An improved ensemble empirical mode decomposition and Hilbert transform for fatigue evaluation of dynamic EMG signal

Q. Wu, C. F. Wei, Z. X. Cai, L. Ding, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


A hybrid dynamic fatigue diagnosis method based on a variation of ensemble empirical mode decomposition (VEEMD) and mean instantaneous frequency (MIF) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. In the method here proposed, a particular noise is added at each stage of the decomposition and a unique residue is computed to obtain each mode. Our results showed that MIF estimated from each instantaneous frequency of intrinsic mode functions (IMFs) decomposed by the proposed VEEMD is a relevant feature to muscular fatigue diagnosis. We found that MIF reduces when the force level of the muscle contraction increases.
Original languageEnglish
Pages (from-to)5903-5908
Number of pages6
Issue number24
Publication statusPublished - 1 Jan 2015


  • Ensemble empirical mode decompositions
  • Hilbert spectrum
  • Intrinsic mode function (IMF)
  • Mean instantaneous frequency
  • Muscular fatigue

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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