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Virtual environments (VEs) can be infinitely large, but movement of the virtual reality (VR) user is constrained by the surrounding real environment. Teleporting has become a popular locomotion interface to allow complete exploration of the VE. To teleport, the user selects the intended position (and sometimes orientation) before being instantly transported to that location. However, locomotion interfaces such as teleporting can cause disorientation. This experiment explored whether practice and feedback when using the teleporting interface can reduce disorientation. VR headset owners participated remotely. On each trial of a triangle completion task, the participant traveled along two path legs through a VE before attempting to point to the path origin. Travel was completed with one of two teleporting interfaces that differed in the availability of rotational self-motion cues. Participants in the feedback condition received feedback about their pointing accuracy. For both teleporting interfaces tested, feedback caused significant improvement in pointing performance, and practice alone caused only marginal improvement. These results suggest that disorientation in VR can be reduced through feedback-based training.
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Crash Prediction Using Deep Learning in a Disorienting Spaceflight Analog Balancing Task
Were astronauts forced to land on the surface of Mars using manual control of their vehicle, they would not have familiar gravitational cues because Mars’ gravity is only 0.38 g. They could become susceptible to spatial disorientation, potentially causing mission ending crashes. In our earlier studies, we secured blindfolded participants into a Multi-Axis Rotation System (MARS) device that was programmed to behave like an inverted pendulum. Participants used a joystick to stabilize around the balance point. We created a spaceflight analog condition by having participants dynamically balance in the horizontal roll plane, where they did not tilt relative to the gravitational vertical and therefore could not use gravitational cues to determine their position. We found 90% of participants in our spaceflight analog condition reported spatial disorientation and all of them showed it in their data. There was a high rate of crashing into boundaries that were set at ± 60 ° from the balance point. Our goal was to see whether we could use deep learning to predict the occurrence of crashes before they happened. We used stacked gated recurrent units (GRU) to predict crash events 800 ms in advance with an AUC (area under the curve) value of 99%. When we prioritized reducing false negatives we found it resulted in more false positives. We found that false negatives occurred when participants made destabilizing joystick deflections that rapidly moved the MARS away from the balance point. These unpredictable destabilizing joystick deflections, which occurred in the duration of time after the input data, are likely a result of spatial disorientation. If our model could work in real time, we calculated that immediate human action would result in the prevention of 80.7% of crashes, however, if we accounted for human reaction times (∼400 ms), only 30.3% of crashes could be prevented, suggesting that one solution could be an AI taking temporary control of the spacecraft during these moments.
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- Award ID(s):
- 1920147
- PAR ID:
- 10342603
- Date Published:
- Journal Name:
- Frontiers in Physiology
- Volume:
- 13
- ISSN:
- 1664-042X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Virtual reality users are susceptible to disorientation, particularly when using locomotion interfaces that lack self-motion cues. Environmental cues, such as boundaries defined by walls or a fence, provide information to help the user remain oriented. This experiment evaluated whether the type of boundary impacts its usefulness for staying oriented. Participants wore a head-mounted display and performed a triangle completion task in virtual reality by traveling two outbound path segments before attempting to point to the path origin. The task was completed with two teleporting interfaces differing in the availability of rotational self-motion cues, and within five virtual environments differing in the availability and type of boundaries. Pointing errors were highest in an open field without environmental cues, and lowest in a classroom with walls and landmarks. Environments with a single square boundary defined by a fence, drop-off, or floor texture discontinuity led to errors in between the open field and the classroom. Performance with the floor texture discontinuity was similar to that with navigational barriers (i.e., fence and drop-off), indicating that an effective barrier need not be a navigational impediment. These results inform spatial cognitive theory about boundary-based navigation and inform application by specifying the types of environmental and self-motion cues that designers of virtual environments should include to reduce disorientation in virtual reality.more » « less
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Teleporting is a popular interface for locomotion through virtual environments (VEs). However, teleporting can cause disorientation. Spatial boundaries, such as room walls, are effective cues for reducing disorientation. This experiment explored the characteristics that make a boundary effective. All boundaries tested reduced disorientation, and boundaries representing navigational barriers (e.g., a fence) were no more effective than those defined only by texture changes (e.g., flooring transition). The findings indicate that boundaries need not be navigational barriers to reduce disorientation, giving VE designers greater flexibility in the spatial cues to include.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fphys.2022.806357
