Monika Köhle

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A new approach for the assessment of gait patterns is presented. The use of neural network techniques for decision making in gait analysis is for some purposes more effective than biomechanical methods or conventional statistics. To demonstrate this, a neural network was trained to distinguish 'healthy' from 'pathological' gait. The algorithm presented here(More)
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 eeects of multiple diseases and still active compensation. The data recorded with ground reaction force measurement platforms is a convenient starting point for gait(More)
The recognition of similarities in high dimensional input spaces and their visualization in low dimensional output spaces is a highly demanding application area for unsupervised artificial neural networks. Some of the problems inherent to structuring high dimensional input data may be shown with an application such as text document classification. One of(More)
A novel approach to gait pattern classification is presented. Based on data collected from patients at an rehabilitation centre we achieve a clustering according to various gait malfunctions by using self-organizing maps. The major benefit of such an approach is that the learning process ends up with a classification of gait malfunctions without the need of(More)
This paper provides an analysis of multi-class e-mail categorization performance. In order to investigate this issue, the quality of various classification algorithms based on two distinct document representation formalisms is compared. In particular, both a standard word-based document representation as well as a character n-gram document representation is(More)