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Title:The extraction of neural information from the surface EMG for the control of upper-limb prostheses : emerging avenues and challenges
Authors:ID Farina, Dario (Author)
ID Jiang, Ning (Author)
ID Rehbaum, Hubertus (Author)
ID Holobar, Aleš (Author)
ID Graimann, Bernhard (Author)
ID Dietl, Hans (Author)
ID Aszmann, Oskar (Author)
Files:This document has no files. This document may have a physical copy in the library of the organization, check the status via COBISS. Link is opened in a new window
Language:English
Work type:Unknown ()
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.
Keywords:neural drive to muscle, high-density EMG, motor neuron, motor unit, myoelectronic control, pattern recognition, regression
Year of publishing:2014
Number of pages:str. 797-809
Numbering:#Vol. #22, #no. #4
PID:20.500.12556/DKUM-48144 New window
UDC:007.5:61
ISSN on article:1534-4320
COBISS.SI-ID:18018070 New window
DOI:10.1109/TNSRE.2014.2305111 New window
NUK URN:URN:SI:UM:DK:NVDQBX2R
Publication date in DKUM:25.05.2015
Views:1028
Downloads:0
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Categories:Misc.
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Record is a part of a journal

Title:IEEE transactions on neural systems and rehabilitation engineering
Shortened title:IEEE trans. neural syst. rehabil. eng.
Publisher:IEEE
ISSN:1534-4320
COBISS.SI-ID:320873 New window

Document is financed by a project

Funder:EC - European Commission
Funding programme:FP7
Project number:251555
Name:Advanced Myoelectric Control of Prosthetic Systems
Acronym:AMYO

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