Classification of EMG signal based on human percentile using SOM

Jali, Mohd Hafiz and Bohari, Zul Hasrizal and Sulaima, Mohamad Fani and Mohd Nasir, Mohamad Na'im and Jaafar, Hazriq Izzuan (2014) Classification of EMG signal based on human percentile using SOM. Research Journal of Applied Sciences, Engineering and Technology, 8 (2). pp. 235-242. ISSN 2040-7459

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Abstract

Electromyography (EMG) is a bio signal that is formed by physiological variations in the state of muscle fibre membranes. Pattern recognition is one of the fields in the bio-signal processing which classified the signal into certain desired categories with subject to their area of application. This study described the classification of the EMG signal based on human body percentile using Self Organizing Mapping (SOM) technique. Different human percentile definitively varies the arm circumference size. Variation of arm circumference is due to fatty tissue that lay between active muscle and skin. Generally the fatty tissue would decrease the overall amplitude of the EMG signal. Data collection is conducted randomly with fifteen subjects that have numerous percentiles using non-invasive technique at Biceps Brachii muscle. The signals are then going through filtering process to prepare them for the next stage. Then, five well known time domain feature extraction methods are applied to the signal before the classification process. Self Organizing Map (SOM) technique is used as a classifier to discriminate between the human percentiles. Result shows that SOM is capable in clustering the EMG signal to the desired human percentile categories by optimizing the neurons of the technique.

Item Type:	Article
Uncontrolled Keywords:	Biceps brachii, classification, electromyography, human percentile, pattern recognition, self organizing map
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering > Department of Diploma Studies
Depositing User:	MOHD HAFIZ JALI
Date Deposited:	29 Oct 2014 12:02
Last Modified:	24 Jul 2023 15:48
URI:	http://eprints.utem.edu.my/id/eprint/13601
Statistic Details:	View Download Statistic

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