Towards a Virtual Coach for Boccia: Developing a Virtual

Augmented Interaction based on a Boccia Simulator

Alexandre Calado

, Simone Marcutti

, Vinícius Silva

, Gianni Vercelli

, Paulo Novais

and Filomena Soares

Algoritmi Centre, University of Minho, Guimarães, Portugal

Department of Informatics, University of Minho, Braga, Portugal

Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genoa, Italy

pjon@di.uminho.pt, fsoares@dei.uminho.pt

Keywords: Boccia, Virtual Reality, Gesture Recognition, Virtual Coach.

Abstract: Disability can be a factor that leads to social exclusion. Considering that involvement in society is paramount

for a person with disability, participation in sports can be a powerful tool for inclusion. Based on this premise,

the authors propose an intelligent virtual coach for Boccia to encourage the practice of this sport on persons

with disabilities, while promoting social inclusion and shortening the learning curve for individuals new to

the sport by learning about game strategy. The envisioned virtual coach will rely on Artificial Intelligence

models, thus requiring the creation of large datasets, namely for ball placement and throwing movement

recommendations. To answer these problems, this work is focused on the development of a Boccia simulator.

With this simulator, it is possible to generate artificial gameplay images and allow the user to control an avatar

with body tracking. Gesture recognition was implemented with a state-machine, thus enabling the player to

throw the ball, with customizable physics, by performing one of two different throwing movements. This

functionality can allow the recording of data describing the body movement associated with the placement of

the ball in a certain position within the virtual court, which is essential for the proposed recommendation

system.

1 INTRODUCTION

The World Health Organization estimates that, based

on data from 2010, more than one billion individuals

live with some form of disability worldwide.

However, the term “disability” is one difficult to

define due to its complexity. According to the

International Classification of Functioning, Disability

and Health (ICF) (World Health Organization, 2011),

problems with human functioning can be organized in

three areas: impairments, activity limitations and

participation restrictions. Thus, disability can be

interpreted as the difficulties encountered in any or all

the three aforementioned areas, result of the

interaction of health conditions with environmental

and personal factors. Additionally, the United

Nations General Assembly (UNGA) has established

https://orcid.org/0000-0002-2725-1067

https://orcid.org/0000-0002-3549-0754

https://orcid.org/0000-0002-4438-6713

that the condition of disability is a key factor of social

exclusion (Fina, Cera, & Palmisano, 2017).

The extensive work developed by John Pierson

(Pierson, 2009) proposes the following broad

definition for the term “social exclusion”: “Social

exclusion is a process that deprives individuals […]

of the resources required for participation in the

social, economic and political activity of society as a

whole. […] Through this process people are cut off

for a significant period in their lives from institutions

and services, social networks and developmental

opportunities that the great majority of a society

enjoys.”. Moreover, the United Nations (UN) defends

that sutainable development is based on three

dimensions: economic, social and economic (UN,

2013). Considering these remarks, one can

understand the importance of the social aspect in the

Calado, A., Marcutti, S., Silva, V., Vercelli, G., Novais, P. and Soares, F.

Towards a Virtual Coach for Boccia: Developing a Virtual Augmented Interaction based on a Boccia Simulator.

DOI: 10.5220/0009142602170224

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 2: HUCAPP, pages

217-224

ISBN: 978-989-758-402-2; ISSN: 2184-4321

217

life of a person with disability. Thus, it is paramount

to design and implement strategies to counteract the

effect of disability on social exclusion and promote

inclusion.

According to Burchel (Burchell, 2006), the

participation in sports is important for socio-cultural

integration and equity. Furthermore, besides the well-

known health-related benefitis, engaging in sports

can contribute for the empowerment of persons with

disabilites, as long as increasing their independence

However, persons with disabilities often face

barriers concerning participation in sports. According

to Donnely & Kidd (Donnelly & Kidd, 2006), two of

the most common causes for non-participation are the

lack of early experience and the lack of awareness of

how to include these individuals in sports.

Taking into account the aforementioned remarks,

the authors propose a solution based on the game of

Boccia to tackle social exclusion of persons with

disabilities.

