Gait pattern classification using compact features extracted from intrinsic mode functions

Ronny K. Ibrahim, Eliathamby Ambikairajah, Branko G. Celler, Nigel H. Lovell

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

    10 Citations (Scopus)

    Abstract

    Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved. 

    Original languageEnglish
    Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages3852-3855
    Number of pages4
    ISBN (Print)9781424418152
    DOIs
    Publication statusPublished - 2008
    Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
    Duration: 20 Aug 200825 Aug 2008

    Other

    Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
    CountryCanada
    CityVancouver, BC
    Period20/08/0825/08/08

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