SVM models for diagnosing balance problems using statistical features of the MTC signals

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Lai, Daniel ORCID: 0000-0003-3459-7709, Begg, Rezaul ORCID: 0000-0002-3195-8591 and Palaniswami, M (2008) SVM models for diagnosing balance problems using statistical features of the MTC signals. International Journal of Computational Intelligence and Applications, 7 (3). pp. 317-331. ISSN 1469-0268

Abstract

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ2 = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balanceimpaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/3776
DOI https://doi.org/10.1142/S1469026808002314
Subjects Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Historical > FOR Classification > 0903 Biomedical Engineering
Historical > SEO Classification > 9204 Public Health (excl. Specific Population Health)
Keywords ResPubID16086, trip falls, support vector machines, receiver operating characteristics (ROC), minimum toe clearance (MTC) signal, statistical features
Citations in Scopus 10 - View on Scopus
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