Features extraction of electromyography signals in time domain on biceps brachii muscle

Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Since EMG signals contain a wealth of information about muscle functions, there are many approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG s...

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Bibliographic Details
Main Authors: Wan Daud, Wan Mohd. Bukhari (Author), Yahya, Abu Bakar (Author), Chong, Shin Horng (Author), Sulaima, Mohamad Fani (Author), Sudirman, Rubita (Author)
Format: Article
Language:English
Published: 2013.
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Online Access:Get fulltext
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001 40388
042 |a dc 
100 1 0 |a Wan Daud, Wan Mohd. Bukhari  |e author 
700 1 0 |a Yahya, Abu Bakar  |e author 
700 1 0 |a Chong, Shin Horng  |e author 
700 1 0 |a Sulaima, Mohamad Fani  |e author 
700 1 0 |a Sudirman, Rubita  |e author 
245 0 0 |a Features extraction of electromyography signals in time domain on biceps brachii muscle 
260 |c 2013. 
856 |z Get fulltext  |u http://eprints.utm.my/id/eprint/40388/1/WanMohdBukhari2013_FeaturesExtractionofElectromyographySignals.pdf 
520 |a Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Since EMG signals contain a wealth of information about muscle functions, there are many approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG signal. The ideal feature is important for the achievement in EMG analysis. Hence, the objective of this paper is to evaluate the features extraction of time domain from the EMG signal. The experiment was setup according to surface electromyography for noninvasive assessment of muscle (SENIAM). The recorded data was analyzed in time domain to get the features. Based on the analysis, three features have been considered based on statistical features. The features was then been evaluate by getting the percentage error of each feature. The less percentage error determines the ideal feature. The results shows that the extracted features of the EMG signals in time domain can be implement in signal classification. These findings could be integrated to design a signal classification based on the features extraction. 
546 |a en 
650 0 4 |a TK Electrical engineering. Electronics Nuclear engineering