Abnormal gait detection using discrete wavelet transform in fourier domain

Abstract

Detection of gait characteristics has found considerable interest in fields of biomechanics and rehabilitation sciences. In this paper an approach for abnormal gait detection employing Discrete Fourier Transform (DFT) followed by Discrete Wavelet transform (DWT) analysis has been presented. The joint angle characteristics in frequency domain have been analyzed and using the harmonic coefficient recognition for abnormal gait has been performed. A classification of gaits has been attempted using k-means clustering based on the data acquired from DFT and DWT. Future work will be the expansion of the detection introduced in this system to include abnormality detection instead of just an abnormal or normal detection that would prove to be a valuable addition for use in a variety of applications including unobtrusive clinical gait analysis, automated surveillance etc. © 2008 Springer-Verlag.