Surface EMG classification using moving approximate entropy
Surface EMG classification using moving approximate entropy
Moving approximate entropy has been proposed as a new method to extract information from the surface electromyographic signal. Twenty subjects performed wrist flexion/extension, isometric contraction and co-contraction while electromyographic signals were recorded with surface electrodes. A moving data window of 200 values was applied to the data (moving approximate entropy). The results show that there is regularity in an EMG signal at the beginning and end of a muscle contraction with low regularity during the middle part.
1163-1167
Ahmad, Siti A.
acbeb287-5b41-4c24-be5e-c9032028a977
Chappell, P.H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e
2008
Ahmad, Siti A.
acbeb287-5b41-4c24-be5e-c9032028a977
Chappell, P.H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e
Ahmad, Siti A. and Chappell, P.H.
(2008)
Surface EMG classification using moving approximate entropy.
In International Conference on Intelligent and Advanced Systems, 2007. ICIAS 2007.
IEEE.
.
(doi:10.1109/ICIAS.2007.4658567).
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Conference or Workshop Item
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Abstract
Moving approximate entropy has been proposed as a new method to extract information from the surface electromyographic signal. Twenty subjects performed wrist flexion/extension, isometric contraction and co-contraction while electromyographic signals were recorded with surface electrodes. A moving data window of 200 values was applied to the data (moving approximate entropy). The results show that there is regularity in an EMG signal at the beginning and end of a muscle contraction with low regularity during the middle part.
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Published date: 2008
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EEE
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Local EPrints ID: 269719
URI: https://eprints.soton.ac.uk/id/eprint/269719
PURE UUID: 46b29219-bd2e-4aeb-b8cf-bf3a6e985332
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Date deposited: 21 Apr 2010 07:46
Last modified: 19 Jul 2019 18:01
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Author:
Siti A. Ahmad
Author:
P.H. Chappell
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