Boccia is a strategy-based precision ball game that

has been developed for individuals with cerebral

palsy. However, due to the dynamics of the game, it

can be easily adapted to persons with other types of

motor impairments or even mental ones. Since 1984,

this game is considered a Paralympic Sport, which is

an important fact to point out, since the Paralympic

Games are one of the most relevant elite sporting

events nowadays.

Although Boccia can be played individually, i.e.

one against one, it can be played in teams of two or,

more generally, of three players. Considering that it

can be played in teams, Boccia is a game that

promotes social interaction, as players of the same

team need to communicate between each other to

define a team strategy. Besides, it also promotes

competitivity between teams, which have to stick

together in order to reach a common goal and beat

their opponents.

Mainly due to its social and inclusive aspect,

along with the flexibility concerning rules, the

authors propose to develop an artificial intelligence-

based virtual coach for Boccia, already referenced in

previous work (Calado, Marcutti, Vercelli, & Novais,

2019). The goal is to encourage more individuals with

disabilities to play the game, promoting physical

activity and shortening the learning curve, but most

importantly, this application aims to contribute for the

social inclusion of these individuals using the

dynamics of the sport.

Although it is still a work in progress, the latter

will be based on a virtual assistant, with whom the

player can interact through voice commands, that will

be capable of auto-suggesting the best possible move

for each player (i.e. the best position to place the

Boccia ball), depending on the circumstances of the

game. The virtual coach will also be able to suggest

the most adequate way of performing the throwing

movement, depending on the recommended position

to place the ball within the court. These

recommendations will be made by the virtual coach

via visual and auditory feedback.

However, considering the possible use of deep

learning approaches to build a reliable recommender

system, a high amount of data is needed. Thus, it is

necessary to build a large image dataset containing a

wide variety of game situations in order to train a

model with good generalization properties. Because

Boccia is not as “mainstream” as other sports, such as

football or tennis, it is difficult to acquire a large

number of images from television broadcasts or from

the internet, as there are no Boccia image datasets

available, at least to the best of the authors’

knowledge. This requires serious efforts to build a

good dataset, an issue that was approached in

previous work (Calado, Marcutti, et al., 2019). In the

latter, a virtual Boccia simulator was developed with

functionalities that enable the user to easily place

each Boccia ball in any desired position within the

virtual court and take a screenshot from any camera

angle. This allows the convenient generation of

images containing artificial Boccia game situations,

thus substantially aiding in the construction of a

consistent dataset. However, one must bear in mind

that real game situations must also be present in the

dataset.

The work described in this paper mainly focuses

on new developments made for this simulator

regarding the tracking of the player’s body for

controlling an avatar within the virtual court. To be

more specific, a Microsoft Kinect v2 was used to

capture the player’s body data. The data stream is

then used to make the virtual avatar replicate the

movements performed by the player, allowing the

latter to throw the Boccia balls inside the virtual

court, in a similar fashion as he/she would in real-life.

This new functionality was added to the simulator

with the purpose of understanding what may be the

most appropriate throwing movement to place the ball

in the desired position, inside the virtual court. With

the proper parameters, the simulated throw can be a

good approximation of a real one, thus, this tool can

be used to record data for training models regarding

the throwing movement recommendations made by

the virtual coach.

The present paper is divided in four sections.

Section 2 addresses a literature review regarding

existing virtual coaches and Boccia simulators.

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

218

Section 3 describes the current virtual Boccia

simulator system architecture, along with a preview

of the virtual coach system architecture. Section 4

presents the preliminary results, showcasing the

functionalities of the current implementation of the

simulator. Finally, final remarks and future work are

discussed in Section 5.

2 LITERATURE REVIEW

The concept of a virtual coach is no novelty, the idea

of a personal device that can monitor, motivate and

teach the user is very attractive for individuals who

want to improve their fitness or their general health

but do not want to hire a personal trainer, whether due

to the cost or due to shyness. According to the review

from Lowe et al. (Lowe & ÓLaighin, 2012), some of

the virtual coaching systems that are available in the

market can be divided into three categories:

smartphone applications, sensors devices and image

processing devices. Smartphone applications use

some of the sensors that are typically integrated in a

smart phone (e.g.: GPS and inertial sensors) for

monitoring physical activity, such as Google Fit and

RunKeeper. Sensor devices can be considered

systems that use a central controller, such as a

smartphone, wristband or other embedded

controllers, and external sensors. Nike+ and Polar

systems are some of the existing solutions that fit in

this category. Finally, image processing devices use

cameras or similar solutions to monitor the

movements and position of the user’s body during

physical activity, such as “Your Shape: Fitness

Evolved”, a game for Xbox 360 that uses Microsoft

Kinect. In general, these virtual coaching systems can

provide feedback to the user during or after the

exercise, along with the possibility of creating a

training program. Taking this into account, the system

proposed in this work can be considered to fall into

this category.

