Wavelet-based characterization of gait signal for neurological abnormalities

Highlights • A robust gait classification scheme to characterize different neurological disorders. • A re-sampling technique to facilitate the use of time–frequency techniques is introduced. • A characteristic feature space quantifying the asymmetry characteristics of gait and the regularity of stri...

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Bibliographic Details
Published in:Gait & posture 2015-02, Vol.41 (2), p.634-639
Main Authors: Baratin, E, Sugavaneswaran, L, Umapathy, K, Ioana, C, Krishnan, S
Format: Article
Language:English
Subjects:
Adult
Aged
Amyotrophic Lateral Sclerosis - diagnosis
Amyotrophic Lateral Sclerosis - physiopathology
Automated diagnosis
Databases, Factual
Diagnosis, Computer-Assisted - methods
Female
Gait
Gait - physiology
Humans
Huntington Disease - diagnosis
Huntington Disease - physiopathology
Male
Middle Aged
Neurological disorders
Non-uniform sampling
Orthopedics
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Wavelet Analysis
Wavelet decomposition
Young Adult
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Summary:Highlights • A robust gait classification scheme to characterize different neurological disorders. • A re-sampling technique to facilitate the use of time–frequency techniques is introduced. • A characteristic feature space quantifying the asymmetry characteristics of gait and the regularity of stride have been suitably exploited for efficient gait signal discrimination. • A maximum average classification accuracy of 85% is obtained in classifying the pathological gait.
ISSN:0966-6362
1879-2219
DOI:10.1016/j.gaitpost.2015.01.012