Towards tailoring player experience in physical Wii games: a case study on relaxation

In this study we construct an artificial neural network model of players’ relaxation preferences while playing a physical Wii game. Developed technology will assist game designers to automate a part of the game design and balancing features, and create physical Wii games with adaptive experiences for the player. The model is trained on data derived from the player-Wii interaction which include physiological response, Wii Remote gesture and game data. In this study the developed relaxation model proved to achieve a highest classification accuracy of 78.42%. Furthermore, the restriction of input data to Wii Remote specific features and the possibility of using this model for tailoring the player experience are discussed. Categories and Subject Descriptors I.2.1 [Artificial Intelligence]: Applications and Expert Systems – Games.

• Type: Conference
• Year: 2009
• Where: ACMACE
• Authors: Patrick Jarnfelt, Sebbe Selvig, Dajana Dimovska
• Keywords: ACMACE 2009, Computer Entertainment Technology, Physical Wii Games, Wii Remote, Wii Remote Gesture