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An Overview of Self-Adaptive Technologies Within Virtual Reality Training.

Vaughan, N., Gabrys, B. and Dubey, V. N., 2016. An Overview of Self-Adaptive Technologies Within Virtual Reality Training. Computer Science Review, 22 (November), 65-87.

Full text available as:

OverviewAdaptiveVR.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.cosrev.2016.09.001


This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training.

Item Type:Article
Uncontrolled Keywords:Virtual reality ; Adaptive systems ; Intelligent Algorithms ; Training
Group:Faculty of Science & Technology
ID Code:24690
Deposited By: Symplectic RT2
Deposited On:06 Sep 2016 15:32
Last Modified:14 Mar 2022 13:58


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