期刊
IEEE ACCESS
卷 8, 期 -, 页码 126784-126796出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3008165
关键词
Brainwaves EEG; cybersickness; deep learning; motion dizziness detection
资金
- Ministry of Science and Technology, Taiwan [MOST-107-2221-E-324-018-MY2]
Virtual Reality (VR) research has been widely applied in many fields. VR promises to deliver the experience that is beyond the user's imagination. One of the advantages of VR is the feeling it gives of being there. VR can provide experiences impossible in the real world, such as flying, diving in deep water, exploring outer space, or living with dinosaurs. Despite the improvements in the software and hardware, the problem of motion sickness remains. We implement a deep learning model to train and predict motion sickness. A questionnaire is a well-known method to measure motion sickness. The weakness of the questionnaire is the measurement carried out after the user experiences motion sickness symptoms. By using the deep learning and EEG, the system will learn and classify motion sickness. The system learns the user's EEG pattern when they begin to feel the sickness symptoms. The system will be trained using deep learning to identify the sickness patterns in the future. By the EEG patterns, the system can predict the sickness symptoms before it occurs. Our model outperforms traditional models in loss values, accuracy, and F-measure metrics in Roller Coaster. With other datasets, our model also performs well. Our model can achieve 82.83% accuracy from the dataset. We also found that the time steps to predict motion sickness during 5 minute periods is a suitable configuration.
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