• Journal of the Korea Computer Graphics Society
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• v.25 no.1
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• pp.23-32
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• 2019

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms. In this work, we apply Deep Neural Network (DNN) deep learning algorithm for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and network structures allowing us to achieve high performance when learning EEG data with the deep learning algorithms. Our approach provides cybersickness measurement with an accuracy up to 98.88%. Besides, we analyze video characteristics where cybersickness occurs by examining the video segments causing cybersickness in the experiments. We discover that cybersickness happens even in unusually persistent changes in the darkness such as the light in a room keeps switching on and off.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.709-714
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• 2018

Visual Reality technology is becoming more and more interesting as it attracts people's interest. VR technology is used in various markets such as games, animation, and education. However, there were many people experiencing motion sickness such as dizziness and headache due to the delay time between hardware such as a device for sending a video after experiencing a VR image and an HMD for reproducing an image. The system proposed in this paper focuses on the environment rather than the movement of the attraction and detects the dividing line existing on the path by the proximity sensor and accurately calculates the position on the path according to the user 's motion. Since the position of the user is synchronized with the VR image, the position error of the user is improved to 0.2%.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.27-38
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• 2015

Vestibulo-ocular reflex (VOR) is a compensatory response of the extraocular muscles generated by vestibular signals to stabilize images on the retina during head/body movements. It has been reported that mismatches between retinal and vestibular information, which cause motion sickness or cybersickness, modify VOR. To investigate the characteristic changes of VOR in subjects experiencing cybersickness, we developed a low-cost, multi-purpose VOR measurement system using LabVIEW and Arduino. To test the applicability of the system, we performed two experiments. In Experiment 1, horizontal and vertical VORs of four participants were measured using a vestibular autorotation task. In Experiment 2, eight participants were exposed to a virtual navigation to measure changes of VORs as an index of cybersickness. We observed significantly greater head rotations and eye movements while the participants were exposed to the virtual navigation than to a static image. The results suggest that the present system can help understand the psychophysiological mechanisms of cybersickness symptoms.