Besides the systems that can be found in the

market, several other examples of virtual coaches

regarding its application for physical activity

motivation and monitoring can be found in the

literature. For instance, Watson et al. (Watson,

Bickmore, Cange, Kulshreshtha, & Kvedar, 2012)

developed an internet-based virtual computer agent

with the goal of increasing the level of physical

activity on overweight or obese individuals.

Ruttkay et al. (Ruttkay & Van Welbergen, 2008)

implemented an intelligent virtual agent that, besides

presenting a sequence of exercises to be performed, it

provides feedback based on utterances and

animations to correct the exercise specific

movements in real-time and also to motivate the user.

Furthermore, it can also adjust its tempo to be in sync

with the user. The results of a try-out showed that the

users got engaged with this virtual coach, however,

the feedback could be misleading or too general and

the speech quality, along with the aspect of the avatar,

should be improved. These are remarks to be taken

into account when designing a similar system, in the

sense that the interactions between the virtual coach

and the user should resemble real interhuman

interactions in order to motivate the user.

Additional approaches for correcting technical

movements by a virtual agent were explored in other

works, such as the one by Kelly et al. (Kelly, Healy,

Moran, & O’Connor, 2010), whom used a virtual

coaching environment to improve the golf swing

technique. In this case, the authors used forty-four

reflective spherical markers (forty-one placed on

anatomical landmarks and three on the golf club) to

record the subject’s golf swing movement in 3D, by

using an infra-red motion capture system. A 3D

virtual avatar mimicking the subject’s swing

movement is then aligned and compared to the

movements of a “coach” 3D virtual avatar, which are

based on the average positions of multiple skilled golf

players. Additionally, graphs can be used to analyse

the joint angles throughout the swing movement and

examine the differences between the “apprentice” and

the “expert” player. Overall, both features can help

the user to learn how to achieve the correct golf swing

technique by adjusting his/her movement to a virtual

coach.

Eyck et al. (Eyck et al., 2006) claims that the use

of a virtual coach is capable of increasing the athletes’

interest and enjoyment. Furthermore, it is a

controlling factor in the sense that individuals

increase their physical activity level to receive

rewards and avoid negative consequences. However,

further research is necessary to assess the long-term

effect of this type of systems.

Regarding Boccia, as far as the authors’

knowledge, no virtual coaching system was found in

the literature. The same was observed for similar

sports, such as pétanque, bocce or lawn bowls.

However, several Boccia virtual simulators were

found, which is closely related to the main focus of

this work.

For instance, the Centro de Referencia Estatal de

discapacidad y dependência in León, Spain, a social

services centre focused on aiding persons with severe

disability and dependence, has developed a Boccia

simulator videogame to facilitate and promote sports

practice (CRE Discapacidad y Dependencia, 2016).

Towards a Virtual Coach for Boccia: Developing a Virtual Augmented Interaction based on a Boccia Simulator

219

The game explains the Boccia rules and allows the

user to throw the ball using a Nintendo Wii remote.

Besides, it is possible to choose between three levels

of ball hardness/softness, considers the different

athlete categories and includes the option of using a

ramp for launching the ball.

A company called Preloaded, in a collaboration

with Channel 4, developed a 3D Boccia simulator for

the general public in order to raise awareness for the

Paralympic Sports (Edwards, 2012). It is a very

complete videogame which is regularly used to teach

schools about Boccia, and it features three modes:

quick play, tournament and arcade. Quick play allows

the user to play a game against another player on the

same machine or against the computer AI. In

tournament mode it is possible to play against the

biggest names in Boccia, in a simulated tournament.

Finally, arcade mode focuses on increasing skills,

such as target practice.

