Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data.

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.

An application of the Dempster-Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery.

This paper utilises a novel method for the classification of subjects with osteoarthritic and normal knee function. The classification method comprises a number of different components. Firstly, the method exploits the Dempster-Shafer theory of evidence allowing for a degree of ignorance in the subject's classification, i.