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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.

SofnYare inspection is a quality assurance method to detect defects early during the software development process. For inspection planning there are defect detection techniques, so-called reading techniques, which let the inspection planner focus the effectiveness of individual inspectors on specific sets of defects. For realistic planning it is important to use empirically evaluated defect detection techniques.In this paper we report on the replication of a largescale experiment in an academic environment. The experiment evaluated the efectiveness of defect detection jor inspectors who use a checklist orfocused scenarios on individual and team level.A main finding of the experiments is that the teams were effective to find defects: In both experiments the inspection teams found on average more than 70% ofthe defects in the product. The checklist consistently was overall somewhat more effective on individual level, while the scennrios traded overall defect detection effectiveness for much better effectiveness regarding their target focus, in our case specific parts ofthe documents.Another main result of the study is that scenario-based reading techniques can be used in inspection planning to focus individual performance without signijcant loss of effectiveness on team level.Keywords: S o f i a r e inspection process, defect detection techniques, quality measurement, replicated experiment, empirical sofhvare engineering.

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.

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