TY - JOUR
T1 - The role of muscle synergies in myoelectric control
T2 - Trends and challenges for simultaneous multifunction control
AU - Ison, Mark
AU - Artemiadis, Panagiotis
N1 - Publisher Copyright:
© 2014 IOP Publishing Ltd.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.
AB - Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.
KW - EMG
KW - brain-machine interfaces
KW - muscle synergies
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U2 - 10.1088/1741-2560/11/5/051001
DO - 10.1088/1741-2560/11/5/051001
M3 - Review article
C2 - 25188509
AN - SCOPUS:84907450108
SN - 1741-2560
VL - 11
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 5
M1 - 051001
ER -