Please use this identifier to cite or link to this item:

TitleGait feature selection in walker-assisted gait using NSGA-II and SVM hybrid algorithm
Author(s)Martins, Maria Manuel Carvalho Freitas
Santos, Cristina
Costa, Lino
Frizera, Anselmo
KeywordsEvolutionary algorithms
Walker-assisted gait
Issue date2014
JournalEuropean Signal Processing Conference
Abstract(s)Nowadays, walkers are prescribed based on subjective standards that lead to incorrect indication of such devices to patients. This leads to the increase of dissatisfaction and occurrence of discomfort and fall events. Therefore, it is necessary to objectively evaluate the effects that walker can have on the gait patterns of its users, comparatively to non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information and this study addresses this problem by selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. In order to do this, it is proposed an approach that combines multi-objective genetic and support vector machine algorithms to discriminate differences. Results with healthy subjects have shown that the main differences are characterized by balance and joints excursion. Thus, one can conclude that this technique is an efficient feature selection approach.
TypeConference paper
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
  Restricted access
417,25 kBAdobe PDFView/Open    Request a copy!

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID