Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43950
Title: An improved ensemble empirical mode decomposition and Hilbert transform for fatigue evaluation of dynamic EMG signal
Authors: Wu, Q
Wei, CF
Cai, ZX
Ding, L
Law, R 
Keywords: Ensemble empirical mode decompositions
Hilbert spectrum
Intrinsic mode function (IMF)
Mean instantaneous frequency
Muscular fatigue
Issue Date: 2015
Publisher: Urban & Fischer
Source: Optik, 2015, v. 126, no. 24, p. 5903-5908 How to cite?
Journal: Optik 
Abstract: 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.
URI: http://hdl.handle.net/10397/43950
ISSN: 0030-4026
EISSN: 1618-1336
DOI: 10.1016/j.ijleo.2015.08.179
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