Equine Gait Data Analysis using Signal Transforms as a Preprocessor to Back Propagation Networks

Abstract

This thesis examines using Back Propagation network in the analysis of equine gait data. Back Propagation networks are capable of classifying non-linear data sets, but are not usually built to handle time series data. By using Fourier and wavelet transforms as a pre-processor, the Back Propagation network is then able to overcome this hurdle. It was then able to analyze and classify gait, shoeing and direction in the gait data quite accurately and effectively. Several methods proved to be more effective than others.