although the BBS score was

improved after the exergaming intervention the lack

of a control group leaves a vagueness whether the

source of improvement was the exergame alone.

Figure 15: Average number of delivered baskets in the

advanced level.

6 CONCLUSIONS

This paper argued and provided evidence that

exergames for seniors, if properly designed, can be an

enjoyable tool for balance training leading to

improvements in physical health conditions.

Using the Kinect technology for exergame

development enables the unobtrusive sensing of

gestures and motions integrated in the game scenario

while providing seniors with a natural way to validate

their movements through the tool feedback. Given the

low cost of the sensor device and its portability the

tool can be deployed and used in settings ranging

from homes to facility centers supporting the elderly.

Furthermore, developing such games using a

three-dimensional rather than a two-dimensional

world makes such systems more realistic and thus

more attractive to use by the elderly.

Work in progress includes adding more features

to the game tool such as multiplayer mode where

seniors could either play against each other or

preferably collaborate to complete the game faster. In

addition, daily missions (such as “Deliver two baskets

under x min”) would increase challenges for more

demanding users. Leveraging on game data analysis

and learning techniques a research direction to be

explored is the automatic adaptation of game

variables based on additional context such as

environmental attributes and individual user

characteristics. Finally, an evaluation study with a

larger sample of seniors, including a control group,

and for a longer period of time would provide a more

sound justification of the present results.

ACKNOWLEDGEMENTS

The evaluation took place at the Elderly Protection

Center in the city of Ptolemaida, Greece. The authors

wish to thank the volunteers that took part in the

evaluation as well as the medical experts for their

valuable contribution in this study.

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