TY - JOUR
T1 - Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network
AU - Ramirez-Bautista, Julian Andres
AU - Huerta-Ruelas, Jorge Adalberto
AU - Kóczy, László T.
AU - Hatwágner, Miklós F.
AU - Chaparro-Cárdenas, Silvia L.
AU - Hernández-Zavala, Antonio
N1 - Publisher Copyright:
© 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Computer-aided diagnosis
KW - Disease diagnosis
KW - Fuzzy cognitive maps
KW - Neural networks
KW - Optimization algorithm
KW - Plantar pressure alterations
UR - http://www.scopus.com/inward/record.url?scp=85078146702&partnerID=8YFLogxK
U2 - 10.1016/j.bbe.2019.12.008
DO - 10.1016/j.bbe.2019.12.008
M3 - Artículo
AN - SCOPUS:85078146702
SN - 0208-5216
VL - 40
SP - 404
EP - 414
JO - Biocybernetics and Biomedical Engineering
JF - Biocybernetics and Biomedical Engineering
IS - 1
ER -
