Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/101581
Title: A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction
Authors: Simão, Miguel 
Mendes, Nuno 
Gibaru, Olivier
Neto, Pedro 
Keywords: EMG; human-machine interaction; pattern classification; regression
Issue Date: 2019
Project: SFRH/BD/105252/2014 
POCI-01-0145-FEDER-016418 
COBOTIS under Grant PTDC/EMEEME/32595/2017 
Serial title, monograph or event: IEEE Access
Volume: 7
Abstract: This paper presents a literature review on pattern recognition of electromyography (EMG) signals and its applications. The EMG technology is introduced and the most relevant aspects for the design of an EMG-based system are highlighted, including signal acquisition and filtering. EMG-based systems have been used with relative success to control upper- and lower-limb prostheses, electronic devices and machines, and for monitoring human behavior. Nevertheless, the existing systems are still inadequate and are often abandoned by their users, prompting for further research. Besides controlling prostheses, EMG technology is also beneficial for the development of machine learning-based devices that can capture the intention of able-bodied users by detecting their gestures, opening the way for new human-machine interaction (HMI) modalities. This paper also reviews the current feature extraction techniques, including signal processing and data dimensionality reduction. Novel classification methods and approaches for detecting non-trained gestures are discussed. Finally, current applications are reviewed, through the comparison of different EMG systems and discussion of their advantages and drawbacks
URI: https://hdl.handle.net/10316/101581
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2906584
Rights: openAccess
Appears in Collections:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais

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