Upper-limb EMG-based robot motion governing using empirical mode decomposition and adaptive neural fuzzy inference system

Hsiu Jen Liu, Kuu-Young Young*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

To improve the quality of life for the disabled and elderly, this paper develops an upperlimb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMGsignals into a set of intrinsicmode functions, each of which represents different physical characteristics ofmuscularmovement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.

Original languageEnglish
Pages (from-to)275-291
Number of pages17
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume68
Issue number3-4
DOIs
StatePublished - 1 Dec 2012

Keywords

  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Electromyography (EMG)
  • Empirical mode decomposition (EMD)
  • Human-assisting robot
  • Upper-limb motion classification

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