The Engineering of Sport 6

pp 403-408

Modelling Traction of Studded Footwear on Sports Surfaces using Neural Networks

  • Bob KirkAffiliated withSports Engineering Research Group, University of Sheffield
  • , Matt CarréAffiliated withSports Engineering Research Group, University of Sheffield
  • , Stephen HaakeAffiliated withSports Engineering, CSES, Sheffield Hallam University
  • , Graeme MansonAffiliated withDynamics Research Group, University of Sheffield

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Traditional regression techniques have shown limited use in the development of empirical models for the traction performance quantities of studded footwear on surfaces. This is due to the unknown and often non-linear relationships between performance parameters, such as traction force, and input variables, from the shoe and surface. Experimental data has been used to train artificial neural networks to model the relationship between stud parameters, namely cross-sectional area, length and two shape coefficients, with dynamic traction as the output variable. A variety of neural network structures and optimisation algorithms were evaluated. The most promising network gave an average prediction error of 10%, compared to an error of 36% when an optimised linear model is employed. This study shows that the neural network technique has powerful potential in understanding the effect of shoe and surface parameters and in the optimisation of traction forces experienced by athletes.