Gait Event Detection with Intelligent Shoes

For more precise control of the FES-derived gait, the timing of specific gait phases is needed. With the information of when the legs are in each phase of gait, the quality of the gait during each phase can be assessed. The goal of this paper is to investigate potential use of machine learning techniques, in particular Support Vector Machine (SVM), and Force Sensitive Resistor (FSR) to detect discrete stages in the cyclic motion of dynamic human gait. We present intelligent shoes for capturing and analyzing dynamic human gait data. Principal Component Analysis(PCA) will be applied for feature generation and data reduction, and Support Vector Machine (SVM) will be applied for training and classifier generation. The classification performance is assessed quantitatively, by measuring the percentage of time steps in which the correct event is found and qualitatively, by observing the types of errors encountered. The experimental results verify that the proposed method is valid and useful with overall accuracy to 91%.