Analysis of movement to develop a virtual reality powered-wheelchair simulator
Bibliographic record
Abstract
Children with limitations in mobility greatly benefit from training to acquire the skills necessary for the safe operation of a powered wheelchair (PW). Proper training leads to safer and more frequent use of the PW, which in turn has an important impact on quality of life. Training can be difficult to accomplish, especially in children with sever physical or cognitive disabilities. A virtual reality based simulator may constitute an adequate solution for the practice of PW driving skills in these individuals, by providing a graded training program in a safe environment. We propose to build a simulator that would incorporate three-dimensional visual feedback using VR equipment, as well as inertial feedback through the use of a motion platform. This would provide a realistic setting, as users would experience the appropriate inertial forces that accompany acceleration, rotation and changes in inclination during PW driving. The programming of these forces relies on the measurement of movement in a real PW. We report here the results of a first experiment involving the measurement of acceleration during various PW driving tasks, including starting, stopping, turning and moving on inclined planes. Our analyses indicate that the movement of a real PW could be adequately reproduced by the motion platform. Implications for the design of the simulator are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 | 0.000 |
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".