The Effect of Resultant Force at the Pushrim on Shoulder Kinetics During Manual Wheelchair Propulsion: A Simulation Study
Bibliographic record
Abstract
The aim of this study was to determine, by simulation on real data, the effect of modifying the direction or effectiveness of a given force amplitude on the load sustained by the shoulder estimated by joint forces and moments. Kinematics and kinetics data were recorded on 14 manual wheelchair users 68.2+/-5.2 years for 10 s at sub-maximal speed (0.96-1.01 m/s). The simulation consisted in modifying force effectiveness at the pushrim while maintaining the same initial force amplitude. Shoulder kinetics were computed for simulated resultant forces from radial to tangent directions and also for initial force effectiveness. The results show that as the force was simulated tangent to the wheel, there was a significant increase in the average proximal and anterior shoulder joint forces. Also, significant increases in average internal rotation, flexion in the sagittal and horizontal plane moments were reported. Higher shoulder kinetics could accelerate the onset of fatigue and increase the risk of injury. A single-case analysis revealed an improvement window for force effectiveness ( approximately 10%) in which shoulder kinetics were not substantially increased. Our results provide useful information on what would happen to shoulder kinetics if we were able to teach manual wheelchair users to modify their force pattern at the pushrim. The results suggest that for an elderly population, it is not wise to aim at producing a mechanically optimal resultant force at the pushrim (i.e., tangent). Smaller increases of the initial force effectiveness would be preferable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".