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Novel delta zero crossing regression features for gait pattern classification

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3 Author(s)
Ronny K. Ibrahim ; School of Electrical Engineering and Telecommunication, University of New South Wales, Australia ; Vidhyasaharan Sethu ; Eliathamby Ambikairajah

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.

Published in:

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Date of Conference:

Aug. 31 2010-Sept. 4 2010