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

Jones, Lianne, Holt, Catherine Avril ORCID: https://orcid.org/0000-0002-0428-8078 and Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X 2008. Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data. Computer Methods in Biomechanics and Biomedical Engineering 11 (1) , pp. 31-40. 10.1080/10255840701550956

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Business (Including Economics)
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QM Human anatomy Q Science > QP Physiology R Medicine > R Medicine (General)
Additional Information:	Special Issue on Motion Analysis and Musculoskeletal Modelling
Publisher:	Taylor & Francis
ISSN:	1025-5842
Last Modified:	17 Oct 2022 09:44
URI:	https://orca.cardiff.ac.uk/id/eprint/5435

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