Investigating the Reliability and Validity of Three Novel Virtual Reality Environments With Different Approaches to Simulate Wheelchair Maneuvers
Bibliographic record
Abstract
Wheelchair manoeuvring has received little attention in the literature despite its importance in mobility and performing activities of daily living and its role in developing secondary injuries for wheelchair users. The focus in this paper was technology development with iterative and proof-of-concept testing. Three versions of a wheelchair simulator that were designed and developed for simulating curvilinear wheelchair propulsion in virtual reality were tested for their validity and reliability. The wheelchair simulators comprise a sophisticated wheelchair ergometer in an immersive virtual reality environment and are developed for manual wheelchair propulsion. These simulators all replicate inertia in translation, in addition to taking three approaches for simulating turning. The three systems were then tested and compared with the real world to see how reliable and valid they are; 15 healthy participants were recruited to perform the Illinois Agility Test (IAT) in two sessions that were at least one week apart. The intraclass correlation coefficient and the Pearson correlation coefficient were found for 16 variables to find the test-retest reliability and convergent construct validity of the systems, respectively. Overall, the three systems showed good validity and reliability with the VR_system 2 (mechanical compensation for rotational inertia) having the best scores and the VR_system 3 (software compensation for rotational inertia) having the lowest scores. Also, it was observed that performing IAT in the real world needed fewer pushes and often accompanied more negative pushes. Participants also used longer strokes in the real world compared to virtual reality environment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".