期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 55, 期 3, 页码 1128-1135出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2007.909536
关键词
classification; electromyography; online; prosthetics; support vector machines
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.
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