Conference Proceedings

A wavelet-based approach for screening falls risk in the elderly using support vector machines

Ahsan H Khandoker, Daniel Lai, Rezaul K Begg, Marimuthu Palaniswami

FOURTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSING AND INFORMATION PROCESSSING, PROCEEDINGS | IEEE | Published : 2006

Abstract

Trip related falls are a prevalent and costly threat to the elderly. Early identification of at-risk gait helps prevent falls and injuries. The main aim of this study is to explore the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] in extracting features for developing a model using Support Vector Machines (SVM) for automated detection of balance impairment and estimation of the falls risk in the elderly. MFC during continuous walking on a treadmill was recorded on 11 healthy elderly and 10 elderly with balance problems (falls risk) and with a history of tripping falls. The multiscale exponents (β) between successive wavelet (Wv) coeffi..

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Grants

Funding Acknowledgements

MFC gait data for this study were taken from VU Biomechanics Unit. Several people have contributed to the creation of the gait database, especially Simon Taylor of the VU Biomechanics Unit. This work is supported by an ARC Linkage grant.