Automatic stride interval extraction from long, highly variable and noisy gait timing signals.

This paper presents a probabilistic algorithm for automatically extracting the stride interval time series from long, highly variable and noisy two-state timing signals. Long interstride temporal records are of particular interest in nonlinear dynamical analysis of gait. The proposed method consists of probabilistic estimation and extraction followed by post-extraction filtering. With noisy timing signals from 10 children with Spastic Diplegia, no statistical differences in the numbers of extracted strides (p=0.94), the mean stride intervals (p=0.55) and the scaling exponents (p=0.94) (a measure of temporal heterogeneity) were found between series extracted by hand and by the probabilistic algorithm. The method is robust to noise and violations of normality. Results support the use of probabilistic extraction as an alternative to laborious manual extraction.