Measurement uncertainties during manual wheelchair propulsion and shoulder kinetics
Bibliographic record
Abstract
Joint forces and moments are often used to estimate the load sustained by joints during a specific task. However,their accuracy is dependent upon the validity of the instrumentation. The SMARTwheel is often used to record the forces and moments exerted by the hand during manual wheelchair propulsion. These forces and moments are then used as inputs to an inverse dynamic model to estimate joint kinetics. A study has shown that this force sensing device as a certain uncertainty (∼1-5%) depending on the variable. Yet no information is available on the impact on the shoulder load of this measurement uncertainty. The purpose of this paper is to compute the uncertainty of the forces and moments measured by the SMARTwheel during manual wheelchair (MWC) propulsion and determine the impact on shoulder kinetics. Fourteen elderly MWC users were tested in an ergometer. They had to propel at submaximal speed (∼ 1m\s) and kinematic and kinetic data were recorded for 10 seconds. Uncertainties on the pushrim forces and moments were computed and added to the initial pushrim kinetics. Inverse dynamic model was used to estimate shoulder joint forces and moments for the initial kinetics condition without and with uncertainty. The results suggest that the uncertainty accounts for variability less than 1 N for the forces and 1 Nm for the moments at the shoulder.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".