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Artificial Neural Network Prediction of Angle Based on Surface Electromyography

机译:基于表面肌电图的人工神经网络角度预测

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The electromyography (EMG) signal can be considered as a manifestation of the muscle activity. An artificial neural network to predict the elbow joint angle using SEMG signals was developed in this paper. SEMG was collected from biceps and triceps and analyzed in statistic characteristics. A three-layer BP neural network was constructed and then was trained by improved back propagation algorism to predict the elbow joint angle by using the RMS of the raw SEMG signal. The experimental results show that this neural network model can well represent the relationship between SEMG signals and elbow joint angles.
机译:肌电图(EMG)信号可被视为肌肉活动的体现。本文开发了一种使用SEMG信号预测肘关节角度的人工神经网络。从二头肌和三头肌中收集了SEMG,并对其统计特征进行了分析。构造了一个三层BP神经网络,然后通过改进的反向传播算法对其进行训练,以利用原始SEMG信号的RMS来预测肘关节角度。实验结果表明,该神经网络模型可以很好地表示SEMG信号与肘关节角度之间的关系。

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