TY - GEN
T1 - Detection of Human Footprint Alterations by Fuzzy Cognitive Maps Trained with Genetic Algorithm
AU - Ramirez-Bautista, Julian Andres
AU - Hernandez-Zavala, Antonio
AU - Huerta-Ruelas, Jorge A.
AU - Hatwagner, Miklos F.
AU - Chaparro-Cardenas, Silvia L.
AU - Koczy, Laszo T.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Mobility is an important part of our daily life, hence the good health of our lower extremities is essential. Gait analysis using kinetic data along with medical Decision Support System or Computer Aided Diagnosis provide to physicians support in gait disorder detection, the risk of foot ulcerations especially in diabetic patients, leg discrepancy, footprint pathologies, and many other applications in biomedical diagnosis. To increase confidence in the system, it is necessary to use a technique which uses a comprehensive reasoning and provide explanations to discover new relationships and combination of features. The present research is an attempt to assess the viability of investigating human footprint alterations using Fuzzy Cognitive Maps (FCM) combined with a Genetic Algorithm (GA), and it is part for preparation of investigating more efficient algorithms in the future. In the proposed method, GA is used to learn the weight matrix of an FCM model applied to identify alterations in the human footprint. Using historical plantar pressure data obtained by electronic platforms, combined with FCM and optimization algorithm, a promising outcome is presented in the field of Computer-Aided Diagnosis.
AB - Mobility is an important part of our daily life, hence the good health of our lower extremities is essential. Gait analysis using kinetic data along with medical Decision Support System or Computer Aided Diagnosis provide to physicians support in gait disorder detection, the risk of foot ulcerations especially in diabetic patients, leg discrepancy, footprint pathologies, and many other applications in biomedical diagnosis. To increase confidence in the system, it is necessary to use a technique which uses a comprehensive reasoning and provide explanations to discover new relationships and combination of features. The present research is an attempt to assess the viability of investigating human footprint alterations using Fuzzy Cognitive Maps (FCM) combined with a Genetic Algorithm (GA), and it is part for preparation of investigating more efficient algorithms in the future. In the proposed method, GA is used to learn the weight matrix of an FCM model applied to identify alterations in the human footprint. Using historical plantar pressure data obtained by electronic platforms, combined with FCM and optimization algorithm, a promising outcome is presented in the field of Computer-Aided Diagnosis.
KW - Disease diagnosis
KW - Fuzzy Cognitive Maps
KW - Genetic Algorithm
KW - clinical decision aid systems
KW - plantar alterations
UR - http://www.scopus.com/inward/record.url?scp=85092033712&partnerID=8YFLogxK
U2 - 10.1109/MICAI46078.2018.00013
DO - 10.1109/MICAI46078.2018.00013
M3 - Contribución a la conferencia
AN - SCOPUS:85092033712
T3 - Proceedings of the Special Session - 2018 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
SP - 32
EP - 38
BT - Proceedings of the Special Session - 2018 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
A2 - Batyrshin, Ildar
A2 - de Lourdes Martinez Villasenor, Maria
A2 - Espinosa, Hiram Eredin Ponce
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
Y2 - 22 October 2018 through 27 October 2018
ER -
