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Development of a classifier to determine factors causing cybersickness in virtual reality environments

Key:GRB+19
Author:Augusto Garcia-Agundez, Christian Reuter, Hagen Becker, Robert Konrad, Polona Caserman, André Miede, Stefan Göbel
Date:December 2019
Kind:Article - use for journal articles only
Journal:Games for Health Journal
Number:6
Pages:439-444
Volume:8
Research Area(s):Serious Games
Abstract:Objective: The goal of this contribution is to develop a classifier able to determine if cybersickness (CS) has occurred after immersion in a virtual reality (VR) scenario, based on a combination of biosignals and game parameters. Methods: We collected electrocardiographic, electrooculographic, respiratory, and skin conductivity data from a total of 66 participants. In addition, we also captured relevant game parameters such as avatar linear and angular speed as well as acceleration, head movements, and on-screen collisions. The data were collected while the participants were in a 10-minute VR experience, which was developed in Unity. The experience forced rotation and lateral movements upon the participants to provoke CS. A baseline was captured during a first simple scenario. The data were then split in per-level, per-60-second, and per-30-second windows. Furthermore, participants filled a pre- and postimmersion simulator sickness questionnaire. Simulator sickness scores were then used as a reference for binary (CS vs. no CS) and ternary (no CS–mild CS–severe CS) classification patterns. Several classification methods (support vector machines, K-nearest neighbors, and neural networks) were tested. Results: A maximum classification accuracy of 82% was achieved for binary classification and 56% for ternary classification. Conclusion: Given the sample size and the variety of movement patterns presented in the demonstration, we conclude that a combination of biosignals and game parameters suffice to determine the occurrence of CS. However, substantial further research is required to improve binary classification accuracy to adequate values for real-life scenarios and to determine better approaches to classify its severity.
URL:https://www.liebertpub.com/doi/abs/10.1089/g4h.2019.0045

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