Development of a Slip Analysis Algorithm: Automating the Maximal Achievable Angle Footwear Slip Resistance Test
Bibliographic record
Abstract
The use of slip-resistant footwear can prevent falls due to slips, which are a common cause of traumatic injuries. Measuring winter footwear slip resistance with the recently developed Maximum Achievable Angle test currently requires a human observer to identify slips in real time, which is challenging and subject to inter-observer variability. This thesis presents an algorithm for detecting and classifying slips on icy slopes as well as estimating slip distances. A machine learning algorithm was trained and validated using motion capture data from 11,000 steps including 4,700 slips from nine healthy young adults. The overall slip detection accuracy was 91.0%. Slips were classified as one of four types: backward toe slips, forward toe slips, backward heel slips, and forward heel slips with accuracies of 97.3%, 54.7%, 82.6%, and 87.3%, respectively. Finally, the algorithm was able to estimate slip distances with accuracies of 3±5%, 26±67%, 4±6%, and 3±13%, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".