Past repor ts have suggested that active visual training in vir tual reality (VR) can reduce symptoms of cybersickness. Here, we adapted such a protocol to a computer-based version and compared it with a passive exposure control condition. We employed hear t rate and other subjective predictors of cybersickness to try to predict the efficacy of the intervention as well as likelihood of drop out. While we found a significant decrease in hear t rate across sessions, the intervention we employed did not appear to be effective at reducing cybersickness or dropout. However, a hear t rate increase of 15.5 bpm from baseline, nausea self-repor t of 4.5 on a scale of 1–10, and dizziness self-repor t of 5.5 on a scale of 1–10 predicted an equal probability of experiment dropout, independent of whether par ticipants were in the experimental or control intervention condition. Our findings suggest that a single immersion of visual training in VR or passive VR exposure may not be sufficient to provide adaptation for VR. At the same time, our findings bolster past repor ts suggesting the value of employing hear t rate monitoring, rather than subjective repor ts, to monitor the onset of cybersickness.
ASJC Scopus subject areas
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Vision and Pattern Recognition