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Record W4408562130 · doi:10.1016/j.apergo.2025.104487

Human slip perception while walking on ice: Can we rely on self-reported slip counts for winter footwear evaluations?

2025· article· en· W4408562130 on OpenAlexafffund
Davood Dadkhah, Hamed Ghomashchi, Tilak Dutta

Bibliographic record

VenueApplied Ergonomics · 2025
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlip (aerodynamics)PerceptionPoison controlEngineeringForensic engineeringEnvironmental scienceGeodesySimulationGeologyPsychologyMedicineMedical emergencyAerospace engineeringNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.321
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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