Peerawat Pannattee

Controlling multiple agents in parallel is an important challenge in human–robot interaction, with growing applications in remote collaboration, telepresence, and multi-agent coordination. However, empirical work on distributed embodiment remains limited, particularly in scenarios where independently controlled avatars operate in distinct environments. To address this gap, we propose the concept of Dual Body Sensation (DBS): a novel theoretical construct of distributed embodiment, describing a state in which a single operator simultaneously experiences embodiment for two independent robots in distinct environments during their control. We conducted a within-subject experiment to investigate the cognitive feasibility of DBS using a 2×3 factorial design that varied visual presentation (independent view, superimposed view) and tactile presentation (no tactile, bilateral tactile, unified tactile). The results indicated that DBS emerged across all conditions and was significantly enhanced under the unified tactile condition. The combination of the superimposed view and unified tactile conditions also increased the sense of body ownership and the sense of agency, while the superimposed view condition reduced cybersickness and cognitive load. These findings provide empirical support for DBS and inform the design of future multi-agent interaction systems that support flexible distribution of bodily control and awareness.

Virtual Reality (VR) technology offers immersive experiences across various domains, but assessing user experience (UX) remains a challenge. Traditional methods, such as questionnaires, are time-consuming and fail to capture the complexity of user interactions, while automated approaches which leverage sensors and deep learning (DL) techniques show promise but have several architectural limitations. Existing frameworks often rely on data from contact-based physiological sensors. They also tend to overlook attributes of the visual stimuli, use simplistic multimodal fusion techniques, and focus on a single UX dimension, all of which reduce their ability to provide a comprehensive and accurate assessment of UX. This study proposes MUXAS-VR, a DL-based framework that overcomes these limitations by leveraging multimodal behavioral cues. These are seamlessly captured using embedded sensors in commercially available VR devices, eliminating the need for contact-based physiological sensors. MUXAS-VR integrates individual behavioral data with visual attributes, such as motion dynamics and visual complexity, to account for individual interactions and visual influences. Its Multimodal Fusion Module (MFM) selectively combines data from multiple modalities to enhance feature representation and predictive performance. Unlike conventional systems, MUXAS-VR evaluates multiple UX dimensions—cybersickness (CS), sense of presence (SOP), and overall satisfaction (OS)—to provide a comprehensive view of VR UX. To validate its effectiveness, we proposed a rigorous evaluation strategy across three scenarios—Missing Content, New Content, and New User—to ensure generalizability and practical applicability. Experimental results showed that multimodal behavioral cues significantly improved predictive performance. Combining these cues with visual attributes provided complementary information, and the MFM outperformed simpler fusion methods by capturing complex interdependencies. These comprehensive evaluations across diverse scenarios demonstrate MUXAS-VR’s usefulness in predicting multiple UX dimensions.

Cybersickness is a growing concern in the field of virtual reality (VR). It is characterized by symptoms, such as headache, sweating, disorientation, and nausea. These symptoms can considerably hinder the users’ immersive experience in VR environments, leading to a pressing need for effective solutions to combat cybersickness. In this study, we aim to tackle cybersickness by presenting a novel high-frequency approach for detecting the timing at which users experience cybersickness. Our approach uses 1-, 5-, or 10-s time-series eye-related indices processed by deep learning algorithms to predict cybersickness severity. In five-fold cross-validation, we achieved 71.09% accuracy in classifying four classes of cybersickness severity when individuals were not distinguished. Furthermore, with individualized cross-validation, we achieved an accuracy of up to approximately 80%. Our approach outperforms other cybersickness prediction studies as it provides the highest frequency in predicting cybersickness. It is anticipated that our approach will be valuable not only for immediate evaluation by researchers investigating cybersickness mitigation but also for early detection and notification of users experiencing cybersickness symptoms. By predicting cybersickness, our approach has the potential to promote the future advancement of VR technology.