This paper quantitatively investigates the feasibility of, and merit in, soliciting continuous subjective cybersickness ratings as participants passively engage in an immersive VR experience. The main research questions addressed are: (1) Feasibility: To what extent will participants successfully engage, unprompted, in continuous cybersickness reporting while engaging with a secondary task? and (2) Merit: To what extent do continuously reported subjective cybersickness ratings offer valuable insights beyond what can be obtained from less frequent querying? Participants used a physical slider device, in conjunction with discreet visual feedback, to continuously report their instantaneous motion sickness state as they rode nineteen consecutive rounds of a virtual roller coaster ride and performed a simple visual counting task. We analyzed the reported sickness ratings in the context of pre-post SSQ scores, #rounds endured before quitting, tonic skin conductance levels (SCL), optical flow of the visual stimulus, and rotational and translational velocity of the virtual viewpoint, as well as in comparison to previously-obtained data from different participants who underwent the same exposure but only verbally reported a single FMS score at the end of each round (every 65s). We found that most participants used the slider actively, and that, averaged across participants, the reported sickness scores not only increased over time but also varied up and down in conjunction with the intensity of the ride. We found a statistically significant positive correlation between instantaneous reported sickness levels and tonic electrodermal activity in 76% of our participants, as well as a statistically significant positive correlation with optical flow magnitude and viewpoint rotational velocity. We observed no significant differences in #rounds completed, Delta SSQ scores, and average maximum or average last-reported sickness levels between the continuous and discrete reporting groups. Altogether, our results (1) demonstrate the feasibility of collecting valid self-reported ratings of cybersickness on a continuous basis during a passive VR experience, and (2) suggest that such data has the potential to be useful for better understanding cybersickness evolution in the context of potentially transient triggers.
Virtual reality (VR) offers benefits such as enhanced interest, engagement, and vocabulary retention when learning another language. Due to the immersive nature of VR, users can enter a virtual setting that provides context clues as to what a particular vocabulary word in the target language represents. Users can also interact with the object to help increase the potential of being able to retain the term in the target language for the object through association with its surroundings. While the influence of using contextually-relevant virtual environments and providing object interaction have been studied separately, the aim of this research is to identify whether the combination of object interaction and a context-relevant environment is acceptable to users and effective at promoting short-term retention for basic vocabulary related to everyday objects.
This work presents FlowZone, an exploratory VR prototype that integrates yoga and mindfulness meditation. The system uses headset and controller data for lightweight pose tracking and provides real-time feedback in guided sessions. A preliminary user study (N=6) revealed common challenges in yoga practice, including uncertainty about pose correctness, difficulty maintaining consistency, and the importance of calming environments. These insights informed the design of FlowZone, which combines accessible pose guidance with immersive, meditation-oriented settings. While not a full evaluation, our early findings suggest that VR yoga and meditation can lower barriers to practice and support stress reduction, pointing toward promising directions for future research.
Notifications are a fundamental aspect of daily computing, whether on desktops, laptops, smartphones, or smartwatches. On average, adults receive around 200 notifications per day-approximately one every five minutes during waking hours. As Extended Reality (XR) headsets advance, they may become the primary medium for digital interactions, making notification management a crucial factor in their usability. While notifications are known to be disruptive on smartphones, their impact could be even more pronounced on headworn devices. To investigate this, we conducted an exploratory five-day study with eight participants wearing display-equipped smartglasses that delivered notifications from their smartphones. Participants used the glasses throughout their day for at least 2 hours receiving on average 62% of all notifications on the glasses, submitted daily journal entries, and participated in post-study interviews. We also logged notification sources and timestamps throughout the study. Our findings reveal both practical advantages and significant challenges of head-worn notification delivery. While participants appreciated the convenience and immediacy of glanceable alerts, concerns about privacy, social acceptability, and distraction emerged as key barriers to adoption.
Enhancing user experience and performance, including task load in immersive environments, requires accurate prediction of user gaze point, reaction time, and mental and physical load uptake. Current gaze prediction approaches focus primarily on motionbased information, lacking physiological data, which leads to poor prediction accuracy in highly dynamic virtual reality (VR) environments. Traditional cognitive load measurements rely on posttask analysis without proper multimodal data integration and fail to capture the real-time dynamics of user states during interaction. Likewise, reaction time or attention load are often assessed only after the interaction, without using real-time immersive sensor data, which limits adaptive responsiveness. To tackle these limitations, we leveraged a comprehensive multimodal dataset VRWalking, which recorded timestamped eye-tracking metrics, physiological signals (heart rate and galvanic skin response), and behavioral performance data during real-time engagement in a VR environment. We developed a unified multitask model based on the MultiPatchFormer architecture, which processes multimodal VR signals through dual patch projection branches for gaze and classification inputs. The model employs multiscale patch embeddings, cross-attention between gaze and classification pathways, channel attention, and transformer encoders to jointly predict continuous user gaze and classify reaction time, cognitive load (mental load and physical load). Our methodology achieved excellent predictive performance: 95.64% for reaction time, 98.01% for mental load, and 97.45% for physical load, with a MAPE (Mean Absolute Percentage Error) of 15.24% for gaze prediction. We applied Shapley Additive explanations (SHAP) analysis to interpret the model's behavior across all features, including eye-tracking, head-tracking, and physiological signals. The analysis revealed which features most influenced the predictions of user gaze, reaction time, mental load, and physical load. Our methods, while based only on the VR-Walking dataset, demonstrated strong performance across all tasks, suggesting promising potential for real-world VR applications such as interactive training systems that respond to user attention lapses, educational platforms that adapt to cognitive load, and performance assessments that consider physiological indicators.
