The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal

The Usefulness of Mean and Median Frequencies in Electromyography Analysis

“…Normally, EMG signals are acquired by surface electrodes that are placed on the skin superimposed on the targeted muscle. In order to use the EMG signal as a diagnosis signal or a control signal, a feature is often extracted before performing analysis or classification stage (Phinyomark et al, 2012a) because a lot of information, both useful information and noise (Phinyomark et al, 2012b), is contained in the raw EMG data. An EMG feature is a distinct characteristic of the signal that can be described or observed quantitatively, such as being large or small, spiky or smooth, and fast or slow.…”

“…In order to analyse the EMG signals during dynamic movements, the effects of muscle force and muscle geometry (joint angle) should have paid more attention (Cechetto et al, 2001;Doheny et al, 2008). In the literature, such effects on MNF and MDF have still been inconclusive (Doheny et al, 2008;Phinyomark et al, 2012c). A summary of the conflicting results mentioned in the literature is also presented.…”

“…To confirm the effect of muscle force on MNF and MDF, the relationship between MNF (and also MDF) and muscle force level was re-evaluated by the new EMG data (Phinyomark et al, 2012c). Figs.…”

“…To answer the question "why's the subject-dependent? ", several related anthropometric variables obtained from the volunteers should be intended to find the possible reasons (Phinyomark et al, 2012c). The preliminary study showed that a number of anthropometric variables have a correlation with the conflicting results, such as standing height, hand breadth, body mass, and forward grip reach.…”

A Novel Approach to Classify the Shoulder Motion of Upper Limb Amputees

“…(Angkoon Limsakul 2012)(Hussain et al, in 2009) proposed db2, db6, db8, dmey, sym8, sym4 and sym 5 wavelet functions with decomposition level 4 and universal scaling with hard threshold function to determine the muscles contraction. For the feature selection from different hand functions, Phinyomark et al, (Angkoon Phinyomark, Phukpattaranont, and Limsakul 2012) Limsakul, C. & Phukpattaranont 2008; Angkoon Phinyomark, Phukpattaranont, and Limsakul 2012) summarized that the best performance can be obtained from db2 with level 2 and db7 with the decomposition level 4. But in (Megahed et al 2008;Reaz, M. B. I.;Hussain, M. S. & Mohd-Yasin 2006) the researchers recommended for the wavelet functions as db2, db7, sym2, sym5, coif 4, bior5.5 and bior 2.2 with the decomposition level 4 for de-noising.…”