Official websites use. Share sensitive information only on official, secure websites. Project Summary:Slip and fall accidents are a major and growing source of occupational injuries. Slip-resistant shoes with a highcoefficient of friction COF are effective at a reducing slipping risk. However, neither experts nor industry hasagreed upon a consistent set of criteria for labeling a shoe as slip-resistant. A consequence of this lack ofstandardization 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. Theproposed research aims to address this problem by developing a predictive model for shoe-floor-contaminantCOF based on shoe parameters that can be measured with little cost.
The overall objective of this R03 studyis to train and validate a statistical model for predicting the COF of footwear against a floor surface in thepresence of a liquid contaminant. The feasibility of this approach is supported by preliminary experimental andmodeling work conducted by the principal investigator. The proposed research will accomplish this goal withtwo aims: Aim 1: Develop and mechanically validate a statistical model for predicting the shoe COF based onthe characteristics of the shoe outsole; Aim 2: Validate the findings of Aim 1 using unexpected slips of humansubjects.
To accomplish Aim 1, a shoe tribometer that mimics the under-shoe conditions during slipping willmeasure coefficient of friction across fifty shoes, three floor surfaces quarry tile, vinyl tile and ceramic tile , andthree 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 predictionvariables 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. For Aim 2, thirty individuals willbe unexpectedly slipped to determine if the developed model can predict actual slipping risk.
