Feature Extraction using Poincaré Plots for Gait Classification

The aim of this study is to evaluate different features, extracted from a Poincaré plot of gait signals, in their ability to classify the gait of patients with neurodegenerative diseases: Parkinson’s disease (PD) and Huntington’s disease (HD). Five different features that describe gait variability were extracted from the Poincaré plots of two gait signals: stride time and percentage of stride time spent in swing phase. Among the set of extracted features, those that displayed significant differences between the two groups and were not correlated with each other, were used as input to the support vector machine classifier. It was found that all extracted features (with exception of one feature in PD vs healthy group comparison) are significantly different between healthy and pathological subjects and are suitable to discriminate them (with accuracies greater than 80%). When comparing PD vs HD, just three features were significantly different, however, a relatively good classification accuracy (around 72%) was achieved using two of them. The results demonstrate that it is feasible to apply variability measures extracted from Poincaré plots of gait data signals in gait classification problems.