Pattern Classification of EMG Signals Using Neural Networks during a Series of Motions
Bibliographic Information
- Other Title
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- ニューラルネットによる連続動作EMGパターンの識別
- ニューラル ネット ニヨル レンゾク ドウサ EMG パターン ノ シキベツ
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Abstract
This paper proposes a pattern classification method of time series EMG signals using neural networks in order to estimate a motion intended by a human operator. To achieve successful classification for non-stationary EMG signals, a new network structure that combines a common back-propagation neural network with recurrent neural filters is used. This network is suitable to express time-varying characteristics of time-series EMG signals. Also dynamics of a terminal attractor is incorporated in the learning rule in order to regulate convergence time. The convergence time is always less than the upper limit of a specified time, so that mental stress of the operator for waitting the convergence of learning can be reduced. In the experiments, the EMG signals measured from four subjects during a series of six motions are used. It is shown from the results that the proposed network can achieve a relatively high classification performance and the learning converges within a specified time.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 117 (10), 1490-1497, 1997
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390282679584354816
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- NII Article ID
- 10000141786
- 130006843572
- 10002811930
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 4303601
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed