A generic kinematic pattern for human walking

Heba Lakany

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

The aim of this work is to investigate the existence of a generic feature vector based on kinematic data for normal walking. The paper describes a method to quantify generic features of the sagittal angles of the lower extremities of human subjects. The idea is to extract salient features from hip, knee and ankle sagittal angles to characterise normal and pathological walking. The algorithm is based on transforming the trajectories of the flexion/extension of joints of subjects using the continuous wavelet transform to represent a feature vector which is then fed to a self-organising map for clustering. The algorithm proved to be successful in distinguishing between normal subjects according to their age group, gender and also distinguishing between normal and pathological subjects. Rules are extracted from self-organising map to determine the salient features characterising each cluster as well as differentiating it from others.
Original languageEnglish
Pages (from-to)27-54
Number of pages28
JournalNeurocomputing
Volume35
Issue number1-4
DOIs
Publication statusPublished - 30 Nov 2000

Keywords

  • kinematic gait analysis
  • human walking
  • self-organising maps
  • wavelet transform
  • rule extraction
  • artificial neural networks
  • sagittal angles
  • joints

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