EMG-based teleoperation of a robot arm in planar catching movements using ARMAX model and trajectory monitoring techniques

This paper presents a methodology of teleoperating a robot arm, using electromyographic (EMG) signals and a trajectory monitoring technique based on human motion analysis. EMG signals from the flexor and extensor muscles of the elbow joint are used to predict the human elbow joint angle, using an auto-regressive moving average with exogenous output (ARMAX) model. A position tracker is attached in the user upper arm, before the elbow joint. It has been identified from previous works on human physiology that the trajectory of the human hand during planar catching tasks lays on a straight line. This motion law is used in order to monitor and refine the trajectory of the human hand that is predicted through EMG and the ARMAX model. The experimental results show that the ARMAX model estimation for the elbow angle, in conjunction with the trajectory monitoring technique, is able to predict the user motion with high accuracy, within different target points unknown to the system, and various hand velocities

[1]  A. Papoulis.  MAT 501 PROBABILITY, RANDOM VARIABLES AND STOCHASTIC PROCESSES (4-0-0-4) , 2002 .

[2]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[3]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[4]  G. Gottlieb,et al.  Directional control of planar human arm movement. , 1997, Journal of neurophysiology.

[5]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[6]  Panagiotis K. Artemiadis,et al.  Teleoperation of a robot manipulator using EMG signals and a position tracker , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Matthew Heath,et al.  The control of goal-directed limb movements: Correcting errors in the trajectory , 1999 .

[8]  R.J. Triolo,et al.  The theoretical development of a multichannel time-series myoprocessor for simultaneous limb function detection and muscle force estimation , 1989, IEEE Transactions on Biomedical Engineering.

[9]  Hong Liu,et al.  Levenberg-Marquardt Based Neural Network Control for a Five-fingered Prosthetic Hand , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Michael I. Jordan,et al.  Are Reaching Movements Planned to be Straight and Invariant in the Extrinsic Space? Kinematic Comparison between Compliant and Unconstrained Motions , 1999 .

[11]  Blake Hannaford,et al.  Hill-Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm - Parameters Optimization , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.