Predictive Forward Dynamic Simulation of Manual Wheelchair Propulsion on a Rolling Dynamometer
Bibliographic record
Abstract
Research studies to understand the biomechanics of manual wheelchair propulsion often incorporate experimental data and mathematical models. This project aimed to advance this field of study by developing a two-dimensional (2D) model to generate first of its kind forward dynamic fully predictive computer simulations of a wheelchair basketball athlete on a stationary ergometer. Subject-specific parameters and torque generator functions were implemented in the model from dual X-ray absorptiometry and human dynamometer measurements. A direct collocation optimization method was used in a wheelchair propulsion model for the first time to replicate the human muscle recruitment strategy. Simulations were generated for varying time constraints and seat positions. Similar magnitudes of kinematic and kinetic data were observed between simulation and experimental data of a first push. Furthermore, seat heights inferior to the neutral position were found to produce similar joint torques to those reported in previous studies. An anterior seat placement produced the quickest push time with the least amount of shoulder torque required. The work completed in this project demonstrates that fully predictive simulations of wheelchair propulsion have the potential of varying simulation parameters to draw meaningful conclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".