Managing multiple activities in virtual reality (VR) is often hindered by fragmented workflows and disruptive application switching. We present JourneyVR, a metaphorical interaction model designed for spatial and sequential workflows that reframes tasks as continuous journeys rather than isolated sessions. Users construct a Journey Map as a layout of islands (tasks) and bridges (transitions), which expands into an immersive world where activities unfold as a coherent, embodied narrative. Through a formative study, a controlled comparison, and an expert evaluation, JourneyVR was shown to enhance experiential continuity, intention to use, and overall satisfaction compared to using a conventional app launcher. Participants highlighted how the metaphor fosters motivation and achievement, while we identify boundaries regarding task type, scalability, and flexibility. Our findings demonstrate that framing sequential activities as navigable journeys can transform fragmented tasks into meaningful narratives, offering concrete guidelines for sustained engagement and more flexible workflows in immersive environments.

Cybersickness has been a hindrance to the widespread adoption of virtual reality. As cybersickness is dynamic, with its manifestation changing constantly, predicting and mitigating it in real time is key. However, previous research has utilized predictive models trained mostly with datasets whose sickness levels were measured and labeled only after experiencing the content. In addition, many datasets rely on physiological signals as input, which makes them difficult to apply in actual VR usage. This makes such timely predictions unreliable or practically infeasible. We have created a publicly available dataset where the ground truth sickness levels were densely marked every 0.5 seconds and adjusted/updated on-demand using the FMS, and purposely excluded the difficult-to-collect physiological data. We demonstrate that predictive models trained with such a dataset, comprising just the content motion profile and FMS data, can still produce comparably reliable sickness prediction, and more so, when user-specific parameters (e.g., age, gender) are added. The created dataset is available at https://github.com/pvtryan1024/2025_Cybersickness_dataset.

Drones are controlled remotely through the on-board live camera often using a headset to immersively (without external distraction) situate the drone operator with a first-person view. The highly dynamic imagery of drone piloting can elicit significant VR sickness. In this poster, we demonstrate the application of mixing the reverse optical flow pattern into the drone piloting imagery for mitigating VR sickness, using only the features of the static landmarks in the scene. We compare it to the cases of applying no mitigation technique (baseline) and mixing in the optical flow pattern from “all” object features. The results show that the suggested method was significantly more effective in reducing sickness than when considering all object features, and had a higher preference for improved visibility and controllability.