title: A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.
abstract: The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.
authors.affiliation: a UFMG , Belo Horizonte, MG , Brazil.
authors.forename: Andre
authors.initials: A
authors.lastname: Andrade
authors.forename: Marcelo
authors.initials: M
authors.lastname: Costa
authors.forename: Leopoldo
authors.initials: L
authors.lastname: Paolucci
authors.forename: Antônio
authors.initials: A
authors.lastname: Braga
authors.forename: Flavio
authors.initials: F
authors.lastname: Pires
authors.forename: Herbert
authors.initials: H
authors.lastname: Ugrinowitsch
authors.forename: Hans-Joachim
authors.initials: HJ
authors.lastname: Menzel
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012727:Sex Characteristics; D016474:Weight-Bearing
keywords: gender classification; ground reaction force; multi-objective; neural networks