Classification of stance and swing gait states during treadmill walking from non-invasive scalp electroencephalographic (EEG) signals

Fernando San Martín Jorquera, Sara Grassi, Pierre André Farine, José Luis Contreras-Vidal

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations

Abstract

In [1] Contreras-Vidal and colleagues have shown the feasibility of inferring the linear and angular kinematics of treadmill walking from scalp EEG. Here, we apply a discrete approach to the same problem of decoding the human gait. By reducing the gait process to a mere succession of Stance and Swing phases for each foot, the average decoding accuracy reached 93.71%. This is sufficient to design a gait descriptor that relies only on this classification of two possible states for each foot over time as input, which could complement the model-based continuous decoding method that lacks in some aspects (foot placement at landing, weight acceptance, etc.)[5]. A final implementation of this method could be used in a powered exoskeleton to help impaired people regain walking capability.

Original languageEnglish (US)
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing
Pages507-511
Number of pages5
DOIs
StatePublished - 2013

Publication series

NameBiosystems and Biorobotics
Volume1
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

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