Predicting slips based on the STM 603 whole-footwear tribometer under different coefficient of friction testing conditions
Bibliographic record
Abstract
Assessing footwear slip-resistance is critical to preventing slip and fall accidents. The STM 603 (SATRA Technology) is commonly used to assess footwear friction but its ability to predict human slips while walking is unclear. This study assessed this apparatus’ ability to predict slips across footwear designs and to determine if modifying the test parameters alters predictions. The available coefficient of friction (ACOF) was measured with the device for nine different footwear designs using 12 testing conditions with varying vertical force, speed and shoe angle. The occurrence of slipping and the required coefficient of friction was quantified from human gait data including 124 exposures to liquid contaminants. ACOF values varied across the test conditions leading to different slip prediction models. Generally, a steeper shoe angle (13°) and higher vertical forces (400 or 500 N) modestly improved predictions of slipping. This study can potentially guide improvements in predictive test conditions for this device.Practitioner Summary: Frictional measures by the STM603 (SATRA Technology) were able to predict human slips under liquid contaminant conditions. Test parameters did have an influence on the measurements. An increased shoe-floor testing angle resulted in better slip predictions than test methods specified in the ASTM F2913 standard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".