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A machine learning approach to assessing gait patterns for Complex Regional Pain Syndrome

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Yang, Mingjing, Zheng, Huiru, Wang, Haiying, McClean, Sally, Hall, Jane and Harris, Nigel (2012) A machine learning approach to assessing gait patterns for Complex Regional Pain Syndrome. Medical Engineering & Physics, 34 (6). pp. 740-746. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S135045331100244X

DOI: 10.1016/j.medengphy.2011.09.018

Abstract

Complex Regional Pain Syndrome (CRPS) is a condition that causes a long-term burning pain in a limb or part of a limb and it can cause various degrees of the physical functional performance deterioration. Objective assessment of physical functional performance of patients is one critical component to evaluate the therapy outcome for CRPS. This paper aims to investigate the feasibility of assessing the physical performance of patients with Complex Regional Pain Syndrome based on the analysis of gait data recorded by an accelerometer in short walking distances. Ten subjects with CRPS and ten control subjects were recruited. Thirty three features were extracted from each recording. A machine learning method, Multilayer perceptron neural-networks (MLP), was applied to classify the normal and abnormal gait patterns from data obtained on a 2.4 m performance evaluation test. The best classification accuracy (99.38%) was achieved using 3 features selected by a step-wise-forward method. To further validate its performance, an independent test set including 14 cases extracted from data obtained on a 20 m performance evaluation test was adopted. A prediction accuracy of 85.7% was obtained.

Item Type:Journal article
Keywords:Accelerometer; Gait analysis; Complex Regional Pain Syndrome; Feature extraction; Classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Smart Environments
ID Code:21030
Deposited By:Dr Huiru Zheng
Deposited On:15 Feb 2012 11:21
Last Modified:09 Aug 2012 09:45

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