Design and Application of a Novel Virtual Reality Navigational Technology (VRNChair)
Bibliographic record
Abstract
This paper presents a novel virtual reality navigation (VRN) input device, called the VRNChair, offering an intuitive and natural way to interact with virtual reality (VR) environments. Traditionally, VR navigation tests are performed using stationary input devices such as keyboards or joysticks. However, in case of immersive VR environment experiments, such as our recent VRN assessment, the user may feel kinetosis (motion sickness) as a result of the disagreement between vestibular response and the optical flow. In addition, experience in using a joystick or any of the existing computer input devices may cause a bias in the accuracy of participant performance in VR environment experiments. Therefore, we have designed a VR navigational environment that is operated using a wheelchair (VRNChair). The VRNChair translates the movement of a manual wheelchair to feed any VR environment. We evaluated the VRNChair by testing on 34 young individuals in two groups performing the same navigational task with either the VRNChair or a joystick; also one older individual (55 years) performed the same experiment with both a joystick and the VRNChair. The results indicate that the VRNChair does not change the accuracy of the performance; thus removing the plausible bias of having experience using a joystick. More importantly, it significantly reduces the effect of kinetosis. While we developed VRNChair for our spatial cognition study, its application can be in many other studies involving neuroscience, neurorehabilitation, physiotherapy, and/or simply the gaming industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".