Recognition of EMG signal patterns by neural networks
Recognition of EMG signal patterns by neural networks
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DOI：
10.1109/iconip.2002.1198158
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发表时间：
2002-11
期刊：
影响因子：
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通讯作者：
Y. Matsumura;Y. Mitsukura;M. Fukumi;N. Akamatsu;F. Takeda
中科院分区：
文献类型：
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作者：
Y. Matsumura;Y. Mitsukura;M. Fukumi;N. Akamatsu;F. Takeda
本文尝试利用神经网络对肌电信号进行识别。将干燥状态下的电极固定在手腕上，然后测量肌电。利用神经网络将这些肌电信号分为中性、上下、左右、腕内、腕外七类。神经网络学习FFT谱来对它们进行分类。此外，在进行识别实验之前，我们使用简单的主成分分析进行主成分分析。计算机仿真结果表明，该方法对肌电信号的分类是有效的。
The 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 neural network learns FFT spectra to classify them. Moreover, we perform the principal component analysis using the simple principal component analysis before we perform recognition experiments. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.