Analyzing human gait patterns for malfunction detection

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 force platforms for gait analysis and show how supervised 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 Radial Basis Function networks for gait pattern classification.