Gait signal classification tool utilizing Hilbert transform based feature extraction and logistic regression based classification

In this paper, we have employed a machine learning approach for automatic classification of healthy and pathological gait signals and subsequent identification of the neurological disorder in the pathological gait signals. The machine learning algorithm we have proposed is the Logit model of the Logical Regression Classifier. As the process of walking is automatically controlled by the nervous system it is important to develop a non-invasive method so that patients with serious neurological disorders like Huntington's disease and Parkinson's disease receive early medical attention and they get proper care before they are more affected. Swing, Stance and double support intervals (expressed as percentages of stride) of 63 subjects were analyzed. In this paper, a relevant gait signal feature extractor is developed which is combined with Logistic Regression Classifier to classify healthy subjects and pathological subjects. Analysis of real-time gait signals is simplified using the Hilbert Transform which converts the real signals into an analytic signal. The proposed algorithm was developed using the MATLAB platform and the average accuracy of multiclass classification is found to be 86.05% while the accuracy of detecting healthy subjects from pathological subjects is 87.79% and the accuracy of classifying subjects having the Huntington's disease and Parkinson's disease is found to be 85.22%.