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Foot Plantar Pressure Estimation Using Artificial Neural Networks

Abstract : In this paper, we present a novel approach to estimate the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle. The proposed method is based on Artificial Neural Networks and can be utilized for the determination of the comfort that is related to the sole construction. Input parameters to the proposed neural network are the material properties and the thicknesses of the sole layers (insole and outsole). A set of simulation experiments has been conducted using analytic finite elements analysis in order to compile the necessary dataset for the training and validation of the neural network. Extensive experiments have shown that the developed method is able to provide an accurate alternative (more than 96 %) compared to the highly expensive, with respect to computational and human resources, approaches based on finite element analysis.
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https://hal.inria.fr/hal-01377425
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Elias Xidias, Zoi Koutkalaki, Panagiotis Papagiannis, Paraskevas Papanikos, Philip Azariadis. Foot Plantar Pressure Estimation Using Artificial Neural Networks. 12th IFIP International Conference on Product Lifecycle Management (PLM), Oct 2015, Doha, Qatar. pp.23-32, ⟨10.1007/978-3-319-33111-9_3⟩. ⟨hal-01377425⟩

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