Artificial elbow joint classification using upper arm based on surface-EMG signal

机译：基于表面EMG信号的上臂人工肘关节分类

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This paper proposes a method of elbow joint motions recognition using surface electro-myography (sEMG) signal for disable people with below-elbow amputation. It solves the situation that forearm without muscle cannot control forearm pronation. The pre-processing system processes sEMG signal to remove noise by soft threshold method, then denoising sEMG signal is sent to artificial neural network which trains features to recognize motions. The probability of this method activating 4 motions is 91.78% that was demonstrated by experimental results of recognition motions.

机译：本文提出了一种利用表面肌电信号（sEMG）识别肘关节以下残障人士的肘关节动作的方法。解决了没有肌肉的前臂无法控制前臂内旋的情况。预处理系统通过软阈值方法对sEMG信号进行处理以去除噪声，然后将去噪的sEMG信号发送到人工神经网络，后者训练特征以识别运动。识别运动的实验结果表明，该方法激活4个运动的概率为91.78％。

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来源
《2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences》|2017年|1-4|共4页
会议地点 Bangkok(TH)
作者
Jicheng Wang; Warit Wichakool;
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作者单位

Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai Songkhla 90112, Thailand;

Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai Songkhla 90112, Thailand;

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原文格式 PDF
正文语种 eng
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关键词
Elbow; Smoothing methods; