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Surface EMG classification for prosthesis control: fuzzy logic vs. artificial neural network


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).

Abstract / Synopsis

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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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number:
Publisher: SciTePress
Notes: Full text are available at Special Collection Division Office.
Keywords: Prosthesis control; Electromyography; Classification; Fuzzy logic; Artificial neural network
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 09 Jul 2014 11:03
Last Modified: 23 Oct 2018 16:08
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