Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network
Electromyography control system (ECS) is a well-known technique for prosthesis control application. It consists of two main modules namely feature extraction and classification. This paper presents the investigation of the classification module in the ECS. The surface electromyographic (EMG) signal...
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2012
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my.upm.eprints.316622018-10-23T08:08:30Z http://psasir.upm.edu.my/id/eprint/31662/ Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network Ahmad, Siti Anom Khalid, Mohd Asyraf Ishak, Asnor Juraiza Md. Ali, Sawal Hamid Electromyography control system (ECS) is a well-known technique for prosthesis control application. It consists of two main modules namely feature extraction and classification. This paper presents the investigation of the classification module in the ECS. The surface electromyographic (EMG) signals were recorded from flexor and extensor muscles of the forearm during wrist flexion and extension. Standard deviation and mean absolute value were used to extract information from the raw EMG signals. Two different classifiers, fuzzy logic and artificial neural network were used in investigating the surface EMG signals. The classifier is responsible to determine the movement of the subject’s limb during specific moment. The two classifiers were compared in terms of their performance. SciTePress 2012 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/31662/1/31662.pdf Ahmad, Siti Anom and Khalid, Mohd Asyraf and Ishak, Asnor Juraiza and Md. Ali, Sawal Hamid (2012) Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network. In: International Conference on Bio-inspired Systems and Signal Processing, 1-4 Feb. 2012, Algarve, Portugal. (pp. 317-320). 10.5220/0003696603170320 |
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| description |
Electromyography control system (ECS) is a well-known technique for prosthesis control application. It consists of two main modules namely feature extraction and classification. This paper presents the investigation of the classification module in the ECS. The surface electromyographic (EMG) signals were recorded from flexor and extensor muscles of the forearm during wrist flexion and extension. Standard deviation and mean absolute value were used to extract information from the raw EMG signals. Two different classifiers, fuzzy logic and artificial neural network were used in investigating the surface EMG signals. The classifier is responsible to determine the movement of the subject’s limb during specific moment. The two classifiers were compared in terms of their performance. |
| format |
Conference or Workshop Item |
| author |
Ahmad, Siti Anom Khalid, Mohd Asyraf Ishak, Asnor Juraiza Md. Ali, Sawal Hamid |
| spellingShingle |
Ahmad, Siti Anom Khalid, Mohd Asyraf Ishak, Asnor Juraiza Md. Ali, Sawal Hamid Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| author_facet |
Ahmad, Siti Anom Khalid, Mohd Asyraf Ishak, Asnor Juraiza Md. Ali, Sawal Hamid |
| author_sort |
Ahmad, Siti Anom |
| title |
Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| title_short |
Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| title_full |
Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| title_fullStr |
Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| title_full_unstemmed |
Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network |
| title_sort |
surface emg classification for prosthesis control: fuzzy logic vs. artificial neural network |
| publisher |
SciTePress |
| publishDate |
2012 |
| url |
http://psasir.upm.edu.my/id/eprint/31662/1/31662.pdf http://psasir.upm.edu.my/id/eprint/31662/ |
| _version_ |
1643830387418333184 |
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13.251813 |