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 contributionpeer-review

    27 Citations (Scopus)


    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)
    Number of pages4
    ISBN (Print)1424408822, 9781424408825
    Publication statusPublished - 2007
    Event15th International Conference on Digital Signal Processing - Cardiff
    Duration: 1 Jul 20074 Jul 2007


    Conference15th International Conference on Digital Signal Processing


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


    Dive into the research topics of 'Time-frequency based features for classification of walking patterns'. Together they form a unique fingerprint.

    Cite this