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Automated Nonlinear Feature Generation and Classification of Foot Pressure Lesions

Mu, T, Pataky, T, Findlow, AH, Aung, M.S.H. and Goulermas, J.Y. 2010, 'Automated Nonlinear Feature Generation and Classification of Foot Pressure Lesions' , IEEE Transactions on Information Technology in Biomedicine, 14 (2) , pp. 418-424.

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    Abstract

    Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields. This paper focuses on foot kinematics and applies kernel principal component analysis(KPCA) for nonlinear dimensionality reduction of features, followed by Fisher’s linear discriminant analysis for the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. Performance comparisons are made using leave-one-out cross-validation. Results show that the proposed method can lead to∼ 94% correct classification rates, with a reduction of feature imensionality from 2100 to 46, without any manual preprocessing or elaborate feature extraction methods. The results imply that foot kinematics contain information that is highly relevant to pathology classification and also that the nonlinear KPCA approach has considerable power in unraveling abstract biomechanical features into a relatively lowdimensional pathology-relevant space.

    Item Type: Article
    Themes: Health and Wellbeing
    Schools: Colleges and Schools > College of Health & Social Care > School of Health Sciences > Centre for Health Sciences Research
    Colleges and Schools > College of Health & Social Care
    Colleges and Schools > College of Health & Social Care > School of Health Sciences
    Journal or Publication Title: IEEE Transactions on Information Technology in Biomedicine
    Publisher: Institute of Electrical and Electronics Engineers
    Refereed: Yes
    ISSN: 1089-7771
    Depositing User: RH Shuttleworth
    Date Deposited: 11 May 2011 10:07
    Last Modified: 20 Aug 2013 17:51
    URI: http://usir.salford.ac.uk/id/eprint/15735

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