Wrist EMG signals identification using neural network

Tadahiro Oyama; Yasue Mitsukura; Stephen Githinji Karungaru; Satoru Tsuge; Minoru Fukumi

doi:10.1109/IECON.2009.5415065

Wrist EMG signals identification using neural network

Tadahiro Oyama, Yasue Mitsukura, Stephen Githinji Karungaru, Satoru Tsuge, Minoru Fukumi

研究成果: Paper › 査読

12 被引用数 (Scopus)

抄録

Recently, researches of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.

本文言語	English
ページ	4286-4290
ページ数	5
DOI	https://doi.org/10.1109/IECON.2009.5415065
出版ステータス	Published - 2009 12月 1
外部発表	はい
イベント	35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009 - Porto, Portugal 継続期間: 2009 11月 3 → 2009 11月 5

Other

Other	35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009
国/地域	Portugal
City	Porto
Period	09/11/3 → 09/11/5

ASJC Scopus subject areas

制御およびシステム工学
電子工学および電気工学

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10.1109/IECON.2009.5415065

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title = "Wrist EMG signals identification using neural network",

abstract = "Recently, researches of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.",

author = "Tadahiro Oyama and Yasue Mitsukura and Karungaru, {Stephen Githinji} and Satoru Tsuge and Minoru Fukumi",

year = "2009",

month = dec,

day = "1",

doi = "10.1109/IECON.2009.5415065",

language = "English",

pages = "4286--4290",

note = "35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009 ; Conference date: 03-11-2009 Through 05-11-2009",

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AU - Oyama, Tadahiro

AU - Mitsukura, Yasue

AU - Karungaru, Stephen Githinji

AU - Tsuge, Satoru

AU - Fukumi, Minoru

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Recently, researches of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.

AB - Recently, researches of artificial arms and pointing devices using ElectroMyoGram(EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.

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T2 - 35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009

Y2 - 3 November 2009 through 5 November 2009

ER -

Wrist EMG signals identification using neural network

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