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

Research output: Contribution to conference › Paper › peer-review

12 Citations (Scopus)

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.

Original language	English
Pages	4286-4290
Number of pages	5
DOIs	https://doi.org/10.1109/IECON.2009.5415065
Publication status	Published - 2009 Dec 1
Externally published	Yes
Event	35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009 - Porto, Portugal Duration: 2009 Nov 3 → 2009 Nov 5

Other

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

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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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,

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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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T1 - Wrist EMG signals identification using neural network

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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