The Effects of Altering the Center of Pressure in Standing Subjects Exposed to Foot-Transmitted Vibration on an Optimized Lumped-Parameter Model of the Foot
Bibliographic record
Abstract
Many workers are exposed to foot-transmitted vibration, which can lead to the development of vibration-induced white foot: a debilitating condition with neurological, vascular and osteoarticular symptoms. To design effective prevention mechanisms (i.e., boots and insoles) for isolating workers from vibration exposure, continued model development of the foot’s biodynamic response in different positions is necessary. This study uses a previously developed model of the foot–ankle system (FAS) to investigates how altering the center of pressure (COP) location can change the biodynamic response of the FAS to standing vibration exposure. Formerly published experimental responses for apparent mass and transmissibility at five anatomical locations in three COP positions were used to optimize the model. Differences occurred with the Kelvin–Voigt elements used to represent the soft tissues of the foot sole: at the heel, the distal head of the metatarsals and distal phalanges. The stiffness increased wherever the COP was concentrated (i.e., forward over the toes or backward over the heel). The variability of the model parameters was always greatest when the COP was concentrated in the heel. This suggests future FAS models need to more clearly address how the soft tissue of the plantar fat pad is modelled.
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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 | 0.001 |
| 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) | 0.002 | 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".