Comparison of human jogging and walking patterns using statistical tabular, scatter distribution and artificial classifiers

Abstract

This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. Three methods were proposed to differentiate and separate both of the jogging and walking data set, they are statistical tabular, scatter distribution and artificial classifier recognition. Linear decision boundary and radial basis function kernel (RBF) were proposed to perform the separation works for artificial classifier recognition section. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum RBF kernel for jogging and walking pattern classification. RBF used in this study was successfully differentiate and classify the jogging and walking patterns of a human arm movement during performing these activities.