Online electromyographic control of a robotic prosthesis.
This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks.
Shenoy
Pradeep
P
Department of Computer Science and Engineering, University of Washington, P.O. Box 352350, Seattle, WA 98195, USA. pshenoy@cs.washington.edu
Miller
Kai J
KJ
Crawford
Beau
B
Rao
Rajesh N
RN
D000465:Algorithms; D001186:Artificial Limbs; D004576:Electromyography / Q000379:methods*; D005246:Feedback; D006801:Humans; D007595:Joint Prosthesis; D008328:Man-Machine Systems; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D013813:Therapy, Computer-Assisted / Q000379:methods*; D014584:User-Computer Interface