Affective VideoGames

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Affective VideoGames
Coordinator:
Tutor: SimoneTognetti (tognetti@elet.polimi.it), AndreaBonarini (andrea.bonarini@polimi.it), MatteoMatteucci (matteo.matteucci@polimi.it)
Collaborator:
Students: AndreaTommasoBonanno (andreat.bonanno@gmail.com)
Research Area: BioSignal Analysis
Research Topic: Affective Computing
Start: 2008/11/14
End: 2010/12/23
Status: Active
Level: Ms
Type: Thesis

Project description

In this project we try to develop a software that can adapt a behaviour of an interactive videogame (a car game like TORCS or a similar parametrizable game), to do this we will use some measure based on excitement of the player, analizing biological signal to valuate the emotional state. The goal is to maximize the fun and reducing the boring of the player, adapting the difficulty of the videogame.

The first part of the project consisted of the analysis of a set of VideoGames. In particular three different available open source games have been studied in order to receive feedbacks from the player (in terms of excitement), finding out which one is the most suitable for the goal of the project: the one potentially affecting most the player's emotions. The games that have been analyzed for this first stage of the project have been the following:

- a puzzle/action 3d game called Beaver Valley;

- TORCS (driving simulator);

- the music video game called Frets on Fire.

TORCS has been selected and experiments with it have been done, leading to the definition of a methodology to face experiments.

Following this methodology, in January 2010 a new set of experiments will be performed with volunteers.

In order to perform this study the ProComp sensing system has been used in order to get biological signals and then to evaluate the emotional state of the player.

More details on the project are available under the discussion tab.

Students that worked on the project in the past

Laboratory work and risk analysis

Laboratory work for this project will be mainly performed at AIRLab/DEI. The only risks are related to the use of computers and data acquisition devices