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Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force

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Bibliographic Details
Main Authors: Sidek, S. Naim, Mohideen, Ahmad Jazlan H.
Other Authors: naim.sidek@gmail.com
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Electromyogram (EMG) signal
Wrist joint angle
Hand grip force
Neural networks
Mean Absolute Error
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21284
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spelling my.unimap-212842012-10-10T07:18:40Z Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force Sidek, S. Naim Mohideen, Ahmad Jazlan H. naim.sidek@gmail.com ahmad_jazlan06@.yahoo.com Electromyogram (EMG) signal Wrist joint angle Hand grip force Neural networks Mean Absolute Error Link to publisher's homepage at http://ieeexplore.ieee.org/ Hand grip force, wrist flexion and wrist extension are the result of forearm muscle activity. In certain applications such as controlling the movements of a robotic prosthetic hand, information relating wrist joint angles to forearm muscle activity is useful to be used as part of the control algorithm. In this paper, we study the relationship between the muscular activity of forearm muscles and wrist joint angles/position while hand grip force is varied. In order to do that, an electronic circuit was constructed to amplify and filter the electromyogram (EMG) signals measured from the Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS) and Extensor Digitorum Communis (EDC). Neural networks were used to model the relationship between EMG signals and wrist joint angle data at different hand grip strength levels. The performances of the networks were indicated by the corresponding Mean Absolute Error values 2012-10-10T07:18:40Z 2012-10-10T07:18:40Z 2012-02-27 Working Paper p. 169-174 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178999 http://hdl.handle.net/123456789/21284 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Electromyogram (EMG) signal
Wrist joint angle
Hand grip force
Neural networks
Mean Absolute Error
spellingShingle Electromyogram (EMG) signal
Wrist joint angle
Hand grip force
Neural networks
Mean Absolute Error
Sidek, S. Naim
Mohideen, Ahmad Jazlan H.
Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 naim.sidek@gmail.com
author_facet naim.sidek@gmail.com
Sidek, S. Naim
Mohideen, Ahmad Jazlan H.
format Working Paper
author Sidek, S. Naim
Mohideen, Ahmad Jazlan H.
author_sort Sidek, S. Naim
title Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
title_short Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
title_full Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
title_fullStr Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
title_full_unstemmed Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force
title_sort measurement system to study the relationship between forearm emg signals and wrist position at varied hand grip force
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21284
_version_ 1643793335517708288
score 13.222552

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