Hand and elbow gesture recognition based on electromyography signal

This project intends to analyze and classify the Electromyography (EMG) signal of muscles that is involved in certain hand and elbow gestures. The Electromyography (EMG) data acquisition protocol is then outlined and performed where the recorded Electromyography (EMG) signal corresponds with cert...

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Main Author:	Abdulhafidh Al-Dubai, Ala Abobakr
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.uthm.edu.my/451/1/24p%20ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI.pdf http://eprints.uthm.edu.my/451/2/ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI%20WATERMARK.pdf
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id	my-uthm-ep.451
record_format	uketd_dc
spelling	my-uthm-ep.4512021-07-25T06:33:38Z Hand and elbow gesture recognition based on electromyography signal 2019-01 Abdulhafidh Al-Dubai, Ala Abobakr TJ Mechanical engineering and machinery This project intends to analyze and classify the Electromyography (EMG) signal of muscles that is involved in certain hand and elbow gestures. The Electromyography (EMG) data acquisition protocol is then outlined and performed where the recorded Electromyography (EMG) signal corresponds with certain hand and elbow gestures. Therefore, four hand gestures were targeted, “hand contraction, forearm rotation, wrist extension and wrist flexion”. Thus, the EMG data that have been collected from 6 subjects are compared at a small demographic scale which is age and gender. Whereas, the EMG signals are collected using the software Lab-Chart 7 with 2 channel and 5 electrodes. The pre-processing of the EMG raw signals is presented using a 6th order Butterworth band pass filter, low and high pass filter with normalization. Furthermore, the features are evaluated using Variance (VAR), Standard Deviation (SD) and Root Mean Square (RMS) to test the significance of the features. Nevertheless, the K-Nearest Neighbour (KNN) classifier is used in order to classify the EMG signals for hand gestures. Lastly, the results from this project showed that the classifier has classified the gestures with a low performance due to the fewer amounts of the subjects and some other reasons. 2019-01 Thesis http://eprints.uthm.edu.my/451/ http://eprints.uthm.edu.my/451/1/24p%20ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI.pdf text en public http://eprints.uthm.edu.my/451/2/ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Abdulhafidh Al-Dubai, Ala Abobakr Hand and elbow gesture recognition based on electromyography signal
description	This project intends to analyze and classify the Electromyography (EMG) signal of muscles that is involved in certain hand and elbow gestures. The Electromyography (EMG) data acquisition protocol is then outlined and performed where the recorded Electromyography (EMG) signal corresponds with certain hand and elbow gestures. Therefore, four hand gestures were targeted, “hand contraction, forearm rotation, wrist extension and wrist flexion”. Thus, the EMG data that have been collected from 6 subjects are compared at a small demographic scale which is age and gender. Whereas, the EMG signals are collected using the software Lab-Chart 7 with 2 channel and 5 electrodes. The pre-processing of the EMG raw signals is presented using a 6th order Butterworth band pass filter, low and high pass filter with normalization. Furthermore, the features are evaluated using Variance (VAR), Standard Deviation (SD) and Root Mean Square (RMS) to test the significance of the features. Nevertheless, the K-Nearest Neighbour (KNN) classifier is used in order to classify the EMG signals for hand gestures. Lastly, the results from this project showed that the classifier has classified the gestures with a low performance due to the fewer amounts of the subjects and some other reasons.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdulhafidh Al-Dubai, Ala Abobakr
author_facet	Abdulhafidh Al-Dubai, Ala Abobakr
author_sort	Abdulhafidh Al-Dubai, Ala Abobakr
title	Hand and elbow gesture recognition based on electromyography signal
title_short	Hand and elbow gesture recognition based on electromyography signal
title_full	Hand and elbow gesture recognition based on electromyography signal
title_fullStr	Hand and elbow gesture recognition based on electromyography signal
title_full_unstemmed	Hand and elbow gesture recognition based on electromyography signal
title_sort	hand and elbow gesture recognition based on electromyography signal
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2019
url	http://eprints.uthm.edu.my/451/1/24p%20ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI.pdf http://eprints.uthm.edu.my/451/2/ALA%20ABOBAKR%20ABDULHAFIDH%20AL-DUBAI%20WATERMARK.pdf
_version_	1747830613335867392

Hand and elbow gesture recognition based on electromyography signal

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