Usability of a virtual reality manual wheelchair simulator
Bibliographic record
Abstract
PURPOSE: Individuals with impaired mobility often require assistance for getting around. The skilled use of a manual wheelchair (MW) is required in order to gain independence while preventing injuries. Training in a virtual reality (VR) setting allows for safe practice of MW skills in a wide range of environments. We developed a low-cost MW simulator which includes visual and haptic feedback. Our objectives were to assess the usability and fidelity of the VR simulator, by clinicians and expert MW users, and to determine whether the addition of haptic feedback would positively improve the user's experience. MATERIALS AND METHODS: This mixed method study investigated the sense of presence, overall experience and ease of use of the experience in six MW users, as well as five clinicians (wheeled mobility experts) who practiced in the simulator. RESULTS: Participants reported a positive perception of usefulness, sense of presence, and immersion during the MiWe simulator experience. The addition of haptic feedback to the simulator significantly enhanced fidelity of the overall experience, compared to the no-feedback condition. CONCLUSION: Our low-cost simulator was well perceived by clinicians and MW users and was considered as a potentially useful tool to complement MW skill training.IMPLICATIONS FOR REHABILITATIONWe developed a low-cost, virtual reality simulator with visual and haptic feedback, for the practice of manual wheelchair skills.Expert clinicians and wheelchair users reported a positive experience after practicing in the wheelchair simulator, in terms of presence, realism and usability.Participants highlighted the potential usefulness of our low-cost simulator in the training of manual wheelchair skills.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".