A New Wheelchair Ergometer Designed as an Admittance-Controlled Haptic Robot
Bibliographic record
Abstract
Wheelchair ergometers aim to simulate the propulsion of a wheelchair in a controlled laboratory setup. One drawback of current ergometers is that the simulated wheelchair is always modeled as a simple unidimensional mass and friction, which do not allow a correct simulation of turning maneuvers. In this paper, we present a new design for a wheelchair ergometer based on haptic robotics. This ergometer allows us to simulate any linear or nonlinear model of the wheelchair-user system in real time, including models that implement turning maneuvers. The presented prototype was validated experimentally. The rear wheels of the ergometer match the rear wheels' velocities of the simulated wheelchair with a root-mean-square error of 0.9 %. Therefore, the ergometer's accuracy is mainly bounded by the accuracy of the wheelchair-user model, which means that future improvements of the wheelchair-user model will be directly reflected by the ergometer. The conditions for stability were also evaluated. A minimal simulated mass of 18 kg and a minimal simulated moment of inertia of 1 kg ·m2are needed. These requirements are encountered by any wheelchair-user combination.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".