Recognition of EMG signal patterns by neural networks

Yuji Matsumura; Yasue Mitsukura; Minoru Fukumi; Norio Akamatsu; Fumiaki Takeda

doi:10.1007/978-3-540-45224-9_85

Recognition of EMG signal patterns by neural networks

Yuji Matsumura, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, Fumiaki Takeda

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

Original language	English
Pages (from-to)	623-630
Number of pages	8
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2773 PART 1
DOIs	https://doi.org/10.1007/978-3-540-45224-9_85
Publication status	Published - 2003 Jan 1
Externally published	Yes
Event	7th International Conference, KES 2003 - Oxford, United Kingdom Duration: 2003 Sept 3 → 2003 Sept 5

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-45224-9_85

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title = "Recognition of EMG signal patterns by neural networks",

abstract = "This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.",

author = "Yuji Matsumura and Yasue Mitsukura and Minoru Fukumi and Norio Akamatsu and Fumiaki Takeda",

year = "2003",

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doi = "10.1007/978-3-540-45224-9_85",

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journal = "Lecture Notes in Computer Science",

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TY - JOUR

T1 - Recognition of EMG signal patterns by neural networks

AU - Matsumura, Yuji

AU - Mitsukura, Yasue

AU - Fukumi, Minoru

AU - Akamatsu, Norio

AU - Takeda, Fumiaki

PY - 2003/1/1

Y1 - 2003/1/1

N2 - This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

AB - This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

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UR - http://www.scopus.com/inward/citedby.url?scp=8344285708&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-45224-9_85

DO - 10.1007/978-3-540-45224-9_85

M3 - Conference article

AN - SCOPUS:8344285708

SN - 0302-9743

VL - 2773 PART 1

SP - 623

EP - 630

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 7th International Conference, KES 2003

Y2 - 3 September 2003 through 5 September 2003

ER -

Recognition of EMG signal patterns by neural networks

Abstract

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