A new dynamic model of the wheelchair propulsion on straight and curvilinear level-ground paths
Bibliographic record
Abstract
Independent-roller ergometers (IREs) are commonly used to simulate the behaviour of a wheelchair propelled in a straight line. They cannot, however, simulate curvilinear propulsion. To this effect, a motorised wheelchair ergometer could be used, provided that a dynamic model of the wheelchair-user system propelled on straight and curvilinear paths (WSC) is available. In this article, we present such a WSC model, its parameter identification procedure and its prediction error. Ten healthy subjects propelled an instrumented wheelchair through a controlled path. Both IRE and WSC models estimated the rear wheels' velocities based on the users' propulsive moments. On curvilinear paths, the outward wheel shows root mean square (RMS) errors of 13% in an IRE vs 8% in a WSC. The inward wheel shows RMS errors of 21% in an IRE vs 11% in a WSC. Differences between both models are highly significant (p < 0.001). A wheelchair ergometer based on this new WSC model will be more accurate than a roller ergometer when simulating wheelchair propulsion in tight environments, where many turns are necessary.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".