Slip and fall accidents are a major and growing source of occupational injuries. Slip-resistant shoes with a high coefficient of friction (COF) are effective at a reducing slipping risk. However, neither experts nor industry has agreed upon a consistent set of criteria for labeling a shoe as slip-resistant. A consequence of this lack of standardization is that significant variability exists across shoes that are labeled slip-resistant. Furthermore, independent testing of shoe COF is expensive, which may limit its use by employers and employees. The proposed research aims to address this problem by developing a predictive model for shoe-floor-contaminant COF based on shoe parameters that can be measured with little cost. The overall objective of this R03 study is to train and validate a statistical model for predicting the COF of footwear against a floor surface in the presence of a liquid contaminant. The feasibility of this approach is supported by preliminary experimental and modeling work conducted by the principal investigator. The proposed research will accomplish this goal with two aims:
Aim 1 : Develop and mechanically validate a statistical model for predicting the shoe COF based on the characteristics of the shoe outsole;
Aim 2 : Validate the findings of Aim 1 using unexpected slips of human subjects. To accomplish Aim 1, a shoe tribometer that mimics the under-shoe conditions during slipping will measure coefficient of friction across fifty shoes, three floor surfaces (quarry tile, vinyl tile and ceramic tile), and three contaminant conditions (water, detergent solution and canola oil). ANOVA methods and a ten-fold cross- validation method will be used to identify the most predictive model and quantify its accuracy. The prediction variables will include outsole hardness, contact area, tread orientation, floor roughness and fluid viscosity. Regression methods will be used to test the hypothesis that the model predicts shoe-floor COF (H1.1). Also, ANOVA methods will be used to test the hypotheses that shoe features (hardness, tread contact area and tread orientation) influence hysteresis COF (H1.2) and adhesion COF (H1.3).
For Aim 2, thirty individuals will be unexpectedly slipped to determine if the developed model can predict actual slipping risk. The model developed in Aim 1 will be used to predict slips based on measured outsole design features. Subjects will be randomly assigned across these outsole types during two unexpected slips. A logistic regression analysis will test the hypothesis that the model predicted slips across the subject population based on shoe tread measurements (H2). This proposed research promotes Research to Practice (R2P) concepts by making assessment of shoe slip-resistance more accessible to employees and employers. The outputs of this research will be newly developed mathematical relationships between footwear features and slip-resistance. The outcomes will be improved shoe design and selection policies that lead to a reduction in slip and fall accidents. This research will address NORA Strategic Goals for Manufacturing (Goal 2), Wholesale and Retail Trade (Goal 2) and Services (in 9 different research goals).
Slip and fall accidents cause a large number of serious occupational injuries and fatalities. Although slip- resistant shoes are an effective counter-measure for preventing these accidents, significant variability exists across shoes that are labeled as slip-resistant. The purpose of this research is to develop a mathematical relationship between shoe outsole features and the resulting COF in order to guide selection of effective slip- resistant footwear.
