A Simplified Upper-Body Model to Improve the External Validity of Wheelchair Simulators
Bibliographic record
Abstract
During over-ground wheelchair propulsion, upper-body (UB) movement causes intracycle velocity variations that are neglected by current wheelchair simulators. This could affect the external validity of wheelchair propulsion on simulators. In this study, we investigated ways to incorporate these dynamics into the dynamic model(DM) reproduced by wheelchair simulators. We aimed to maximize the DM accuracy and minimize the number of required inputs. First, two DMs were presented: Model RL represented propulsion on a typical roller-based wheelchair simulator and model UB represented over-ground propulsion, modeling the UB as five rigid bodies. Then, three new DMs were presented: Model trunk (TR), model upper arm (UA), and model forearm (FA); these models simplified model UB by estimating the UB kinematics based on the acceleration of only one segment. For all DMs, wheelchair velocity prediction was tested over-ground at a self-selected velocity among 19 experienced manual wheelchair users with a spinal cord injury. UB kinematics was reconstructed based on personalized kinematic patterns recorded on a wheelchair simulator. Models UB and UA were the most accurate: they reduced the root-mean-square intracycle velocity prediction error from 0.044 m/s (RL) to 0.026 m/s (UB) and 0.024 m/s (UA), and reduced the velocity peak time prediction error from -27.7% (RL) to 1.7% (UB) and -7.3% (UA). Implementing model UA instead of model RL on a wheelchair simulator may improve the external validity of wheelchair propulsion on a simulator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".