[DC] Self-Adaptive Technologies for Immersive Trainings

Abstract

Online learning is the preferred option for professional training, e.g. Industry 4.0 or e-health, because it is more cost efficient than on-site organisation of realistic training sessions. However, current online learning technologies are limited in terms of personalisation, interactivity and immersiveness that are required by applications such as surgery and pilot training. Virtual Reality (VR) technologies have the potential to overcome these limitations. However, due to its early stage of research, VR requires significant improvements to fully unlock its potential. The focus of this PhD is to tackle research challenges to enable VR for online training in three dimensions: (1) dynamic adaptation of the training content for personalised trainings, by incorporating prior knowledge and context data into self-learning algorithms; (2) mapping of sensor data onto what happens in the VR environment, by focusing on motion prediction techniques that use past movements of the users, and (3) investigating immersive environments with intuitive interactions, by gaining a better understanding of human motion in order to improve interaction. The designed improvements will be characterised though a prototype VR training platform for multiple use cases. This work will not only advance the state of the art on VR training, but also on online e-learning applications in general.