TY - GEN
T1 - Teleoperation of a robot arm in 2D catching movements using EMG signals and a bio-inspired motion law
AU - Artemiadis, Panagiotis K.
AU - Kyriakopoulos, Kostas J.
PY - 2006/12/22
Y1 - 2006/12/22
N2 - This paper presents a methodology of robot arm teleoperation, using electromyographic (EMG) signals and a bio-inspired motion law. The methodology is implemented in planar catching movements, in situations that the user reaches and grasps objects lying on a table in front of him. EMG signals from the flexor and extensor muscles of both the elbow and the wrist joint are used to predict the elbow and wrist joint angle. This is done by using two auto-regressive moving average with exogenous output (ARMAX) models, one for each joint. A position tracker is attached in the user's upper arm, before the elbow joint, and is used for the application of the bio-inspired motion law. This law states that the trajectory of the human hand during planar reaching tasks lays on a straight line. Thus, by applying this motion law at the predicted hand trajectory, the errors of the joint angle estimation through the ARMAX model are reduced. The grasping intention of the user, after reaching the target is decoded through a discrimination algorithm based on feature extraction of the EMG signals from the forearm. The experimental results show that the two ARMAX model estimations for the joint angles, in conjunction with the motion law, are able to predict the user's motion with high accuracy, within different target points and various movement velocities.
AB - This paper presents a methodology of robot arm teleoperation, using electromyographic (EMG) signals and a bio-inspired motion law. The methodology is implemented in planar catching movements, in situations that the user reaches and grasps objects lying on a table in front of him. EMG signals from the flexor and extensor muscles of both the elbow and the wrist joint are used to predict the elbow and wrist joint angle. This is done by using two auto-regressive moving average with exogenous output (ARMAX) models, one for each joint. A position tracker is attached in the user's upper arm, before the elbow joint, and is used for the application of the bio-inspired motion law. This law states that the trajectory of the human hand during planar reaching tasks lays on a straight line. Thus, by applying this motion law at the predicted hand trajectory, the errors of the joint angle estimation through the ARMAX model are reduced. The grasping intention of the user, after reaching the target is decoded through a discrimination algorithm based on feature extraction of the EMG signals from the forearm. The experimental results show that the two ARMAX model estimations for the joint angles, in conjunction with the motion law, are able to predict the user's motion with high accuracy, within different target points and various movement velocities.
KW - Auto-regressive model
KW - Electromyographic (EMG) signal
KW - Robot teleoperation
UR - http://www.scopus.com/inward/record.url?scp=33845586003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845586003&partnerID=8YFLogxK
U2 - 10.1109/BIOROB.2006.1639057
DO - 10.1109/BIOROB.2006.1639057
M3 - Conference contribution
AN - SCOPUS:33845586003
SN - 1424400406
SN - 9781424400409
T3 - Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
SP - 41
EP - 46
BT - Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
T2 - 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
Y2 - 20 February 2006 through 22 February 2006
ER -