Implementation of feature extraction methods and support vector machine for classification of partial body weight supports in overground robot-aided walking

Joana Figueiredo, Cristina P. Santos, Eloy Urendes, Jose L Pons, Juan C. Moreno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A general need for Wearable Robots (WRs) is to perform optimal selection of information for control and monitoring during daily assistance with an autonomous systems. A good feature selection algorithm is key to perform automated estimation of the mode of locomotion under environmental variations. Ambulatory body weight support (BWS) systems can be combined with WRs to provide safe ambulation and support in overground walking in cases of lower limb paralysis. This study aimed to develop a support vector machine (SVM) model for binary and multiclass classification that performs gait pattern recognition for different values of partial BWS during overground robot-aided walking. The principal component analysis (PCA) and kernel-based PCA (kPCA) were applied to improve the classification performance. As a result, the combination of temporal and kinematic features showed to improve the accuracy in the discrimination of gait patterns in healthy patients (88%). In SVM multiclass classification the 'one-against-one' approach showed to have a more stable performance (true positive and true negative rate are consistent) than 'one-against-all' approach and also lower computational cost both for training and SVM's decision making.

Original languageEnglish (US)
Title of host publication2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
PublisherIEEE Computer Society
Pages763-766
Number of pages4
ISBN (Electronic)9781467363891
DOIs
StatePublished - Jul 1 2015
Event7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France
Duration: Apr 22 2015Apr 24 2015

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2015-July
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
CountryFrance
CityMontpellier
Period4/22/154/24/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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