Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data |
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Authors: | Jones Lianne Holt Cathy A Beynon Malcolm J |
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Affiliation: | Cardiff School of Engineering, Cardiff University, Cardiff, Wales, UK. |
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Abstract: | There are certain major obstacles to using motion analysis as an aid to clinical decision making. These include: the difficulty in comprehending large amounts of both corroborating and conflicting information; the subjectivity of data interpretation; the need for visualization; and the quantitative comparison of temporal waveform data. This paper seeks to overcome these obstacles by applying a hybrid approach to the analysis of motion analysis data using principal component analysis (PCA), the Dempster-Shafer (DS) theory of evidence and simplex plots. Specifically, the approach is used to characterise the differences between osteoarthritic (OA) and normal (NL) knee function data and to produce a hierarchy of those variables that are most discriminatory in the classification process. Comparisons of the results obtained with the hybrid approach are made with results from artificial neural network analyses. |
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