Human slip perception while walking on ice: Can we rely on self-reported slip counts for winter footwear evaluations?
Bibliographic record
Abstract
Fall-related injuries on icy surfaces are a major public health concern. Slip-resistant winter boots that incorporate the latest composite outsole technologies have demonstrated the potential to prevent falls in winter weather in lab-based testing. However, the real-world benefits of this composite footwear remain difficult to measure because of a lack of accurate evaluation methods. In particular, existing methods rely on comparing self-reported slip counts to identify differences in slip resistance performance between different footwear models. However, prior research has primarily focused on slip detection on soapy and oily surfaces, revealing that small slips ( ≤ 30 mm) often go undetected, with humans correctly identifying them only 50% of the time. No studies have yet examined slip perception on icy surfaces, which possess significantly lower coefficients of friction compared to soapy and oily environments. The objective of this study was to investigate the agreement between self-reported slip counts and motion capture detected slips while walking on ice with winter footwear. Twenty-five healthy participants were asked to walk on ice surfaces (melting ice 0.5 ± 1.0 °C and cold ice -3.5 ± 1.0 °C) while wearing three models of winter boots with varying slip resistance performance (poor, moderate, good) and were asked to report any slips they experienced. Ground truth slip identification and slip length measurement was done using an 8-camera Vicon motion capture system. Slips were categorized as small slips ( ≤ 30 mm), moderate slips (30–100 mm), or large slips ( > 100 mm) for each boot and the proportion detected by participants was calculated. A total of 7743 slips were identified from 53,944 steps captured by the motion capture system with 4395, 1999 and 1349 slips recorded from the boots with poor, moderate and good slip resistance, respectively. These included 1658 small slips, 2521 moderate slips, and 3564 large slips. Overall, participants only reported 38.3% of these slips including 375 small slips (22.6% reported), 823 moderate slips (32.6% reported) and 1767 large slips (49.6% reported). These findings showed a strong positive correlation between self-reported slips and slip length ( ρ = 0.573, p < 0 . 001 ) demonstrating that participants were significantly more likely to report larger slips. The findings of this study demonstrate the need to develop more objective methods of recording slip events for real-world winter footwear evaluations. • Investigates the agreement between self-reported slip counts and motion capture detected slips on icy surfaces. • Finds that participants reported only 38.3% of actual slip events recorded by motion capture. • Larger slips are significantly more likely to be reported than smaller slips, with a 49.6% report rate for large slips. • Concludes that self-reported slip counts may not provide a comprehensive measure of slip risk. • Demonstrates the need for more objective methods to evaluate winter footwear slip resistance in real-world conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".