An Orientation Estimator for the Wheelchair's Caster Wheels
Bibliographic record
Abstract
Wheelchair ergometers are a highly valuable tool in the study of the biomechanics of manual wheelchair propulsion. However, current ergometers have some drawbacks that affect their level of realism. For example, the moment of inertia of the wheelchair-user system and the caster wheels' orientation are usually neglected, despite their high influence on the wheelchair's behavior. Taking these factors into account requires a complex dynamic model, and the calibration of such a model requires on-the-field recordings of the caster wheels' orientation, which are currently difficult to obtain. In this paper, we have proposed an open-loop observer that estimates each caster wheel's orientation (CWO) based only on the rear wheels' kinematics. The model was validated by propelling the wheelchair on three different floors (vinyl, carpet, and concrete) with five different normal forces between the caster wheels and the ground. Comparison between the estimated CWO and a reference one recorded by an optoelectronic device gave an accuracy error of less than ±8°. This error reduced to ±5° when the wheelchair was propelled following straight or slightly curved patterns. This observer has implications in the design of better wheelchair ergometers and simulators, as well as in the control of electric wheelchairs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".