Novel delta zero crossing regression features for gait pattern classification

Ronny K. Ibrahim, Vidhyasaharan Sethu, Eliathamby Ambikairajah

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

    1 Citation (Scopus)

    Abstract

    Many recent research works on gait pattern classification indicates that static features are used. This paper describes of extracting novel dynamic features as complimentary features for the gait pattern classification. The dynamic features are obtained by using regression on the delta zero crossing counts (ΔZCC) of the acceleration signal. The classification results using the filterbank features with the novel dynamic features showed an overall accuracy of 97% was achieved. This is an improvement of 3% from using the filterbank features alone.

    Original languageEnglish
    Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages2427-2430
    Number of pages4
    ISBN (Electronic)9781424441242
    ISBN (Print)9781424441235
    DOIs
    Publication statusPublished - 2010
    Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
    Duration: 31 Aug 20104 Sep 2010

    Other

    Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
    CountryArgentina
    CityBuenos Aires
    Period31/08/104/09/10

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