Neural network evaluation for shoe insoles fitness

The purpose of this research is to evaluate and analyze the fitness for human foot in different shoe insoles. We find the most-related evaluation between foot shapes and shoe insoles by using the grey-relational approach. Based on the plantar pressure measurement on the shoe insoles, we put them into the artificial neural network (ANN) as the training pair (pressure-insole) for network training. After training iterations, the network will have enough generalizing capability to classify the pattern of the plantar pressure. Back-propagation neural network (BPNN) is used to convert and classify shoe insoles. By referring to the classified results estimated by the network, a designer can make the best decision for where the design project is heading. Furthermore, this approach can effectively reduce the design-cycle time and meet the customers´ demands. Results and contributions in this paper are as follows. First of all, we conducted a foot experiment to verify research assumptions. Secondly, we investigated validity of using grey-relational approach to estimate the fitness of foot based on the plantar pressure data. Thirdly, we verified the validity of ANN´s learning and classifying capabilities. Lastly, we used ANN to learn from the fitness data and predict the most appropriate insoles for the foot.