Signal processing of EMG signal for continuous thumbangle estimation

Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in r...

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Main Authors:	Siddiqi, Abdul Rahman, Sidek, Shahrul Naim, Khorshidtalab, Aida
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2016
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/14/52509-Signal%20processing%20of%20EMG%20signal%20for%20continuous%20thumb-angle%20estimation_SCOPUS.pdf http://irep.iium.edu.my/52509/15/52509-updated.pdf

id	iium-52509
recordtype	eprints
spelling	iium-525092019-01-10T04:55:22Z http://irep.iium.edu.my/52509/ Signal processing of EMG signal for continuous thumbangle estimation Siddiqi, Abdul Rahman Sidek, Shahrul Naim Khorshidtalab, Aida T Technology (General) Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise-discretization’ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53%. IEEE 2016-01-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/52509/14/52509-Signal%20processing%20of%20EMG%20signal%20for%20continuous%20thumb-angle%20estimation_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/52509/15/52509-updated.pdf Siddiqi, Abdul Rahman and Sidek, Shahrul Naim and Khorshidtalab, Aida (2016) Signal processing of EMG signal for continuous thumbangle estimation. In: 41st Annual Conference of the IEEE Industrial Electronics Society -IECON 2015, 9th-12th November 2015, Yokohama, Japan. http://ieeexplore.ieee.org/document/7392128/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Siddiqi, Abdul Rahman Sidek, Shahrul Naim Khorshidtalab, Aida Signal processing of EMG signal for continuous thumbangle estimation
description	Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise-discretization’ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53%.
format	Conference or Workshop Item
author	Siddiqi, Abdul Rahman Sidek, Shahrul Naim Khorshidtalab, Aida
author_facet	Siddiqi, Abdul Rahman Sidek, Shahrul Naim Khorshidtalab, Aida
author_sort	Siddiqi, Abdul Rahman
title	Signal processing of EMG signal for continuous thumbangle estimation
title_short	Signal processing of EMG signal for continuous thumbangle estimation
title_full	Signal processing of EMG signal for continuous thumbangle estimation
title_fullStr	Signal processing of EMG signal for continuous thumbangle estimation
title_full_unstemmed	Signal processing of EMG signal for continuous thumbangle estimation
title_sort	signal processing of emg signal for continuous thumbangle estimation
publisher	IEEE
publishDate	2016
url	http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/14/52509-Signal%20processing%20of%20EMG%20signal%20for%20continuous%20thumb-angle%20estimation_SCOPUS.pdf http://irep.iium.edu.my/52509/15/52509-updated.pdf
first_indexed	2023-09-18T21:14:23Z
last_indexed	2023-09-18T21:14:23Z
_version_	1777411464801288192

Signal processing of EMG signal for continuous thumbangle estimation

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