A Method for Online Estimation of Human Arm Dynamics

Abstract

Human arm dynamics can be used for human body performance analysis or for control of human-machine interfaces. In this paper, a novel method for online estimation of human forearm dynamics using a second-order quasi-linear model is presented. The proposed method uses Moving Window Least Squares to locally identify dynamic parameters for a limited number of operating points in a variable space defined by elbow joint angle and velocity, and the electromyogram signals collected from upper-arm muscles. The dynamic parameters for these limited points are then employed to train a Radial Basis Function Artificial Neural Network to interpolate/extrapolate for online estimates of arm dynamic parameters for other operating points in the variable space. The proposed estimation method is evaluated on a single degree-of-freedom robotic arm.