Gait pattern classification using compact features extracted from intrinsic mode functions.
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.
Ibrahim
Ronny K
RK
School of Electrical Engineering and Telecommunication, University of New South Wales, Australia. z3153320@student.unsw.edu.au
Ambikairajah
Eliathamby
E
Celler
Branko G
BG
Lovell
Nigel H
NH
D000054:Acceleration; D000328:Adult; D000368:Aged; D001330:Electronic Data Processing; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D016138:Walking / Q000502:physiology*; D016474:Weight-Bearing / Q000502:physiology