Experiments in Gait Pattern Classification with Neural Networks of Adaptive Architecture

Clinical gait analysis is an area aiming at the provision of support for diagnoses and therapy considerations, the development of bio-feedback systems, and the recognition of effects of multiple diseases and still active compensation patterns during the healing process. The data recorded with ground reaction force measurement platforms is a convenient starting point for gait analysis. We discuss the usage of raw data from such measurement platforms for gait analysis and show how unsupervised artificial neural networks may be employed for gait malfunction identification. In this paper we provide our latest results in this line of research by using Incremental Grid Growing and Growing Grid networks for gait pattern classification. 1 Introduction Human gait analysis is particularly attractive to model because of its importance in everyday life and its complexity as a total body movement. In particular, pathological gait in humans following disease or injury is the subject of much contemp..