Automating real-time feedback in virtual reality training through data mining

Project description

This project will develop an automated, real-time feedback system for virtual reality (VR) training that harnesses data mining techniques. Based on electronic measures of trainees’ behaviour in VR environments previously established by the research team, we will develop sophisticated predictive models of expert and novice behavior. Using these models the feedback system will deliver nuanced feedback to trainees during a task, replicating the advice typically provided by an instructor. This system will lead to greater uptake and increased efficiency in training across industries reliant upon VR simulation, such as aviation, aerospace, mining, health and emergency services.

Project team

Leader: James Bailey

Staff: James Bailey

Students: Yun Zhou

Collaborators: Ioanna Ioannou (Medicine) Gregor Kennedy (Education) Stephen O'Leary (Medicine) Sudanthi Wijewickrema (Medicine), Peter Gibson (Cochlear Ltd)

Sponsors: Australian Research Council

Other projects

Convergence of engineering and IT with the life sciences projects

Networks and data in society projects

Disciplines

Biomedical Engineering,Computing and Information Systems

Domains

Convergence of engineering and IT with the life sciences, Networks and data in society

Keywords

data mining; virtual surgery