Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network

Julian Andres Ramirez-Bautista, Jorge Adalberto Huerta-Ruelas, László T. Kóczy, Miklós F. Hatwágner, Silvia L. Chaparro-Cárdenas, Antonio Hernández-Zavala

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.

Original languageEnglish
Pages (from-to)404-414
Number of pages11
JournalBiocybernetics and Biomedical Engineering
Issue number1
StatePublished - 1 Jan 2020
Externally publishedYes


  • Computer-aided diagnosis
  • Disease diagnosis
  • Fuzzy cognitive maps
  • Neural networks
  • Optimization algorithm
  • Plantar pressure alterations

Fingerprint Dive into the research topics of 'Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network'. Together they form a unique fingerprint.

Cite this