Estimating Falls Risk in the Elderly: A Wavelet Based Multiscale Analysis

Falls are a prevalent and costly threat to the elderly. Early identification of at-risk gait helps prevent falls and injuries. The goal of this study was to assess whether a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] could detect balance impairment and estimate falls risk. The multiscale exponents (beta) between successive wavelet (Wv) coefficient levels after Wv decomposition of MFC series (512 points) into eight levels from level 2 (Wv2) to level 256 (Wv256), were calculated for a sample of healthy elderly adults (n = 11) and a sample with falls history (n=10). Using receiver operating characteristic (ROC) analysis, the most powerful predictor variable was found to be beta_Wvl6-Wv8 (ROC_area=1.0), followed by beta_Wv8-Wv4 (ROC_area=0.92). For relative risk estimation, posterior probabilities of quadratic discriminants (QD) were calculated using the features beta_Wvl6-Wv8 and beta_Wv8-Wv4 In conclusion, wavelet based multiscale analysis appears as powerful tools for falls risk diagnosis; for example, it may be used to indicate the need for referral for falls prevention intervention (e.g., exercise program to improve balance)