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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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
SciTePress
2012
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/31662/1/31662.pdf http://psasir.upm.edu.my/id/eprint/31662/ |
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| 要約: | 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. |
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