Teleoperation of a robot arm in 2D catching movements using EMG signals and a bio-inspired motion law

Artemiadis, PK; Kyriakopoulos, KJ

dc.contributor.author	Artemiadis, PK	en
dc.contributor.author	Kyriakopoulos, KJ	en
dc.date.accessioned	2014-03-01T02:44:12Z
dc.date.available	2014-03-01T02:44:12Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31751
dc.subject	Auto-regressive model	en
dc.subject	Electromyographic (EMG) signal	en
dc.subject	Robot teleoperation	en
dc.subject.other	Electromyography	en
dc.subject.other	Joints (anatomy)	en
dc.subject.other	Motion estimation	en
dc.subject.other	Physiological models	en
dc.subject.other	Signal encoding	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Auto-regressive moving average with exogenous output (ARMAX) models	en
dc.subject.other	Electromyographic (EMG) signals	en
dc.subject.other	Joint angle estimation	en
dc.subject.other	Robot teleoperations	en
dc.subject.other	Robotic arms	en
dc.title	Teleoperation of a robot arm in 2D catching movements using EMG signals and a bio-inspired motion law	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIOROB.2006.1639057	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIOROB.2006.1639057	en
heal.identifier.secondary	1639057	en
heal.publicationDate	2006	en
heal.abstract	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.	en
heal.journalName	Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006	en
dc.identifier.doi	10.1109/BIOROB.2006.1639057	en
dc.identifier.volume	2006	en
dc.identifier.spage	41	en
dc.identifier.epage	46	en