Experimental Brain Research

, Volume 237, Issue 2, pp 291–311 | Cite as

Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

  • Dapeng YangEmail author
  • Yikun Gu
  • Nitish V. Thakor
  • Hong Liu


The development of advanced and effective human–machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.


Myoelectric signal Motion control Hand prosthesis Pattern recognition 



Central nervous system










Intramuscular EMG


Surface EMG


High-density EMG


Motor unit


Degree of freedom


Myoelectric signal control


Pattern recognition


PR-based MSC


Regression-based MSC


Encoding-based MSC


Synergy-based MSC


Principal components analysis


Targeted muscle reinnervation


Classification accuracy


True positives rate


False positives rate


Graphic user interfaces


Southampton hand assessment procedure


Time/frequency domain


Continuous wavelet transform


Transcutaneous electric nerve stimulus


Human–machine interface


Support vector machine


Support vector domain description


Domain adaptation


Linear discriminant analysis


Common model component analysis


Radio frequency identification


Inertial measurement unit



This work is partially supported by the National Natural Science Foundation of China (NO. 51675123, NO.51521003) and China Scholarship Council.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dapeng Yang
    • 1
    • 2
    Email author
  • Yikun Gu
    • 1
  • Nitish V. Thakor
    • 3
  • Hong Liu
    • 1
  1. 1.State Key Laboratory of Robotics and SystemHarbin Institute of TechnologyHarbinChina
  2. 2.Artificial Intelligence LaboratoryHarbin Institute of TechnologyHarbinChina
  3. 3.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA

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