Another example is the BocciaSim (Osório & Sá,

2008), an open-source project started by two high

school students. It focuses on a simple 2D Boccia

simulator controlled with mouse and keyboard, which

makes it less attractive to be played by users with

disabilities due to its lack of intuitiveness and

immersiveness.

There were also some works found in the

literature regarding this subject, such as the work by

Guedelha (Guedelha, 2007), which presents a basic

version of the Boccia game controlled only by

keyboard and mouse or joystick. Finally, Ribeiro

(Machado Ribeiro, 2017) made efforts to create a

realistic Boccia simulator focused on BC3

classification athletes based on Unreal game engine,

allowing accurate physics and realistic collision and

ballistics.

Considering the aforementioned Boccia

simulators, no results were presented regarding the

effect of their regular usage on the promotion of the

sport or social inclusion. However, the results from a

study done by Barak et al. (Barak, Mendoza-Laiz,

Gutiérrez Fuentes, Rubiera, & Hutzler, 2016) showed

that individuals with severe physical disabilities who

participated in a rehabilitation programme based on

Boccia benefited from a positive effect on their

psychosocial status.

It is noteworthy to mention that previous work by

the authors also used Boccia as a context for

promoting and monitoring physical activity on the

elderly (Figueira et al., 2017; Silva et al., 2018). This

included efforts regarding real-time game scoring

(Calado, Silva, Soares, & Novais, 2019; Leite,

Calado, & Soares, 2018), throwing movement

detection (Calado, Leite, Soares, Novais, & Arezes,

2018) and User-Interface design (Calado, Leite,

Soares, Novais, & Arezes, 2019).

3 SYSTEM ARCHITECTURE

This section describes the system architecture of the

current implementation of the Boccia simulator. The

predicted architecture for the virtual coach (which is

a work in progress) is also presented in order to help

the reader understand the role of the simulator in the

overall system.

3.1 A Preview of the Virtual Coach

Figure 1 depicts a diagram regarding a preview of the

architecture for the virtual coach system. The area-of-

play is captured, in real-time by a camera placed

directly above the Boccia court. A deep learning

model is then used to detect each of the Boccia balls

within the camera Field-of-View (FOV) and sort

them according to colour (red, blue or white), which

was the target of previous work (Calado, Silva, et al.,

2019). The distance between each red and blue ball

and the jack (white ball) is then computed, based on

their respective centroid coordinates, which allows

the mapping of all balls within the area-of-play and

the computation of the score for the current game

situation. This data is then forwarded to each of both

team’s Strategy Learning Algorithm, a model that

computes a recommendation regarding the optimal

position to place the ball, providing team strategy

aided by artificial intelligence.

For each suggested move there is an associated

throwing movement that may facilitate the task of

placing the ball in the desired position. A second

algorithm can recommend a throwing movement that

is more adequate for this task. The output from the

latter can also allow the player, through a virtual

avatar displayed on the Virtual Coach GUI, to see the

representation of the suggested throwing movement,

thus improving training. However, the training of the

two models, regarding ball positioning and throwing

movement recommendations, requires a large amount

of data. This is where the virtual Boccia simulator

plays an important role, as its current features allow

the easy generation of gameplay images and

simulation of throws with realistic ball physics using

real time body tracking data, which is also possible to

be recorded.

The aforementioned recommendations are then

conveyed to the virtual coach per se, an intelligent

virtual agent capable of using the incoming data to

give visual and audio feedback to the player, so the

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

220

Figure 1: Preview of the virtual coach system architecture.

latter can make an informed decision about his/her

next move or learn different types of Boccia

strategies.

The user will be able to dynamically interact with

the virtual coach at any time by using voice

commands and natural language processing. The

virtual coach will be able to convey information to the

player by using a display placed on the players’ chair

that will show the area-of-play covered by the

camera, a virtual avatar, current game score and the

suggested optimal ball position. Additionally,

headphones can be used for audio feedback based on

voice cues.

Moreover, different types of sensors will be used

on the players in order to acquire various bio-signals,

which the behaviour may trigger different virtual

coach responses. Regarding the bio-signals GUI, the

signals acquired by the biosensors, as well as the ones

acquired by the inertial sensors, are stored in a data

base and plotted for each game, or session, so the

coach, or caregiver, can posteriorly analyse the

player’s physiological data and detect any possible

anomalies.

