Abstract
The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.
Highlights
Muscle fatigue is defined as a temporary decrease in the physical force during exercise (Liu et al, 2014; Kyranou et al, 2018)
The results show that, compared with other methods, the optimized CEEMD (OCEEMD) suppresses the mode mixing with high efficiency for the decomposition of surface electromyography (sEMG) signals, and the extracted feature shows a high sensitivity to the sEMG changes with muscle fatigue
While the number of intrinsic mode functions (IMFs) is smaller for the complementary ensemble empirical mode decomposition (CEEMD) than for the CEEMDAN, the de-noising effect is better for the CEEMDAN than for the CEEMD
Summary
Muscle fatigue is defined as a temporary decrease in the physical force during exercise (Liu et al, 2014; Kyranou et al, 2018). While the mechanism of muscle fatigue is complicated, accurate detection of fatigue is of great significance for assessing functional impairment, planning training programs, and evaluating rehabilitation effect (Gandevia, 2013). For these reasons, the detection of muscle fatigue has been a hot topic in the field of rehabilitation and sports medicine over the last couple of decades. A surface electromyography (sEMG) signal captures the state of muscle activity and motor function and is considered as an effective tool to evaluate local muscle fatigue (Chowdhury et al, 2013). It is a reliable approach to processing sEMG signals by a non-linear method and to extract muscle fatigue features from the complexity
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
