Real-Time Feedback-Driven Framework For Automated Cybersickness Mitigation

As technologies are becoming more advanced day by day, the embracement of virtual reality (VR) technology among users is also increasing in daily activities for various purposes, and subsequently, the barrier between the real and virtual world is fading. Despite the versatile uses, cybersickness (CS) is a major problem which is induced among users due to the immersive VR experience. There is a plethora of research findings and methods to measure the users’ CS such as virtual reality sickness questionnaire (VRSQ), simulator sickness questionnaire (SSQ), fast motion scale questionnaire (FMS), and others. Recently, machine learning approaches have also been adopted to predict CS but to the best of our knowledge, there is no research work to mitigate the user's CS based on the physiological data during immersive moments. To bridge this gap, in this study, we propose a framework where our primary focus is to mitigate the users’ discomfort during their immersive experience. To develop the framework, we trained several deep neural network models to choose the best model for getting CS prediction. We used two open-source physiological datasets to carry out this study. We gathered the user’s real-time eye tracking data during the immersive experience and fit it into the model to predict the real-time sickness level. The CS reduction toolkit was also deployed to the framework, which applies the adaptive CS mitigation technique on simulation based on the sickness level to maintain the user’s comfort. Lastly, we have designed the user study to carry out a user study to evaluate our framework's effectiveness based on the users' feedback.