3.2 Virtual Boccia Simulator

The current architecture of the Boccia simulator, the

main focus of this work, can be observed in Figure 2.

This system is composed by a Kinect sensor, a

processing unit, and an external screen for displaying

the graphic interface.

The Microsoft Kinect V2 (Microsoft, 2017) is

used in order to track the user joints position in the

3D space in real-time. The body tracking API outputs

as skeleton model composed of 25 joints. In order to

reliably work with Kinect in several platforms at the

same time, a Node.js application was developed. This

Node.js application, on the Windows OS machine,

can receive the sensor data by using the official

Kinect SDK and transmit the data to other platforms.

The Kinect V2 is used to track the players and to

recognize the two main gestures (underarm throw and

upper arm throw) performed when throwing a ball

during a Boccia match.

Figure 2: Proposed system architecture.

4 PRELIMINARY RESULTS

The Boccia simulator was developed in Unity 3D

game engine (Unity, 2019) and all of the dimensions

used were set according to the real-world measures.

In Figure 3, it is depicted a diagram with the main

functionalities of the simulator. For better

understanding of the Boccia game rules, the

interested reader may refer to the official Boccia

International Sports Federation documentation

(BISFed, 2017).

A “free-camera” mode was implemented in

previous work (Calado, Marcutti, et al., 2019), which

Towards a Virtual Coach for Boccia: Developing a Virtual Augmented Interaction based on a Boccia Simulator

221

Figure 3: Overview of the most relevant features of the virtual Boccia simulator.

allows the placement of Boccia balls anywhere inside

the court and the taking of screenshots. The court is

also customizable by changing the texture of the floor

background by choosing one of twelve available

textures as shown in Figure 3. These customizations

facilitate the extraction of images, augmenting the

data to be used for the training of Artificial

Intelligence algorithms referenced in section 3.1. An

example of a game situation image generated by this

feature can be observed in Figure 4.

Figure 4: Example of an artificial game situation generated

with the “free-camera” functionality.

The new functionalities added to the simulator

allow the user to select an “avatar mode”, which

enables the visualization of a virtual avatar

mimicking the player movements in real-time. The

used avatar was adapted from (Mixamo, 2019),

Figure 5. This allows the recording of body data

describing the movement done by the user to place

the ball in a certain position within the court. In a later

stage, this data can be subsequently used to train the

recommendation system, allowing the user, with the

visual feedback provided by an avatar, to have an idea

of how to perform the throwing movement in order to

place the ball in the desired position.

Additionally, a gesture recognition algorithm

based on a state-machine was implemented (Figure

6), which detects when the player performed, with the

right arm, one of the two most common throwing

gestures in Boccia: underarm and overarm. A state-

machine is a simple model to track the events

triggered by external inputs. This is done by assigning

intermediate states to decide what happens when a

specific input comes, and which event is triggered.

Figure 5: Up: Player performing the underarm throw;

Down: Player performing the overarm throw.

The implemented state-machine has three states.

It is initialized on the idle state and passes to state S1

when the right arm is in a typical position for building

momentum (Position 1 in the diagram), whereas, if

the player’s right arm is in a position that is typically

assumed at the end of a throwing movement (Position

2 in the diagram), the state-machine passes to state

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

222

S2. However, if Position 2 is not assumed before 1.5

seconds have passed after state S1 is activated, the

state-machine returns to idle state (S0).

Figure 6: State-machine used for detecting throwing

gestures from the Kinect body tracking data.

As the state-machine reaches S2, a throwing event

is triggered, and the ball is thrown according to the

direction of the velocity vector of the avatar’s hand

joint at the time instant when state S2 is activated. The

force at which the ball is thrown is calculated as being

the mass of the ball (which is set to 275 g in the Unity

world, in accordance with the game regulations)

times the avatar’s hand joint acceleration, allowing to

simulate the trajectory on the virtual simulator using

physics approximated to reality.

The parameters used for complying with the

conditions set by Position 1 and 2 were based on the

difference between the right hand and shoulder

coordinates acquired by the Kinect.

Furthermore, as depicted in Figure 3, the ball

physics are customizable by the user, which includes

the following parameters used for adjusting the

physics of a rigid body in the Unity’s virtual world:

bounciness, static friction, dynamic friction, drag and

angular drag. Thus, the user can regulate these

parameters to realistically simulate the throwing of

balls with different levels of softness/hardness, as it is

done in the game. Of course, the setting of these

parameters depends on the disability and the

associated level of the player.

