The methods for measuring virtual reality (VR) sickness can be divided into objective indicators and subjective surveys. The method using objective indicators measures VR sickness by monitoring changes in the users’ physiological signals. Various physiological indicators have been used with this method, each with a different processing technique and outcome. This diversity complicates the establishment of standardized metrics (such as biodata-based scores and quantification) for VR sickness. Therefore, this study proposes a method for evaluating VR sickness using the recurrence period density entropy (RPDE) and conducts experiments to validate the feasibility of this approach utilizing prominent physiological data, such as electrocardiography data. The experimental results confirm that although RPDE values vary as individual metrics, the changes in these values may be correlated with VR sickness.

In this paper, we propose an interactive mobile gait training system that allows trainees to interact actively with its content (the gait training content). The proposed system is a new type of gait training one combining a mobile robot with virtual reality (contents). It is a mobile system that projects virtual contents (for example, virtual footprints) for gait training on the actual ground (or floor). The performance and effectiveness of the proposed system were examined through a trainee’s foot recognition test and usability evaluation. The test results confirmed that the proposed system showed an average recognition ratio of more than 97%, meaning that the system could accurately recognize the trainee’s foot. In addition, as a result of usability evaluation, the overall satisfaction was 86%, confirming that the proposed system is effective.

As VR hardware advances, hardware-related factors inducing motion sickness are expected to be gradually resolved; consequently, software-based mitigation methods are garnering significant attention. While various software-based methods exist, they often rely on modifying the content itself, presenting a significant drawback: difficulty in immediate application to existing content. Meanwhile, the Visual Guide (VG) technique reduces sickness by superimposing a separate image onto the content. This approach offers the distinct advantage of being easily applicable to existing content. However, motion sickness mitigation via visual guides suffers from a drawback: the added imagery can distract from the content, thereby reducing user concentration. To address this, this paper proposes a mitigation method utilizing the transparency of the visual guide to minimize the loss of focus on the content. To evaluate the proposed method, experiments were conducted to collect EEG data, Simulator Sickness Questionnaire (SSQ) scores, and responses to a custom-designed concentration questionnaire. Under the tested conditions, the 60-s fade-out condition showed a favorable descriptive balance between lower mean SSQ score changes and preserved concentration. Because corrected pairwise post-hoc comparisons were not performed and the EEG analysis was based on whole-session average power spectral density, these findings should be interpreted as condition-level trends rather than definitive pairwise or event-related effects.

Mitigating in-vehicle VR sickness is a critical challenge for the use of immersive content in autonomous vehicles. In this study, to solve in-vehicle VR sickness, the cause of motion sickness in each field is identified by conducting a scoping review in three fields (medicine, control engineering, and computer engineering). As a result of the confirmation, motion sickness was defined in all three academic fields based on the theory of sensory conflict and posture instability, so we defined and identified seven major SFs based on these universal theories. Based on the defined SF, the analysis of the current research trend confirmed that there is an important research gap that shows that motion-induced motion sickness (MIMS) research focuses on the dynamic control of the vehicle while at the same time overlooks cognitive-sensory factors such as Mismatched Environmental Perception and feedback prediction error. At the same time, visually induced motion sickness (VIMS) research is focused on rendering control elements, which often reduces user immersion. This review suggests the need to focus not only on approaching the form of motion sickness in the vehicle, but also on the possible sensory-cognitive conflict of the passenger in the vehicle. By connecting the three academic fields related to motion sickness, it provides a strategic roadmap for the final in-vehicle content control system. Ultimately, this framework is expected to serve as a fundamental technology resource for in-vehicle content viewing, laying the groundwork for in-vehicle VR content consumption.