Invariant surface EMG feature against varying contraction level for myoelectric control based on muscle coordination

Jiayuan He, Dingguo Zhang, Xinjun Sheng, Shunchong Li, Xiangyang Zhu

Research output: Contribution to journalArticlepeer-review

105 Citations (SciVal)


Variations in muscle contraction effort have a substantial impact on performance of pattern recognition based myoelectric control. Though incorporating changes into training phase could decrease the effect, the training time would be increased and the clinical viability would be limited. The modulation of force relies on the coordination of multiple muscles, which provides a possibility to classify motions with different forces without adding extra training samples. This study explores the property of muscle coordination in the frequency domain and found that the orientation of muscle activation pattern vector of the frequency band is similar for the same motion with different force levels. Two novel features based on discrete Fourier transform and muscle coordination were proposed subsequently, and the classification accuracy was increased by around 11% compared to the traditional time domain feature sets when classifying nine classes of motions with three different force levels. Further analysis found that both features decreased the difference among different forces of the same motionp < 0.005) and maintained the distance among different motionsp > 0.1). This study also provided a potential way for simultaneous classification of hand motions and forces without training at all force levels.

Original languageEnglish
Article number6846283
Pages (from-to)874-882
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
Publication statusPublished - 1 May 2015


  • Discrete Fourier transform
  • Electromyography
  • Force variation
  • Muscle coordination
  • Pattern recognition
  • Prosthetic hands

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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