Recognition System for EMG Signals by using Non-negative Matrix Factorization

Yuuki Yazama; Yasue Mitsukura; Minoru Fukumi; Norio Akamatsu

Recognition System for EMG Signals by using Non-negative Matrix Factorization

Yuuki Yazama, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

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

Abstract

IIn this paper, the feature vector of a few dimension for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.

Original language	English
Pages	2130-2133
Number of pages	4
Publication status	Published - 2003 Sept 24
Externally published	Yes
Event	International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: 2003 Jul 20 → 2003 Jul 24

Other

Other	International Joint Conference on Neural Networks 2003
Country/Territory	United States
City	Portland, OR
Period	03/7/20 → 03/7/24

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

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title = "Recognition System for EMG Signals by using Non-negative Matrix Factorization",

abstract = "IIn this paper, the feature vector of a few dimension for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.",

author = "Yuuki Yazama and Yasue Mitsukura and Minoru Fukumi and Norio Akamatsu",

year = "2003",

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AU - Yazama, Yuuki

AU - Mitsukura, Yasue

AU - Fukumi, Minoru

AU - Akamatsu, Norio

PY - 2003/9/24

Y1 - 2003/9/24

N2 - IIn this paper, the feature vector of a few dimension for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.

AB - IIn this paper, the feature vector of a few dimension for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.

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M3 - Paper

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T2 - International Joint Conference on Neural Networks 2003

Y2 - 20 July 2003 through 24 July 2003

ER -

Recognition System for EMG Signals by using Non-negative Matrix Factorization

Abstract

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ASJC Scopus subject areas

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