Department of Software Technology
Vienna University of Technology
Clinical Gait Analysis by Neural Networks: Issues and Experiences
Clinical gait analysis is an area aiming at the provision of support for
diagnoses and therapy considerations, the development of bio-feedback systems
to train patients, and the recognition of effects of multiple diseases and
still active compensation.
The data recorded with ground reaction force measurement platforms is a
convenient starting point for gait analysis.
We argue in favor of using the raw data from such force platforms and apply
artificial neural networks for gait malfunction identification.
In this paper we discuss our latest results in this line of research
by using a supervised learning rule.
The employed classification approach is learning vector quantization
which proved to be highly robust in the training process yielding a
remarkably high recognition accuracy of gait patterns.
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Comments: rauber@ifs.tuwien.ac.at