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Robust finger motion classification using frequency characteristics of surface electromyogram signals

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Main Authors: Ishikawa, Keisuke, Akita, Junichi, Toda, Masashi, Kondo, Kazuaki, Sakurazawa, Shigeru, Nakamura, Yuichi
Other Authors: g2110005@fun.ac.jp
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Surface-Electromyogram Signals (EMG)
Finger motion classification
Frequency characteristics
Tension estimate
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21421
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spelling my.unimap-214212012-10-18T08:33:44Z Robust finger motion classification using frequency characteristics of surface electromyogram signals Ishikawa, Keisuke Akita, Junichi Toda, Masashi Kondo, Kazuaki Sakurazawa, Shigeru Nakamura, Yuichi g2110005@fun.ac.jp akita@is.t.kanazawau.ac.jp toda@fun.ac.jp kondo@media.kyotou.ac.jp sakura@fun.ac.jp yuichi@ccm.media.kyotou.ac.jp Surface-Electromyogram Signals (EMG) Finger motion classification Frequency characteristics Tension estimate Link to publisher's homepage at http://ieeexplore.ieee.org/ Finger motion classification using surface electromyogram (EMG) signals is currently being applied to myoelectric prosthetic hands with methods of pattern classification. It can be used to classify motion with great accuracy under ideal circumstances. However, the precision of classification falling to change the quantity of EMG feature with muscle fatigue has been a problem. We addressed this problem in this study, which was aimed at robustly classifying finger motion against changes in EMG features with muscle fatigue. We tested the changes in EMG features before and after muscle fatigue and propose a robust feature that uses a methods of estimating tension in finger motion by taking muscle fatigue into consideration. 2012-10-18T08:33:43Z 2012-10-18T08:33:43Z 2012-02-27 Working Paper p. 362-367 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179039 http://hdl.handle.net/123456789/21421 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 Surface-Electromyogram Signals (EMG)
Finger motion classification
Frequency characteristics
Tension estimate
spellingShingle Surface-Electromyogram Signals (EMG)
Finger motion classification
Frequency characteristics
Tension estimate
Ishikawa, Keisuke
Akita, Junichi
Toda, Masashi
Kondo, Kazuaki
Sakurazawa, Shigeru
Nakamura, Yuichi
Robust finger motion classification using frequency characteristics of surface electromyogram signals
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 g2110005@fun.ac.jp
author_facet g2110005@fun.ac.jp
Ishikawa, Keisuke
Akita, Junichi
Toda, Masashi
Kondo, Kazuaki
Sakurazawa, Shigeru
Nakamura, Yuichi
format Working Paper
author Ishikawa, Keisuke
Akita, Junichi
Toda, Masashi
Kondo, Kazuaki
Sakurazawa, Shigeru
Nakamura, Yuichi
author_sort Ishikawa, Keisuke
title Robust finger motion classification using frequency characteristics of surface electromyogram signals
title_short Robust finger motion classification using frequency characteristics of surface electromyogram signals
title_full Robust finger motion classification using frequency characteristics of surface electromyogram signals
title_fullStr Robust finger motion classification using frequency characteristics of surface electromyogram signals
title_full_unstemmed Robust finger motion classification using frequency characteristics of surface electromyogram signals
title_sort robust finger motion classification using frequency characteristics of surface electromyogram signals
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21421
_version_ 1643793372273442816
score 13.222552

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