Reduction Of Limb Position Invariant Of SEMG Signals For Improved Prosthetic Control Using Spectrogram
Prostheses are artificial devices that replace a missing body part, which might be lost through injury, infection, or a condition present at birth. It is proposed to re-establish the normal functions of the missing body part and can be made by hand or with a computer-aided design. As per the World H...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTHM
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25801/2/REDUCTION%20OF%20LIMB.PDF http://eprints.utem.edu.my/id/eprint/25801/ https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8643/4267 |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | Prostheses are artificial devices that replace a missing body part, which might be lost through injury, infection, or a condition present at birth. It is proposed to re-establish the normal functions of the missing body part and can be made by hand or with a computer-aided design. As per the World Health Organization, around 160,000 individuals in Malaysia are required to use prostheses. One of the elements that influence the current prosthesis control is that the variety in the limb position and normal use results in different electromyogram (EMG) signals with the same movement at various positions. Consequently, the objective of this study is to ensure that amputees can control their prosthetics in an exact manner regardless of their hand movement and limb position. The raw EMG signals are taken from eight different hand movements’ classes at five different limb positions and each of these hand movements has seven electrodes attach to it. This paper utilizes time-frequency distribution which is spectrogram to extract the EMG feature and six SVM classification learners; linear, quadratic, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian were compared to find the most reasonable one for this application. The analysis performance is then verified based on classification accuracy. From the results, the overall accuracy for the classification is 65% (linear), 87.5% (quadratic) and 97.5% (cubic), 100% (fine Gaussian), 70% (medium Gaussian, and 45% (coarse Gaussian), respectively. It is believed that the study could fill in as knowledge to improve conventional prosthetic control strategies. |
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