Gender recognition using optimal gait feature based on recursive feature elimination in normal walking

Abstract

This study aims to propose a novel approach for gender recognition using best feature subset based on recursive feature elimination (RFE) in normal walking. This study has focused on the analysis of gait characteristics by distinguishing the gait phases as initial contact (IC), Mid-stance (MS), Pre-swing, and swing (SW), and collected the large number of gait to improve the reliability of quantitative assessment of natural variability associated with muscle activity during free walking. The gait system was designed using pressure and a tri-axis accelerometer sensor, and a 9-channel electromyography sensor for measuring the data. Gender recognition method was proposed using support vector machine (SVM) and random forest (RF) based on RFE to determine best feature subset. Statistical results show that effects of gender-based differences on gait characteristic including temporal, kinematics, and muscle activity were investigated. The temporal parameters of stride time and gait cycle (%) in the gait phases of IC, MS, and SW were significantly different between females and males (p<0.01). The females exhibited both a lower angle and a root mean square acceleration of the knee joint as compared to the males, and there was a clear gender-based difference with respect to knee angle movement. In addition, most muscle activation measurements in the females were larger than those of the males with respect to the gait phases. Gender classification result shows that SVM-RFE was 99.11% (SVM classifier) and RF-RFE was 98.89% (SVM and RF classifier), having powerful performance.