Electromyography Signal On Biceps Muscle In Time Domain Analysis
Features extraction is important for electromyography (EMG) signal analysis. The paper’s objective is to evaluate the features extraction of the EMG signal. The experimental set-up for EMG signal acquisition followed the procedures recommended by Europe’s Surface Electromyography for Non-invasive As...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Malaysia Pahang, Malaysia
2014
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/14068/1/17_Yahya_et_al.pdf http://eprints.utem.edu.my/id/eprint/14068/ http://jmes.ump.edu.my/index.php/archive/volume-7-december-2014.html |
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| Summary: | Features extraction is important for electromyography (EMG) signal analysis. The paper’s objective is to evaluate the features extraction of the EMG signal. The experimental set-up for EMG signal acquisition followed the procedures recommended by Europe’s Surface Electromyography for Non-invasive Assessment of Muscle (SENIAM) project. The EMG signal’s data were analysed in the time domain to get the features. Four features were considered based on the analysis, which are IEMG, MAV, VAR and RMS. The average muscle force condition can be estimated by correlation between the EMG voltage amplitude with linear estimation with the full-wave rectification method. The R-squared value determined the correlation between the EMG voltage amplitude with the loads. IEMG was chosen as the reference feature for estimation of the muscle’s force due to its R-squared value equal to 0.997. By referring to the IEMG, the linear equation obtained from the correlation was used for estimation of the muscle’s force. These findings can be integrated to design a muscle force model based on the biceps muscle. |
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