EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings

As robots come closer to humans, an efficient human-robot-control interface is an utmost necessity. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. A mathematical model is trained to decode upper...

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Bibliographic Details
Main Authors: Artemiadis, Panagiotis (Contributor), Kyriakopoulos, Kostas J. (Author)
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers / IEEE Robotics and Automation Society, 2011-03-28T18:03:16Z.
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Online Access:Get fulltext
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100 1 0 |a Artemiadis, Panagiotis  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Mechanical Engineering  |e contributor 
100 1 0 |a Artemiadis, Panagiotis  |e contributor 
100 1 0 |a Artemiadis, Panagiotis  |e contributor 
700 1 0 |a Kyriakopoulos, Kostas J.  |e author 
245 0 0 |a EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings 
260 |b Institute of Electrical and Electronics Engineers / IEEE Robotics and Automation Society,   |c 2011-03-28T18:03:16Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/61981 
520 |a As robots come closer to humans, an efficient human-robot-control interface is an utmost necessity. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. A mathematical model is trained to decode upper limb motion from EMG recordings, using a dimensionality-reduction technique that represents muscle synergies and motion primitives. It is shown that a 2-D embedding of muscle activations can be decoded to a continuous profile of arm motion representation in the 3-D Cartesian space, embedded in a 2-D space. The system is used for the continuous control of a robot arm, using only EMG signals from the upper limb. The accuracy of the method is assessed through real-time experiments, including random arm motions. 
520 |a European Commission (NEUROBIOTICS project FP6-IST-001917) 
546 |a en_US 
655 7 |a Article 
773 |t IEEE transactions on robotics