Wavelet-Based Feature Extraction for Support Vector Machines for Screening Balance Impairments in the Elderly

Trip related falls are a prevalent problem in the elderly. Early identification of at-risk gait can help prevent falls and injuries. The main aim of this study was to investigate the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] in comparison to MFC histogram plot analysis in extracting features for developing a model using support vector machines (SVMs) for screening of balance impairments in the elderly. MFC during walking on a treadmill was recorded on 13 healthy elderly and 10 elderly with a history of tripping falls. Features extracted from MFC histogram and then multiscale exponents between successive wavelet coefficient levels after wavelet decomposition of MFC series were used as inputs to the SVM to classify two gait patterns. The maximum accuracy of classification was found to be 100% for a SVM using a subset of selected wavelet based features, compared to 86.95% accuracy using statistical features. For estimating the relative risk of falls, the posterior probabilities of SVM outputs were calculated. These results suggest superior performance of SVM in the detection of balance impairments based on wavelet-based features and it could also be useful for evaluating for falls prevention intervention.