Biomechanical Gait Analysis for the Extraction of Slip Resistance Test Parameters
Bibliographic record
Abstract
Falling accidents caused by slipping represent a high proportion of all accidents and are cost intensive in industry as well as in the private sphere. To prevent such accidents, the slip resistance of flooring must be evaluated. Therefore, measurement methods are necessary. These methods must provide results that comply with an individual's perception of when a floor is slippery. This article describes the analysis of human walking motion to derive essential parameters and estimate their values for measuring the slip resistance of flooring. Human walking motion of 22 persons is analysed to discover the critical phases for slipping. The heel strike was extracted as the critical phase for falling accidents caused by slipping. A model of the friction between the shoe and flooring is set up to describe the conditions in that phase. Heel strike velocity, requirements quotient and contact pressure are extracted as essential parameters from the friction model. With the biomechanical gait analyses of the walking of more than 170 single steps made by 22 test persons, values for these parameters are derived. Suggestions are made to adopt these values as test parameters for slip resistance test devices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".