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

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..