An analysis of greyhound gait using wavelets

In this paper we present a method of analysing gait of quadrupedal animals using wavelets. Time series data such as joint trajectories can be decomposed by the discrete wavelet transform to represent components of different frequency bandwidth. Differences between two similar trajectories can be detected by comparing the components of the same bandwidth. We analysed kinematic data of the hindlimbs of three greyhounds with normal gait and with tibial nerve paralysis. Abnormalities in the gait patterns were detected and quantified by comparing the energy contribution of the components that were present at the same level of the wavelet transform.