New parameter estimation method of linear adaptive filter for modeling kinematic motion primitive of effective DOF of hand

Abstract

In this paper, We have extracted and modeled the motion primitive of human hand in a simple 1-DOF rhythmic motion task, i.e. manipulating mass-spring-damper system. The experiment was carried out by using 1-DOF haptic box with virtual reality in Simulink environment. The interaction dynamics of haptic box and human which consists of hand and brain reveals the role of the human as an intelligent admittance. We tested 6 people who tried to combine motion primitives to produce smoother motion during learning process. In addition, we developed a novel identification method for modeling the rhythmic motion of hand in model space. It is shown that adaptive filter as a predictor of motion primitives with two parameters and two initial values appears as an ellipse in model space. The geometrical properties of ellipse are related to the parameters and initial values of adaptive filter that make it possible to identify the parameters of adaptive filter in model space.