Gait patterns classification using spectral features

R. K. Ibrahim, E. Ambikairajah, B. Celler, N. H. Lovell, L. Kilmartin

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

25 Citations (Scopus)


Accelerometry has been shown to be a good tool for ambulatory activity monitoring. This paper describes the use of spectral features for classification of gait activities based on accelerometric data. The classification is performed by a Gaussian mixture model (GMM) based statistical classifier at the back end. Fifty subjects participated in the experiment and an overall classification accuracy of 86% was achieved using the proposed 25 dimensional features for five different human gait patterns including walking on level surfaces, walking up and down stairs and walking up and down ramps.

Original languageEnglish
Title of host publicationThe IET Irish Signals and Systems Conference, ISSC 2008
Subtitle of host publication18-19 June 2008, National University Of Ireland, Galway, Ireland
Place of PublicationStevenage
PublisherInstitution of Engineering and Technology
Number of pages5
Edition539 CP
ISBN (Print)9780863419317
Publication statusPublished - 2008
Externally publishedYes
EventIET Irish Signals and Systems Conference, ISSC 2008 - Galway, Ireland
Duration: 18 Jun 200819 Jun 2008


OtherIET Irish Signals and Systems Conference, ISSC 2008


  • Accelerometry
  • Ambulatory monitoring
  • Feature extraction
  • Gait patterns


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