Prediction of coefficient of friction based on footwear outsole features

Traction testing of footwear is expensive, which may create barriers for certain users to assess footwear. This study aimed to develop a statistical model that predicts available coefficient of friction (ACOF) under boundary lubrication conditions based on inexpensive measurements of footwear outsole features. Geometric and material hardness parameters were measured from fifty-eight footwear designs labeled as slip-resistant. A robotic friction measurement device was used to quantify ACOF with canola oil as the contaminant. Stepwise regression methods were used to develop models based on the outsole parameters and floor type to predict ACOF. The predictive ability of the regression models was tested using the k-fold cross-validation method. Results indicated that 87% of ACOF variation was explained by three shoe outsole parameters (tread surface area, heel shape, hardness) and floor type. This approach may provide an assessment tool for safety practitioners to assess footwear traction and improve workers' safety.