Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.
Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach.
Industrial Electronics Department, University of Minho, Guimarães, Portugal. Electronic address: mariam@dei.uminho.pt.
Maria
M
Martins
Production and Systems Department, University of Minho, Guimarães, Portugal. Electronic address: lac@dps.uminho.pt.
Lino
L
Costa
Electrical Engineering Department, Federal University of Espirito Santo (UFES), Av. Fernando Ferrari, 514, Vitoria, ES, Brazil. Electronic address: anselmo@ele.ufes.br.
Anselmo
A
Frizera
Bioengineering Group, Spanish National Research Council (CSIC), Crta. Campo Real Km 0.200 - Arganda del Rey, Madrid, Spain. Electronic address: ceres@iai.csic.es.
Ramón
R
Ceres
Industrial Electronics Department, University of Minho, Guimarães, Portugal. Electronic address: cristina@dei.uminho.pt.
Cristina
C
Santos
D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D019295:Computational Biology; D051341:Dependent Ambulation / Q000502:physiology* / Q000706:statistics & numerical data; D005684:Gait / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D012046:Rehabilitation / Q000295:instrumentation / Q000706:statistics & numerical data; D060388:Support Vector Machine; D014741:Video Recording; D014853:Walkers; D055815:Young Adult
Evolutionary algorithms; Gait analysis; Walker assistance