Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb

Normally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gri...

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Main Authors:	Sidek, Shahrul Na'im, Jalaludin, Nor Anija, Shamsudin, Abu Ubaidah
Format:	Article
Language:	English
Published:	Elsevier 2012
Subjects:	TA168 Systems engineering
Online Access:	http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/1/Surface_Electromyography_%28sEMG%29-based_thumb-tip_angle.pdf

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recordtype	eprints
spelling	iium-245092014-09-15T02:23:53Z http://irep.iium.edu.my/24509/ Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb Sidek, Shahrul Na'im Jalaludin, Nor Anija Shamsudin, Abu Ubaidah TA168 Systems engineering Normally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. In this work the relationship between the electromyogram (EMG) signals and thumb tip forces are investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The classification of the EMG signals based on different force and thumb configuration is performed by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to classify the EMG signals. Elsevier 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/24509/1/Surface_Electromyography_%28sEMG%29-based_thumb-tip_angle.pdf Sidek, Shahrul Na'im and Jalaludin, Nor Anija and Shamsudin, Abu Ubaidah (2012) Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb. Procedia Engineering, 41. pp. 650-656. ISSN 1877-7058 http://www.sciencedirect.com/science/article/pii/S1877705812026252# 10.1016/j.proeng.2012.07.225
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TA168 Systems engineering
spellingShingle	TA168 Systems engineering Sidek, Shahrul Na'im Jalaludin, Nor Anija Shamsudin, Abu Ubaidah Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
description	Normally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. In this work the relationship between the electromyogram (EMG) signals and thumb tip forces are investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The classification of the EMG signals based on different force and thumb configuration is performed by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to classify the EMG signals.
format	Article
author	Sidek, Shahrul Na'im Jalaludin, Nor Anija Shamsudin, Abu Ubaidah
author_facet	Sidek, Shahrul Na'im Jalaludin, Nor Anija Shamsudin, Abu Ubaidah
author_sort	Sidek, Shahrul Na'im
title	Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
title_short	Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
title_full	Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
title_fullStr	Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
title_full_unstemmed	Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
title_sort	surface electromyography (semg)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb
publisher	Elsevier
publishDate	2012
url	http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/ http://irep.iium.edu.my/24509/1/Surface_Electromyography_%28sEMG%29-based_thumb-tip_angle.pdf
first_indexed	2023-09-18T20:36:45Z
last_indexed	2023-09-18T20:36:45Z
_version_	1777409096656355328

Surface Electromyography (sEMG)-based thumb-tip angle and force estimation using artificial neural network for prosthetic thumb

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