Foot Plantar Pressure Estimation Using Artificial Neural Networks - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Foot Plantar Pressure Estimation Using Artificial Neural Networks

Elias Xidias
  • Fonction : Auteur
  • PersonId : 989995
Zoi Koutkalaki
  • Fonction : Auteur
  • PersonId : 989996
Panagiotis Papagiannis
  • Fonction : Auteur
  • PersonId : 989997
Paraskevas Papanikos
  • Fonction : Auteur
  • PersonId : 989998
Philip Azariadis
  • Fonction : Auteur
  • PersonId : 989999

Résumé

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.
Fichier principal
Vignette du fichier
421082_1_En_3_Chapter.pdf (618.11 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01377425 , version 1 (07-10-2016)

Licence

Paternité

Identifiants

Citer

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⟩
102 Consultations
266 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More