Exploring user motor behaviour in bimanual interactive video games

Video games have proved very valuable in rehabilitation technologies. They guide therapy and keep patients engaged and motivated. However, in order to realize their full potential, a good understanding is required of the players' motor control. In particular, little is known regarding player behaviour in tasks demanding bimanual interaction. In this work, an experiment was designed to improve the understanding of such tasks. A driving game was developed in which players were asked to guide a differential wheeled robot (depicted as a rocket) along a trajectory. The rocket could be manipulated by using an Xbox controller's triggers, each supplying torque to the corresponding side of the robot. Such a task is redundant, i.e. there exists an infinite number of input combinations to yield a given outcome. This allows the player to strategize according to their own preference. 10 participants were recruited to play this game and their input data was logged for subsequent analysis. Two different motor strategies were identified: an "intermittent" input pattern versus a "continuous" one. It is hypothesized that the choice of behaviour depends on motor skill and minimization of effort and error. Further testing is necessary to determine the exact relationship between these aspects

Modelling player preferences in AR mobile games

© 2019 IEEE. In this paper, we use preference learning techniques to model players' emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players' frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players' experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed