Recognition of wrist EMG signal patterns using neural networks
Recognition of wrist EMG signal patterns using neural networks
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发表时间：
2004-12
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通讯作者：
Y. Matsumura;M. Fukumi;N. Akamatsu;K. Nakaura
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作者：
Y. Matsumura;M. Fukumi;N. Akamatsu;K. Nakaura
近年来，包括蜂窝电话在内的信息终端由于IT的新发展而具有高性能。如果使用标准（诸如蓝牙），则它使我们能够仅使用一个设备来收集和执行各种装置的操作接口。例如，手机可以开关，可以调节音量，CD播放器可以调节音量等等，我们称之为“全操作设备”。然而，这样的设备还不可用。因此，我们提出了一个识别系统的基础上，手腕运动，专注于肌电图（EMG），使用主体肌肉的自愿运动产生的身体信号，作为初始阶段的建设总的操作设备。本文尝试用神经网络对肌电信号进行识别。将干燥状态下的电极贴在手腕上，然后测量肌电信号。这些肌电信号被分类使用神经网络分为七类：中性，上下，左右，内扭曲，外扭曲。NN学习这些信号的FFT频谱，以便对其进行分类。此外，我们引入了一个模块化结构的神经网络，以提高识别精度。计算机仿真表明，该方法对肌电信号的分类是有效的。
Information terminals in recent years, including cellular phones, have high performances due to new advances in IT. If a standard (such as Bluetooth) is used, it enables us to collect and to perform operational interfaces of various apparatuses using one equipment only. For example, a cellular phone can be turned on and off, made into manners mode, a CD player's volume can be easily regulated, and so on. We call this "total operation device". However, such a device is not available yet. Therefore, we propose a recognition system based on wrist movements by focusing on ElectroMyoGram (EMG), using the body signals generated by voluntary movements of subject muscles, as the initial stage for construction of the total operation device. This paper tries to recognize EMG signals using neural networks (NNs). The electrodes under the dry state are attached to wrists and then EMG signals are measured. These EMG signals are classified using NNs into seven categories: neutral, up and down, right and left, inside twist, outside twist. The NN learns the FFT spectra of these signals in order to classify them. Moreover, we introduce a modular structure of the NN for improving the recognition accuracy. Computer simulations show that our approach is effective to classifying the EMG signals.