Preliminary experimental method to quantify vibrations with various powered wheelchair set-ups
Bibliographic record
Abstract
Various problems are arisen from vibrations that are experienced during powered wheelchair (PWC) use. Therefore, it is difficult to determine the cause and impacts of vibrations, which may be important to inform PWC set-up, positioning and training. Indeed, during the design and prescription of PWC, different settings and components can be chosen like wheel types, active wheel position, setup of shock absorbing systems, etc.The objectives were to: (i) propose a preliminary experimental method to measure vibration in various PWC set-ups (e.g. wheel types, spring and damper adjustments, cushions) based on an instrumented PWC with accelerometers and pressure mattress sensors, and (ii) demonstrate the proposed experimental method to capture vibrations generated on the seat of the PWC and the torso of the PWC user for one type of perturbation (door threshold obstacle) and three different wheel types (pneumatic filled with air, anti-puncture wheel and pneumatic filled with gel).Results suggested that gel type wheels generated less vibrations when facing obstacle with low height. This preliminary experiment showed that the sensor apparatus is precise enough to capture differences in speed, obstacle variation and wheel types. Although statistical differences were observed, clinical significance must now be determined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".