Redirected walking (RDW) is a technology that enables users to walk seamlessly in an enormous virtual space within a narrow real space while avoiding collisions with physical elements. Although RDW provides accurate proprioceptive sensations, redirection performance is limited by visual–vestibular inconsistencies. This study aims to support seamless walking in a VR environment by alleviating inconsistencies using four vestibular stimulations: noisy and directional galvanic vestibular stimulation, bone-conduction vibration, and caloric vestibular stimulation. The user study demonstrated that the stimulations successfully enable spatial expansion without impairing immersion and presence. Non-electrical stimulations (bone-conduction vibration and caloric vestibular stimulation) expanded the detection threshold, making them alternatives to electrical stimulations, and direction-based stimulation (directional galvanic vestibular stimulation) improved the user’s gait stability in RDW. Finally, the findings suggested improving the user experience for vestibular stimulation RDW either by lowering audio interference or increasing the synchronization between the RDW gain and the stimulation intensity.

Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs. These multimodal NUIs have variable effects on different types of driver distraction and on different stages of drivers' secondary tasks. However, current studies provide a limited understanding of NUIs. The design of multimodal NUIs is typically based on evaluation of the strengths of a single modality. Furthermore, studies of multimodal NUIs are not based on equivalent comparison conditions. To address this gap, we compared five single modalities commonly used for NUIs (touch, mid-air gesture, speech, gaze, and physical buttons located in a steering wheel) during a lane change task (LCT) to provide a more holistic view of driver distraction. Our findings suggest that the best approach is a combined cascaded multimodal interface that accounts for the characteristics of a single modality. We compared several combinations of cascaded multimodalities by considering the characteristics of each modality in the sequential phase of the command input process. Our results show that the combinations speech + button, speech + touch, and gaze + button represent the best cascaded multimodal interfaces to reduce driver distraction for IVIS.

Cultural heritage (CH) artifacts, such as ceramics and clothes, reflect the unique characteristics of ancient cultures and have the potential to be sustainably employed in modern design and entertainment. In particular, the shape of ceramics reflects regional and historical characteristics, so datafication is a promising avenue to preserve these assets for future generations. However, design is a specialized domain that requires significant human (expert and novice) labor. This often tedious process decreases the labeler’s motivation to complete the task, and data consistency varies with the experience and motivation of the labeler. To increase engagement, we developed an image labeling platform with graphical icon-based labeling methods and introduced gamification. The robust labeling methods with gamification increased novices’ engagement and decreased the workload of expert and novice labelers, but decreased data agreement between experts and novices, so we consider opportunities for gamification within the specialized cultural heritage domain.

Virtual reality (VR) locomotion should allow users to move freely in the virtual space while staying within the tracking area in the real space. The redirected walking (RDW) technique enables users to walk naturally in an unlimited virtual space within a limited tracking area by rotating the virtual scene view. However, conflicting visual and vestibular signals during RDW can lead to user discomfort and decreased immersion. To avoid user discomfort, an RDW gain should be within the detection threshold (DT) range. However, a large angle of walking redirection is required when physically avoiding obstacles or escaping from a narrow space, so DT expansion is necessary. In this study, to change the curvature DT range and enhance RDW performance, we proposed an optical flow (OF)-generating vection in a virtual environment. Further, we investigate methods to reduce user discomfort and increase RDW efficiency considering familiar and unfamiliar VR users. The findings showed that the introduction of OF led to a reduction in the DT range for all users, irrespective of the OF’s direction. However, conditions with OF resulted in an extended DT range for users familiar with VR while concurrently diminishing the DT range for those who were VR unfamiliar. To delve further, our analysis indicated that when both the OF and redirecting directions were identical, the RDW performance was robust to VR familiarity, whereas in opposing directions, the DT range increased for VR-familiar users. Our study findings suggested using OF for the RDW technique and extending its applicability in virtual environments.

In virtual reality, redirected walking (RDW) enables users to stay within the tracking area while feeling that they are traveling in a virtual space that is larger than the physical space. RDW uses a visual attractor to the user’s sight and scene manipulation for intermittent reorientation. However, repeated usage can hinder the virtual world immersion and weaken the reorientation performance. In this study, we propose using sounds and smells as alternative stimuli to draw the user’s attention implicitly and sustain the attractor’s performance for intermittent reorientation. To achieve this, we integrated visual, auditory, and olfactory attractors into an all-in-one stimulation system. Experiments revealed that the auditory attractor caused the fastest reorientation, the olfactory attractor induced the widest angular difference, and the attractor with the combined auditory and olfactory stimuli induced the largest angular speed, keeping users from noticing the manipulation. The findings demonstrate the potential of nonvisual attractors to reorient users in situations requiring intermittent reorientation.

Understanding drivers’ states is essential for providing personalized interventions and adaptive feedback in vehicles, thereby ensuring safer driving and a more comfortable driver experience. As driving tasks necessitate understanding and reaction to the rapidly changing road environment and successful management of working memory to prevent dual-task interference, driver situation awareness (SA) should be primarily considered for such interventions and feedback. However, due to its complex nature, most of the current methods for measuring SA rely on questionnaires. In this work, we aim to develop sensor-based assessments of driver SA, especially towards road signs, which play an important role in immediate decision-making during driving. We collected eye-tracking data and physiological responses from 32 participants during simulated driving and annotated this data according to the levels of SA that drivers achieved. Our ensemble-based machine learning model that uses physiological measures and gaze-related features demonstrated an accuracy of 78.02% in a three-class driver SA prediction. Since SA is a key component in evaluating vehicular interfaces, our VR-based approach holds the potential for the iterative design of in-car infotainment applications and road environments. The method also lays a foundation for SA-adaptive cooperation between human drivers and AI in the context of advanced driver assistance.