This chapter provides an overview of artificial neural network applications for the detection and classification of various gaits based on their typical characteristics. Gait analysis is routinely used for detecting abnormality in the lower limbs and also for evaluating the progress of various treatments. Neural networks have been shown to perform better compared to statistical techniques in some gait classification tasks. Various studies undertaken in this area are discussed with a particular focus on neural network's potential in gait diagnostics. Examples are presented to demonstrate the suitability of neural networks for automated recognition of gait changes due to aging from their respective gait patterns and their potential for identification of atrisk or nonfunctional gait.

Introduction

Neural networks have been shown to be successful in a variety of applications, including solving problems in biomedical, financial, and engineering areas. Recently, there has been particularly rapid growth in biomedical and healthrelated applications because of better predictive abilities and nonlinear modeling capabilities compared to traditional statistical techniques. Specifically, recognition of movement patterns, especially gait, has benefited as a result of neural network usages. The aim of this chapter is to provide an overview of neural network applications in movement pattern identification for possible use of neural networks as a diagnostic tool. A brief overview of gait analysis is first provided followed by neural network applications in various gait studies.