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首页> 外文会议>International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology;ECTI-CON 2010 >EMG signal estimation based on adaptive wavelet shrinkage for multifunction myoelectric control
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EMG signal estimation based on adaptive wavelet shrinkage for multifunction myoelectric control

机译：基于自适应小波收缩的肌电信号肌电信号估计

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摘要

Electromyography (EMG) signal is interfered with different kinds of noise and wavelet denoising algorithm is a powerful method to reduce noises in EMG signal. Hard and soft shrinkage, traditional wavelet transformation, are applied to wavelet coefficients with threshold value. From the limitation of hard and soft shrinkage, this study proposes nine improved wavelet shrinkage methods that achieve a compromise between two standards. EMG signal from six hand motions with additive noise at different signal-to-noise ratios were applied to evaluate the efficiency of the methods in denoising viewpoint. In addition, features of estimated denoising signal are sent to classification task to measure the performance in myoelectric control. The experimental results show that adaptive wavelet shrinkage method (ADP) provides the better performance than traditional methods and other modified methods in both of denoising and pattern recognition viewpoints. Accuracy of recognition of EMG signal transformed by ADP is improved about 6.5–78.5% depending on the level of noise. ADP is an efficient method for producing useful EMG signal without noise and improving application of myoelectric control.
机译：肌电图（EMG）信号会受到不同类型的噪声的干扰，而小波降噪算法是减少EMG信号中噪声的有效方法。硬收缩率和软收缩率是传统的小波变换，应用于具有阈值的小波系数。从硬收缩和软收缩的局限性出发，本研究提出了九种改进的小波收缩方法，可以在两个标准之间达成折衷。应用来自六个手部动作的EMG信号，并以不同的信噪比添加噪声，以评估该方法在降噪方面的效率。另外，将估计的降噪信号的特征发送到分类任务以测量肌电控制中的性能。实验结果表明，在去噪和模式识别方面，自适应小波收缩方法（ADP）的性能均优于传统方法和其他改进方法。根据噪声水平，ADP转换的EMG信号的识别精度提高了约6.5–78.5％。 ADP是一种有效的方法，可产生有用的EMG信号而无噪声，并改善了肌电控制的应用。

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