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. 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.
Jones
Lianne
L
Cardiff School of Engineering, Cardiff University, Cardiff, Wales, UK.
Holt
Cathy A
CA
Beynon
Malcolm J
MJ
D001696:Biomechanical Phenomena / Q000706:statistics & numerical data*; D001698:Biomedical Engineering; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005684:Gait / Q000502:physiology; D006801:Humans; D007719:Knee Joint / Q000502:physiology / Q000503:physiopathology*; D008954:Models, Biological; D009038:Motion; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D020370:Osteoarthritis, Knee / Q000175:diagnosis / Q000503:physiopathology*; D025341:Principal Component Analysis