Classificação de dados cinéticos da inicialização da marcha utilizando redes neurais artificiais e máquinas de vetores de suporte
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2015-07-01
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Universidade Federal de Goiás
Resumo
The aim of this work was to assess the performance of computational methods to classify ground reaction force (GRF) to identify on which surface was done the gait initiation. Twenty-five subjects were evaluated while performing the gait initiation task in two experimental conditions barefoot on hard surface and barefoot on soft surface (foam). The center of pressure (COP) variables were calculate from the GRF and the principal component analysis was used to retain the main features of medial-lateral, anterior-posterior and vertical force components. The principal components representing each force component were retained using the broken stick test. Then the support vector machines and multilayer neural networks ware trained with Backpropagation and Levenberg-Marquartd algorithm to perform the GRF classification . The evaluation of classifier models was done based on area under ROC curve and accuracy criteria. The Bootstrap cross-validation have produced area under ROC curve a and accuracy criteria using 500 samples database. The support vector machine with linear kernel and margin parameter equal 100 produced the best result using medial-lateral force as input. It registered area under ROC curve and accuracy with 0.7712 and 0.7974. Those results showed significance difference from the vertical and anterior-posterior force. Then we may conclude that the choice of GRF component and the classifier model directly influences the performance of the classification.
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Redes neurais artificiais , Máquinas de vetores de suporte , Análise de componentes principais , Inicialização da marcha , Acurácia, área abaixo da curva ROC , Força de reação do solo , Artificial neural network , Support vector machine , Principal component analysis , Gait initiation , Accuracy , Area under a ROC curve , Ground reaction force
Citação
TAKAÓ, T. B. Classificação de dados cinéticos da inicialização da marcha utilizando redes neurais artificiais e máquinas de vetores de suporte. 2015. 71 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2015.