Alternative to the “avatar mode”, the user can

select the “manual mode”, where all the parameters

related with ball throwing can be manually set, such

as height, force magnitude and direction.

Additionally, it also provides the option to position

the ball anywhere inside the previously selected box

in the court before throwing it.

5 FINAL REMARKS AND

FUTURE WORK

The World Health Organization estimates that

approximately 15% of the worldwide population lives

with some form of disability. Having a disability can

lead to social exclusion, therefore it is paramount to

ensure that every individual with a motor or cognitive

disability can be able to participate in society, to the

largest extent possible.

Following this idea, the present work suggests the

development of a virtual Boccia coach in order to

tackle these issues. This tool aims to contribute for the

inclusion and empowerment of individuals with

motor or mental impairments. Additionally, a Virtual

Boccia Simulator was developed to easily allow the

creation of different types of game situations for the

training of new players as well as, the virtual coach’s

Artificial Intelligence algorithms.

This work focused on new developments made on

the simulator, namely the “avatar mode”, enabling the

visualization of a virtual avatar mimicking the player

movements in real-time. Moreover, a gesture

recognition algorithm based on a state-machine was

implemented, which is able to detect when the player

performs one of the two most common throwing

gestures in Boccia.

Future work includes a continuous improvement

of the simulator, by tackling the regularity of the

virtual court’s ground to better mirror the real-world

conditions. The Kinect data will be recorded to later

be used to train the virtual coach recommendation

system. Additionally, it is intended to add wearable

devices to the framework since it can provide more

data to the virtual coach. The system level autonomy

should also be increased by adding interactive

machine learning enabling it to learn on the fly. This

will allow the input of knowledge from professionals

into the system in order to better train the virtual

coach, improving the virtual coach recommendations.

ACKNOWLEDGEMENTS

This work has been supported by RISEWISE (RISE

Women with disabilities In Social Engagement) EU

project under the Agreement No. 690874. Vinicius

Silva also thanks FCT for the PhD scholarship

SFRH/BD/SFRH/BD/133314/2017.

Towards a Virtual Coach for Boccia: Developing a Virtual Augmented Interaction based on a Boccia Simulator

223

REFERENCES

Barak, S., Mendoza-Laiz, N., Gutiérrez Fuentes, M. T.,

Rubiera, M., & Hutzler, Y. (2016). Psychosocial effects

of competitive boccia program in persons with severe

chronic disability. Journal of Rehabilitation Research

and Development, 53(6), 973–988.

https://doi.org/10.1682/JRRD.2015.08.0156

BISFed. (2017). BISFed International Boccia Rules (v.2).

Burchell, A. (2006). The Importance of Sport to the

Disabled. The Commonwealth Health Minister’s

Book.

Calado, A., Leite, P., Soares, F., Novais, P., & Arezes, P.

(2018). Real-Time Gesture Classification for

Monitoring Elderly Physical Activity Using a

Wireless Wearable Device. The Ninth International

Conference on Sensor Device Technologies and

Applications, 164–168.

Calado, A., Leite, P., Soares, F., Novais, P., & Arezes, P.

(2019). Design of a Framework to Promote Physical

Activity for the Elderly. In T. Ahram, W.

Karwowski, & R. Taiar (Eds.), Human Systems

Engineering and Design (pp. 589–594). Cham:

Springer International Publishing.

Calado, A., Marcutti, S., Vercelli, G., & Novais, P. (2019).

Developing an Intelligent Virtual Coach for Boccia :

Design of a Virtual Boccia Simulator. 15th AAATE

Conference Global Challenges in Assistive

Technology Conference Global Challenges in

Assistive Technology [In Press].

Calado, A., Silva, V., Soares, F., & Novais, P. (2019). Ball

Detection for Boccia Game Analysis. 2019 6th

International Conference on Control, Decision and

Information Technologies (CoDIT), 1468–1473.

CRE Discapacidad y Dependencia. (2016). Boccia Virtual.

