Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality

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2024-02-05
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Salehi, Masoud
Javadpour, Nikoo
Beisner, Brietta
Sanaei, Mohammadamin
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arXiv
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Gilbert, Stephen
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Virtual Reality Applications Center
At VRAC, our mission is clear: “To elevate the synergy between humans and complex interdisciplinary systems to unprecedented levels of performance”. Through our exceptional Human Computer Interaction (HCI) graduate program, we nurture the next generation of visionaries and leaders in the field, providing them with a comprehensive understanding of the intricate relationship between humans and technology. This empowers our students to create intuitive and transformative user experiences that bridge the gap between innovation and practical application.
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Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness.
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This is a preprint from Salehi, Masoud, Nikoo Javadpour, Brietta Beisner, Mohammadamin Sanaei, and Stephen B. Gilbert. "Innovative Cybersickness Detection: Exploring Head Movement Patterns in Virtual Reality." arXiv preprint arXiv:2402.02725 (2024). doi: https://doi.org/10.48550/arXiv.2402.02725. Copyright 2024 The Authors. CC BY.
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