Time-frequency based features for classification of walking patterns

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

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

    26 Citations (Scopus)

    Abstract

    The analysis of gait data has been a challenging problem and several new approaches have been proposed in recent years. This paper describes a novel front-end for classification of gait patterns using data obtained from a tri-axial accelerometer. The novel features consist of delta features, low and high frequency signal variations and energy variations in both frequency bands. The back-end of the system is a Gaussian mixture model based classifier. Using Bayesian adaptation, an overall classification accuracy of 96.1% was achieved for five walking patterns.

    Original languageEnglish
    Title of host publication2007 15th International Conference on Digital Signal Processing, DSP 2007
    Subtitle of host publication1-4 July 2007, Cardiff University, Wales, UK
    EditorsSaeid Sanei, Jonathon A. Chambers, John McWhirter, Yulia Hicks, Anthony G. Constantinides
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages187-190
    Number of pages4
    ISBN (Print)1424408822, 9781424408825
    DOIs
    Publication statusPublished - 2007
    Event15th International Conference on Digital Signal Processing - Cardiff
    Duration: 1 Jul 20074 Jul 2007

    Conference

    Conference15th International Conference on Digital Signal Processing
    CityCardiff
    Period1/07/074/07/07

    Keywords

    • gait patterns
    • accelerometry
    • ambulatory monitoring
    • Gaussian mixture models

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