Virtual reality (VR) presents immersive opportunities across many applications, yet the inherent risk of developing cybersickness during interaction can severely reduce enjoyment and platform adoption. Cybersickness is marked by symptoms such as dizziness and nausea, which previous work primarily assessed via subjective post-immersion questionnaires and motion-restricted controlled setups. In this paper, we investigate the dynamic nature of cybersickness while users experience and freely interact in VR. We propose a novel method to continuously identify and quantitatively gauge cybersickness levels from users' passively monitored electroencephalography (EEG) and head motion signals. Our method estimates multitaper spectrums from EEG, integrating specialized EEG processing techniques to counter motion artifacts, and, thus, tracks cybersickness levels in real-time. Unlike previous approaches, our method requires no user-specific calibration or personalization for detecting cybersickness. Our work addresses the considerable challenge of reproducibility and subjectivity in cybersickness research. In addition to our method's implementation, we release our dataset of 16 participants and approximately 2 hours of total recordings to spur future work in this domain. Source code: https://github.com/eth-siplab/EEG_Cybersickness_Estimation_VR-Beyond_Subjectivity.

To enhance productivity and to streamline workflows, there is a growing trend to embed large language model (LLM) functionality into applications, from browser-based web apps to native apps that run on personal computers. Here, we introduce LLM-for-X, a system-wide shortcut layer that seamlessly augments any application with LLM services through a lightweight popup dialog. Our native layer seamlessly connects front-end applications to popular LLM backends, such as ChatGPT and Gemini, using their uniform chat front-ends as the programming interface or their custom API calls. We demonstrate the benefits of LLM-for-X across a wide variety of applications, including Microsoft Office, VSCode, and Adobe Acrobat as well as popular web apps such as Overleaf. In our evaluation, we compared LLM-for-X with ChatGPT's web interface in a series of tasks, showing that our approach can provide users with quick, efficient, and easy-to-use LLM assistance without context switching to support writing and reading tasks that is agnostic of the specific application.

Virtual Reality (VR) systems have traditionally required users to operate the user interface with controllers in mid-air. More recent VR systems, however, integrate cameras to track the headset's position inside the environment as well as the user's hands when possible. This allows users to directly interact with virtual content in mid-air just by reaching out, thus discarding the need for hand-held physical controllers. However, it is unclear which of these two modalities—controller-based or free-hand interaction—is more suitable for efficient input, accurate interaction, and long-term use under reliable tracking conditions. While interacting with hand-held controllers introduces weight, it also requires less finger movement to invoke actions (e.g., pressing a button) and allows users to hold on to a physical object during virtual interaction.In this paper, we investigate the effect of VR input modality (controller vs. free-hand interaction) on physical exertion, agency, task performance, and motor behavior across two mid-air interaction techniques (touch, raycast) and tasks (selection, trajectory-tracing). Participants reported less physical exertion, felt more in control, and were faster and more accurate when using VR controllers compared to free-hand interaction in the raycast setting. Regarding personal preference, participants chose VR controllers for raycast but free-hand interaction for mid-air touch. Our correlation analysis revealed that participants' physical exertion increased with selection speed, quantity of arm motion, variation in motion speed, and bad postures, following ergonomics metrics such as consumed endurance and rapid upper limb assessment. We also found a negative correlation between physical exertion and the participant's sense of agency, and between physical exertion and task accuracy.

When learning to play an instrument, it is crucial for the learner’s muscles to be in a relaxed state when practicing. Identifying, which parts of a song lead to increased muscle tension requires self-awareness during an already cognitively demanding task. In this work, we investigate unobtrusive pressure sensing for estimating muscle tension while practicing songs with the guitar. First, we collected data from twelve guitarists. Our apparatus consisted of three pressure sensors (one on each side of the guitar pick and one on the guitar neck) to determine the sensor that is most suitable for automatically estimating muscle tension. Second, we extracted features from the pressure time series that are indicative of muscle tension. Third, we present the hardware and software design of our PressurePick prototype, which is directly informed by the data collection and subsequent analysis.

ybersickness has been one of the main impediments to the widespread adoption of Virtual Reality for decades. It has been argued that several factors can influence the occurrence of cybersickness, such as technical factors, interaction design, but also users’ demographics and their perceived presence. Yet, previous studies had comparably small sample sizes and demographically homogeneous samples; comparisons across studies (e.g., regarding demographic factors) are challenging due to the large variation in the studied virtual environments. In this paper, we address these limitations and report the results of a lab-in-the-field experiment on cybersickness with a large and heterogeneous sample of $N =837$ participants who navigated and interacted inside a virtual environment (ages 18–80, $M = 29.34, SD = 9.50$, 431 males, 400 females, 6 non-binaries and other). We found that female participants and participants with lower VR experience were more susceptible to experiencing higher levels of cybersickness. Participants’ cybersickness levels increased with the time spent in VR and with the distance traversed in the virtual world up to a point, above which reported levels declined. We also found a link between higher levels of cybersickness and reduced head motion, as well as between lower levels of cybersickness and more head motion, which led them to explore more of the virtual environment. In contrast to past studies, we did not find any evidence suggesting an effect of age on cybersickness, nor a negative correlation between presence and cybersickness. Based on our results, we derived a model that achieves a mean classification accuracy of 67.1% for two levels of cybersickness using demographic, user experience, and behavioral data in VR.