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Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.

Recently, wavelet analysis has proved to be one of the most powerful signal processing tools for the analysis of surface electromyography (sEMG) signals. It has been widely used in sEMG pattern classification for both clinical and engineering applications. This study investigated the usefulness of extracting sEMG features from multiple-level wavelet decomposition and reconstruction. A suitable wavelet based function was used to yield useful resolution components from the sEMG signal. The optimal sEMG resolution component was selected and then its reconstruction carried out. Throughout this process, noise and unwanted sEMG components were removed. Effective sEMG components were extracted with twenty-five state-of-the-art features in both the time domain and the frequency domain. Two criteria were deployed in the evaluation, scatter graphs and a class separation index. The experimental results show that most sEMG features extracted from the reconstructed sEMG signal of the first and second-level wavelet detail coefficients yield improved class separability in feature space. Some features extracted from the sub-signals are recommended such as the myopulse percentage rate, zero crossing, Willison amplitude and the mean absolute value. The proposed method will ensure that the classification accuracy will be as high as possible while the computational time will be as low as possible. Ill. 3, bibl. 24, tabl. 2 (in English; abstracts in English and Lithuanian). A. Phinyomark, A. Nuidod, P. Phukpattaranont, C. Limsakul. Požymių išskyrimas ir vilnelių transformacijos koeficientų sumažinimas elektromiografijos atvaizdams klasifikuoti // Elektronika ir elektrotechnika. -Kaunas: Technologija, 2012. -Nr. 6(122). -P. 27-32.Vilnelių transformacija yra vienas iš geriausių signalų apdorojimo įrankių atliekant paviršinės elektromiografijos (pEMG) signalų analizę. Ji plačiai naudojama klasifikuojant pEMG atvaizdus tiek klinikinėse, tiek inžinerinėse taikomosiose programose. Panaudota tinkama vilnelių funkcija siekiant gauti tinkamos rezoliucijos komponentus iš pEMG signalo. Buvo parinktas optimalus pEMG rezoliucijos komponentas ir atlikta jos rekonstrukcija. Šio proceso metu buvo pašalintas triukšmas ir nepageidaujami pEMG komponentai. Eksperimentiniai rezultatai parodė, kad dauguma pEMG bruožų, išskirtų iš rekonstruoto pEMG signalo, padeda geriau atskirti klases bruožų erdvėje. Pasiūlytas metodas užtikrina kiek įmanoma didesnį klasifikacijos tikslumą ir mažesnę skaičiavimo trukmę. Il. 3, bibl. 24, lent. 2 (anglų kalba; santraukos anglų ir lietuvių k.).

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The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.