Beynon, Malcolm James ![]() ![]() |
Abstract
In this paper, a novel object classification method is introduced and developed within a biomechanical study of human knee function in which subjects are classified to one of two groups: subjects with osteoarthritic (OA) and normal (NL) knee function. Knee-function characteristics are collected using a three-dimensional motion-analysis technique. The classification method transforms these characteristics into sets of three belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function, and an associated level of uncertainty. The evidence from each characteristic is then combined into a final set of belief values, which is used to classify subjects. The final belief values are subsequently represented on a simplex plot, which enables the classification of a subject to be represented visually. The control parameters, which are intrinsic to the classification method, can be chosen by an expert or by an optimization approach. Using a leave-one-out cross-validation approach, the classification accuracy of the proposed method is shown to compare favorably with that of a well-established classifier-linear discriminant analysis. Overall, this study introduces a visual tool that can be used to support orthopaedic surgeons when making clinical decisions.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Business (Including Economics) Engineering |
Uncontrolled Keywords: | bone ; gait analysis ; medical computing ; statistical analysis ; uncertainty handling ; Classification ; Dempster–Shafer theory of evidence (DST) ; motion analysis ; osteoarthritic (OA) knee function |
ISSN: | 1083-4427 |
Last Modified: | 17 Oct 2022 09:07 |
URI: | https://orca.cardiff.ac.uk/id/eprint/2119 |
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