NDLI: Evaluating virtual reality experience and performance: a brain based approach

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Evaluating virtual reality experience and performance: a brain based approach

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Evaluating virtual reality experience and performance: a brain based approach

Content Provider	ACM Digital Library
Author	Pike, Matthew Ch'ng, Eugene
Abstract	The recent trend and parallel development/adoption of Virtual Reality, Brain Sensing Measures and associated technology such as Augmented Reality by large corporations, and the rise in the interests in the consumer market have set a positive tone for research in these disciplines. An important human factors area that is a catalyst to broad VR applications is the measure of perception, mental workload, and immersion amongst other issues, which are determining factors in the experience of using virtual environments. Traditional approaches in studying these issues use well-developed subjective measures via questionnaires. A new opportunity in the parallel developments in wearable physiological sensors such as brain scanners could potentially be an objective approach in resolving many subjective uncertainties amongst other prospects. Here, we propose the integration of these two emerging fields in order to provide a continuous, objective, physiological measure of an individual's VR experience for the purposes of enhancing user experience and improving performance. This positional paper attempts to merge two complementary field of work, and discusses implications which could potentially open up avenues of research which were traditionally difficult due to the limitations of equipment, or the lack of quantified approach.
Starting Page	469
Ending Page	474
Page Count	6
File Format	PDF
ISBN	9781450346924
DOI	10.1145/3013971.3014012
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cognitive ergonomics Human computer interaction Human factors Virtual reality User experience Brain computer interfaces Quantitative measures
Content Type	Text
Resource Type	Article

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