摘要
手臂肌肉电信号是手臂运动过程中,通过电极记录下来的微小肌肉电流信号。由于该信号可以无创检测,模仿性强,已广泛应用于人工智能假肢领域。研究了小波阈值去噪算法在手臂肌电信号检测中的应用,针对传统阈值去噪算法存在离散点、误差大等缺点,引入非线性函数过渡,利用控制系数,改进阈值去噪算法。利用3种方法对同一含噪信号信息进行仿真分析,通过实验表明,该算法对采集到的手臂单通道肌电信号进行去噪处理,克服了传统阈值去燥算法的缺点,可以有效去除干扰信号,提高信噪比和信息识别的准确性。
The electrical signal of the arm muscles is a small muscle current signal recorded during the arm movement. Because the signal can be non-invasive and highly imitative, it has been widely used in the field of artificial intelligence in terms of artificial limb. This paper mainly focuses on the application of wavelet threshold denoising algorithm in arm EMG signal detection. Aiming at the disadvantage of traditional threshold denoising algorithm of discrete points and big error, it introduces non-linear function transition, and uses the control coefficient to improve threshold denoising algorithm. The using of three method on the same noisy signal information simulation experiments shows that the algorithm is used to denoise the acquired single arm EMG signal, overcoming the shortcomings of traditional dry threshold algorithm and effectively removing the interference signal. It also improves the accuracy of signal-to-noise ratio and information recognition.
出处
《电子技术应用》
2018年第3期122-125,共4页
Application of Electronic Technique
基金
忻州师范学院院级青年基金项目(QN201405)
关键词
手臂肌肉电信号
人工智能
小波阈值去噪
非线性函数
控制系数
arm muscle electrical signals
artificial intelligence
the wavelet threshold denoising
non-linear function
the control coefficient
