Driving performance in a power wheelchair simulator
Bibliographic record
Abstract
Purpose: A power wheelchair simulator can allow users to safely experience various driving tasks. For such training to be efficient, it is important that driving performance be equivalent to that in a real wheelchair. This study aimed at comparing driving performance in a real and in a simulated environment. Method: Two groups of healthy young adults performed different driving tasks, either in a real power wheelchair or in a simulator. Smoothness of joystick control as well as the time necessary to complete each task were recorded and compared between the two groups. Driving strategies were analysed from video recordings. The sense of presence, of really being in the virtual environment, was assessed through a questionnaire. Results: Smoothness of joystick control was the same in the real and virtual groups. Task completion time was higher in the simulator for the more difficult tasks. Both groups showed similar strategies and difficulties. The simulator generated a good sense of presence, which is important for motivation. Conclusions: Performance was very similar for power wheelchair driving in the simulator or in real life. Thus, the simulator could potentially be used to complement training of individuals who require a power wheelchair and use a regular joystick.Implications for RehabilitationIndividuals who require a power wheelchair for mobility often lack sufficient training, which may be supplemented through the use of a simulator.For such training to be efficient, it is important that performance in the simulator and in a real wheelchair be equivalent.This study showed that driving performance, strategies used and difficulties encountered were very similar, for groups of healthy adults driving in the simulator or in a real power wheelchair.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".