Wavelet analysis of surface electromyography

Abstract

Muscle fatigue is often a result of unhealthy work practice. It has been known for some time that there is a significant change in the spectrum of the electromyography (EMG) of the muscle when it is fatigued. Due to the very complex nature of this signal however, it has been difficult to use this information to reliably automate the process of fatigue onset determination. If such a process implementation were feasible, it could be used as an indicator to reduce the chances of work-place injury. This research report on the effectiveness of the wavelet transform applied to the EMG signal as a means of identifying muscle fatigue. We report that with the appropriate choice of wavelet functions and scaling factors, it is possible to achieve reliable discrimination of the fatigue phenomenon, appropriate to an automated fatigue identification system.

sEMG Techniques to Detect and Predict Localised Muscle Fatigue

“…Refrence ID RMS (Basmajian & De Luca, 1985;Kumar & Mital, 1996) (Cohen, 1995;Raez et al, 2006;Ricamato et al, 1992) Gabor Transform (Gabor, 1946) Wavelet analysis (Kumar et al, 2003;Laterza & Olmo, 1997) Autogression analysis (Graupe & Cline, 1975;Kim et al, 2005;Tohru, 1992) Entropy (Jaynes, 1957;Sung et al, 2008) Recurrence Quantification Analysis (Filligoi et al, 2010;Morana et al, 2009) HOS (Hussain et al, 2008Kanosue et al, 1979) Composite Features (Boostani & Moradi, 2003;Hudgins et al, 1993;Phinyomark et al, 2009) Table 1. Signal analysis and feature characteristics…”

“…There are a number of so-called 'mother wavelets' that can be used for signal decomposition, including Symm-let, Coiflet, Haar, Morlet, Daubechies and Mexican Hat (Kumar et al, 2003).…”

“…Guglielminotti & Merletti (1992) hypothesised that if the wavelet analysis is selected to fit with the shape of the MUAP, the WT would give the best energy location in a time-scale. Kumar et al (2003) stated that the STFT does not give an optimal time or frequency resolution for the non-stationary signal, although the relatively short time windows may trace spectral variations with time. The WT, comprised of numerous WFs, can be used to decompose the sEMG signal.…”

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

“…Finally, the CWD seems to be very effective in decreasing the effects of cross terms and in retaining most of the useful properties of a TF distribution; however, it does not dramatically improve the results with respect to the SPWVD in terms of better peak tracking capabilities. Wavelet and Wavelet packets (WP) have been recently introduced in the analysis of muscle fatigue [18], [14], [12], [25], [29], [11], [13], [27]. For instance it may be possible to assess muscle fatigue by determining the wavelet decomposition of the signal with the wavelets Sym4 or Sym5 and eight or nine levels of iterations in the decomposition [18].…”

“…The main drawback of the CWT is that it is computationally expensive and requires large storage requirements. Discrete wavelet transform (DWT) is proposed to overcome this issue [18], [14], [12], [25], [29]. All the analyzed papers regarding the assessment of muscle fatigue by using wavelets and WP have focused in analyzing just changes in the frequency of the SEMG signal over time, yet the amplitude of the SEMG signal has been disregarded.…”