How to Observe Users’ Movements in Virtual Environments: Viewpoint Control in a Power Wheelchair Simulator
Bibliographic record
Abstract
OBJECTIVE: We describe a networked, two-user virtual reality (VR) power wheelchair (PWC) simulator system in which an actor (client) and an observer (clinician) meet. We then present a study with 15 observers (expert clinicians) evaluating the effect of three principal forms of viewpoint control (egocentric-egomotion, egocentric-tethered, and client-centric) on the observer's assessment of driving tasks in a virtual environment (VE). BACKGROUND: VR allows for the simulation and assessment of real-world tasks in a controlled, safe, and repeatable environment. Observing users' movement behavior in such a VE requires appropriate viewpoint control for the observer. The VR viewpoint user interface should allow an observer to make judgments equivalent or even superior to real-world situations. METHOD: A purpose-built VR PWC simulator was developed. In a series of PWC driving tasks, we measured the perceived ease of use and sense of presence of the observers and compared the virtual assessment with real-world "gold standard" scores, including confidence levels in judgments. RESULTS: Findings suggest that with more immersive techniques, such as egomotion and tethered egocentric viewpoints, judgments are both more accurate and more confident. The ability to walk and/or orbit around the view significantly affected the observers' sense of presence. CONCLUSION: Incorporating the observer into the VE, through egomotion, is an effective method for assessing users' behavior in VR with implications for the transferability of virtual experiences to the real world. APPLICATION: Our application domain serves as a representative example for tasks where the movement of users through a VE needs to be evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".