General Uncertainty Analysis for Manual Wheelchair Propulsion Dynamics and Development of an Instrumented Wheel
Bibliographic record
Abstract
Manual wheelchair propulsion (MWP) is an inefficient and physically straining process. A reliably fabricated and instrumented wheel can help researchers to accurately calculate the forces and moments exerted by the wheelchair users and propose strategies to improve MWP. In this study, an instrumented wheel is designed, fabricated, and validated by using general uncertainty analysis. A six-component transducer is used to measure three-dimensional forces and moments applied by the wheelchair user on the handrim. The output of the transducer are forces and moments, which are directly transmitted to a PC using a USB port. By developing the transformation equations, the actual forces and moments on the hand of the wheelchair user are calculated. The angular position of the hand on the handrim is calculated from the kinetic data obtained through the instrumented wheel, and the derived equations. The general uncertainty analysis method is used to calculate the uncertainty values for the variables of interest with the Taylor series expansions. An analysis of the results shows that it is possible to obtain reliable information for MWP by using the instrumented wheel. Most of the data have uncertainties under 5% during much of the propulsion phase, and the patterns and overall behavior of the results are comparable to published data.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".