Comparing the usability of a virtual reality manual wheelchair simulator in two display conditions
Bibliographic record
Abstract
Virtual reality (VR) simulators can help train manual wheelchair skills. Transfer of skills from the virtual to the real world may depend on the sense of presence, or of being "in" the virtual environment. OBJECTIVES: To compare 1) the usability (in terms of performance, overall experience, and satisfaction), as well as 2) the sense of presence, in a wheelchair simulator with two display conditions: a head-mounted display (HMD) or a computer monitor. METHODS: Sixteen healthy adults practiced in the wheelchair simulator, first with a computer monitor display and then with an HMD. Task performance, cybersickness, presence, and overall experience in VR were assessed. RESULTS: Four of the participants were unable to complete all tasks in the HMD condition. When comparing the two display conditions, performance was the same, except for one task (bathroom) which took longer with the computer monitor. The HMD condition was rated as significantly higher in terms of sense of presence and VR experience but provoked more intense symptoms of cybersickness. DISCUSSION: Use of an HMD increased symptoms of cybersickness, with small gains in wheelchair performance. Thus, the use of an HMD may be warranted for the training of wheelchair skills, if tolerated by participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".