title: 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.
authors.affiliation: Dept. of Electr. & Comput. Eng., Queen's Univ., Kingston, ON, Canada.
authors.forename: Farid
authors.initials: F
authors.lastname: Mobasser
authors.forename: Keyvan
authors.initials: K
authors.lastname: Hashtrudi-Zaad
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D009862:Online Systems; D013647:Task Performance and Analysis