Towards VR Training Adapted to Affective and Cognitive States: General Method and Evaluation of Mental Workload

Adaptive virtual reality (VR) applications are used in training and rehabilitation to provide personalized experience, through the adaptive logic that adjust the virtual environment based on the user's behavior. While users' behavior is influenced by affective and cognitive states (ACS), adaptive logic typically relies only on users' performance. First, this paper introduces a general method for the adaptation of VR applications that integrates ACS. We then provide an implementation of this model adapting difficulty with regards to both performance and mental workload. Finally, we present a user study (N=30) to compare our mental workload-based method (experimental) to a state-of-the-art adaptation relying on performance only (control). Results show that our adaptation method led to a decrease of 10.7% in mental workload, an increase of 22.8% in performance, and an overall better experience for most participants. These results were achieved without the participants' awareness of the adaptive logic of each condition. Taken together, our results promote the integration of ACS in adaptive VR to enhance users' experience and efficiency, and better fit the function of VR training applications.