RESUMO
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.
Assuntos
Fenômenos Biomecânicos/estatística & dados numéricos , Articulação do Joelho/fisiopatologia , Osteoartrite do Joelho/fisiopatologia , Engenharia Biomédica , Simulação por Computador , Interpretação Estatística de Dados , Marcha/fisiologia , Humanos , Articulação do Joelho/fisiologia , Modelos Biológicos , Movimento (Física) , Movimento/fisiologia , Redes Neurais de Computação , Osteoartrite do Joelho/diagnóstico , Análise de Componente PrincipalRESUMO
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.e., a level of uncertainty as to whether a gait variable indicates osteoarthritis or not. Secondly, the inclusion of simplex plots allows both the classification of a subject, and the contribution of each associated gait variable to that classification, to be represented visually. As a result, the method is further able to highlight periodic changes in a subject's knee function due to total knee replacement surgery and subsequent recovery. The visual representation enables a simple clinical interpretation of the results from the quantitative analysis.
