Gesture Recognition Based on Human Grasping Activities Using PCA-BMU

Abstract

— This research study presents the recognition of fingers grasp for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between object at the same time while developing the touching contact. Human grasping can be detected by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of thumb, index and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors which are placed at back of a glove. Based on the human grasp taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method which is capable to improve amplitude transmission characteristic with the minimal combination of time and amplitude response spreads and no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis – Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.