Foot Plantar Pressure Estimation Using Artificial Neural Networks

  • Elias Xidias
  • Zoi Koutkalaki
  • Panagiotis Papagiannis
  • Paraskevas Papanikos
  • Philip AzariadisEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 467)


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.


Artificial neural network Foot plantar pressure Mechanical comfort 



This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program “ARISTEIA”.


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Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Elias Xidias
    • 1
  • Zoi Koutkalaki
    • 1
  • Panagiotis Papagiannis
    • 1
  • Paraskevas Papanikos
    • 1
  • Philip Azariadis
    • 1
    Email author
  1. 1.Department of Product and Systems Design EngineeringUniversity of the AegeanErmoupoliGreece

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