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

ABSTRACTIn 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.