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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/21421

Title: Robust finger motion classification using frequency characteristics of surface electromyogram signals
Authors: Ishikawa, Keisuke
Akita, Junichi
Toda, Masashi
Kondo, Kazuaki
Sakurazawa, Shigeru
Nakamura, Yuichi
???metadata.dc.contributor.url???: 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
Keywords: Surface-Electromyogram Signals (EMG);Finger motion classification;Frequency characteristics;Tension estimate
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 362-367
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: 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.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179039
http://hdl.handle.net/123456789/21421
ISBN: 978-145771989-9
Appears in Collections:Conference Papers

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