Retrieved February 5, 2019, from

http://www.crediscapacidadydependencia.es/cresan

andres_01/documentacion/bocciavirtual/index.htm

Donnelly, P., & Kidd, B. (2006). Literature Reviews on

Sport for Development. Sport for Development &

Peace. International Working Group, 1–195.

Edwards, P. (2012). Boccia. Retrieved February 5, 2019,

from https://preloaded.com/work/boccia/

Eyck, A., Geerlings, K., Karimova, D., Meerbeek, B.,

Wang, L., IJsselsteinjn, W., … Weterink, J. (2006).

Effect of a Virtual Coach on Athletes’ Motivation.

International Conference on Persuasive Technology

2006, 158–161.

Figueira, C., Silva, J., Santos, A., Sousa, F., Silva, V.,

Ramos, J., … Arezes, P. (2017). iBoccia: Monitoring

elderly while playing Boccia gameplay. ICINCO

2017 - Proceedings of the 14th International

Conference on Informatics in Control, Automation

and Robotics, 1(Icinco), 670–675.

https://doi.org/10.5220/0006473606700675

Fina, V. Della, Cera, R., & Palmisano, G. (2017). The

United Nations convention on the rights of persons

with disabilities: A commentary. In The United

Nations Convention on the Rights of Persons with

Disabilities: A Commentary.

https://doi.org/10.1007/978-3-319-43790-3

Guedelha, H. (2007). Boccia , o simulador. 96.

Kelly, P., Healy, A., Moran, K., & O’Connor, N. E. (2010).

A virtual coaching environment for improving golf

swing technique. Proceedings of the 2010 ACM

Workshop on Surreal Media and Virtual Cloning -

SMVC ’10, 51.

https://doi.org/10.1145/1878083.1878098

Leite, P., Calado, A., & Soares, F. (2018). Boccia Court

Analisys for Real-time Scoring. Proceedings of the

15th International Conference on Informatics in

Control, Automation and Robotics, 2, 511–516.

https://doi.org/10.5220/0006918305110516

Lowe, S., & ÓLaighin, G. (2012). The age of the virtual

trainer. 9th Conference of the International Sports

Engineering Association (ISEA), 34, 242–247.

https://doi.org/10.1016/j.proeng.2012.04.042

Machado Ribeiro, J. D. (2017). Boccia Game Simulator -

Applications for Training Cerebral Palsy Patients.

Faculty of Engineering of the University of Porto.

Retrieved from

https://paginas.fe.up.pt/~ee11264/Tese/assets/JoseD

iogoRibeiro_Dissertacao_MIEEC_VersaoProvisori

a.pdf

Microsoft. (2017). Developing with Kinect.

Mixamo. (2019). Mixamo.

Osório, B., & Sá, P. (2008). BOCCIASIM. Retrieved

February 5, 2019, from http://boccia-

sim.blogspot.com

Pierson, J. (2009). Tackling social exclusion. Routledge.

Ruttkay, Z., & Van Welbergen, H. (2008). Elbows higher!

Performing, observing and correcting exercises by a

virtual trainer. Lecture Notes in Computer Science

(Including Subseries Lecture Notes in Artificial

Intelligence and Lecture Notes in Bioinformatics),

5208 LNAI, 409–416. https://doi.org/10.1007/978-3-

540-85483-8_41

Silva, V., Ramos, J., Soares, F., Novais, P., Arezes, P.,

Sousa, F., … Santos, A. (2018). iBoccia: A

Framework to Monitor the Boccia Gameplay in

Elderly. Lecture Notes in Computational Vision and

Biomechanics, 27. https://doi.org/10.1007/978-3-

319-68195-5

UN. (2013). World Economic and Social Survey 2013:

Sustainable Development Challenges. In United

Nations, Department for Economic and Social

Affairs.

https://doi.org/10.1016/j.urolonc.2009.06.002

Unity. (2019). Unity Real-Time Development Platform |

3D, 2D VR & AR Visualizations.

Watson, A., Bickmore, T., Cange, A., Kulshreshtha, A., &

Kvedar, J. (2012). An internet-based virtual coach to

promote physical activity adherence in overweight

adults: Randomized controlled trial. Journal of

Medical Internet Research, 14(1).

https://doi.org/10.2196/jmir.1629

World Health Organization. (2011). WORLD REPORT ON

DISABILITY.

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

224