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 m e~~u r e m e n t platforms is a converrient 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 OUT 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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In developing countries, blindness is a major health problem whose control depends on the application of simple measures by frontline workers because, in many of these countries, specialist medical care is not readily available. To assist primary health workers in the management of common and potentially blinding eye disorders, we have developed a prototype computer program for a hand-held computer that incorporates a set of guidelines for diagnosis and treatment. This eye treatment program will help evaluate the potential of such devices for improving health care delivery in developing countries. This approach is now feasible because of recent advances in expert system and portable computer technology.