Abstract
Bioinformation is information generated from biological movement. By using a variety of modern technologies, we can use this information to form a meaningful model for researchers to study. An electromyographic (EMG) signal is one type of bioinformation that is used in many areas to help people study human muscle movement. This information can help in both clinical areas and industrial areas. EMG is a very complicated signal, so processing it is vital. The processing of EMG signals is divided into collection, denoising, decomposition, feature extraction and classification steps. In this article, the wavelet denoising step and several decomposition processes are discussed to show the usage of this technique in the final classification step. At the end of the study, we find that after the wavelet denoising step, the classification accuracy, which uses the K-nearest neighbor of the independent component analysis features, improves, but the accuracy of the wavelet coefficient features and autoregression coefficient features decreases.
Highlights
Discussion of the Influence ofAn electromyographic (EMG) signal is a time-series signal that is collected mainly for clinical applications, and it has been widely used in research laboratories to meet the needs of sectors such as biomechanics and motor control [1]
By obscuring the composition of the EMG signal, we can determine that the EMG signal is composed of many MUAPs, so the features of classical techniques used for analysis, which are extracted from s-EMG signals, cannot guarantee effectiveness [14]
The independent component analysis (ICA) algorithm has appeared in many multi-dimension signal processing processes and mostly plays an important role, which is a special case of the blind source separation technique [8]
Summary
An electromyographic (EMG) signal is a time-series signal that is collected mainly for clinical applications, and it has been widely used in research laboratories to meet the needs of sectors such as biomechanics and motor control [1]. Mary described electromyographic signals as muscular activity involving muscle neurons and regulating muscle fiber inside the motor unit [3]. Based on this description and other research, the collection methods of EMG signals are divided into two types. EMG signals near other, which thisin loses the crosstalk between different signals neareach each other, which all the signals eachthe channel or tobetween use eachdifferent channel’s signal to perform feature extraction; causes information loss. EMG signals, and we compare themsome to normal processing causes information loss. To process multichannel signals,the and we compare them to normal processing methods ods toshow show thedifferences differences between thefeatures features thatthe thedifferent different methods yield. To show the differences between the features that the different methods yield
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