• Project
  • Completed

Increasing accessibility of online board games to blind and visually impaired people via machine learning

Giorgio Stefano Gnecco - Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences

Giorgio Stefano Gnecco Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences

Giorgio Gnecco is a Professor at IMT School for Advanced Studies Lucca in Italy, working in the Analysis of compleX Economic Systems (AXES) research unit. His current research focuses on the theory and applications of machine learning, artificial neural networks, Big Data and game theory, as well as optimization applied to economics and engineering. 

  • Project start date :
    2022/01/03
  • Status :
    Completed
  • Research organization :
    IMT School for Advanced Studies Lucca
  • Team :
    Dr Davide Bottari (IMT School for Advanced Studies Lucca), Dr Barbara Leporini (CNR – ISTI), Prof. Antonio Camurri (University of Genoa – DIBRIS)

In 2022, Game in Labs selected Prof. Giorgio Gnecco’s project to study the use of machine learning techniques to improve the accessibility of online board games for blind and visual impaired people. Using a literature review and a concrete case of machine learning application, this project aims to develop a better human-computer interaction and improve access to board games.

Project overview

Playing board games has proven beneficial for player integration and socialization. In the case of blind and visual impaired people, it has been established that increasing autonomy through board games has positive effects on social interactions, quality of life and personal development. Some mechanisms exist to improve accessibility in digital games, in particular online board games, for these people. However, their ability to play independently remains limited. Therefore, the objective of this project is to help blind and visual impaired people play online board games, improving their interaction with the screen based on machine learning and sensory substitution techniques. 

Increasing accessibility of online board games to blind and visually impaired people via machine learning

Methodology

The main methods used are advanced computer programming and machine learning. These techniques make it possible to follow and adapt players’ interactions with a normal or touch screen in real time, by automatically recognizing the position of the cursor or finger on the screen and providing audio textual feedback on the positions of game pieces, for example. These methods are applied to the online version of the board game Quantic, hosted on Board Game Arena, to offer a version adapted to blind and visual impaired people. 

Outcomes

In addition to the accessibility of the website hosting an online board game, adding descriptive texts and auditory feedback on game elements can help improve accessibility for blind and visual impaired people. The possibility of applying these parameters depends on the complexity of the components and mechanics of the digitized game. 

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