Estimating Human-Centered Slip-Resistance of Winter Footwear on Ice Using Mechanical Testing

Area of Science:

• Biomechanics and Materials Science
• Footwear Engineering
• Injury Prevention Research

Background:

• Slips and falls are significant causes of injuries.
• Slip-resistant footwear is key to prevention.
• Human-centered tests are accurate but costly; mechanical tests are efficient but less so.

Purpose of the Study:

• To develop a predictive model for estimating human-centered slipperiness scores (Maximum Achievable Angle - MAA).
• To utilize mechanical slip resistance measurements for accessible footwear evaluation.
• To bridge the gap between efficient mechanical testing and realistic human locomotion dynamics.

Main Methods:

• Thirty-seven winter footwear samples were tested.
• Both human-centered MAA testing and SATRA STM 603 mechanical testing were employed on wet and dry ice.
• Ten regression models were trained and validated using cross-validation, with performance assessed by RMSE, MAE, MAPE, R², and Bland-Altman analysis.

Main Results:

• The Ridge regression model demonstrated the most consistent predictive performance.
• On wet ice, the Ridge model achieved an RMSE of 2.73° and MAE of 2.27°.
• On dry ice, the Ridge model achieved an RMSE of 1.58° and MAE of 1.33°.
• Results showed promising accuracy, though slight deviations indicate room for improvement.

Conclusions:

• Mechanical slip resistance metrics can effectively predict human-centered MAA outcomes.
• The developed predictive model shows promise for footwear slip resistance assessment.
• Further refinement is necessary to meet stringent precision requirements for certification.