User modelling for adaptive training in high performance driving

User model creation is a fundamental component for the development of intelligent personalised systems. This thesis proposes an adaptive user modelling framework that uses a combination of unobtrusive task-related and physiological data with the aim of identifying strengths and weaknesses in user performance in the defined task. The research is focused on utilising the framework to provide personalised content adaptation in car racing games. Our system adopts concepts from the Trace Theory (TT) framework, and uses machine learning techniques to extract specific features from the user and the game. These metrics are then transformed and evaluated into higher level abstractions such as experience, exploration and physiological attention by utilising the educational theoretical frameworks of Flow and Zone Theory. The end result is to provide new game paths utilising the user’s model. We demonstrate that this procedural generation of user-tailored content drives the self-motivating behaviour of players to immerse and engage themselves in the game’s virtual world. Collection of data and feedback from multiple users (52) allowed us to associate the model’s outcomes to the user responses, as well as device multiple trial scenarios to verify their training and engagement. We have also evaluated the algorithms for the generation of new tracks for their suitability on the skill’s profile of 41 of our subjects and race track diversity among the evolved paths. We have also designed a method for predicting the states of the user-controlled system by combining information from both sources – vehicle and user – via Gaussian Processes (GPs). In the context of high speed car racing we showed that the forthcoming position and speed of the car can be predicted with high accuracy by our trained user models. This opens up future possibilities of generating better personalised tracks for individuals or even real-time share-control of the car to optimally assist the users in dangerous situations.Open Acces