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

Dapeng Yang, Yikun Gu, Nitish V Thakor, Hong Liu

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalExperimental Brain Research
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Hand prosthesis
  • Motion control
  • Myoelectric signal
  • Pattern recognition

ASJC Scopus subject areas

  • Neuroscience(all)

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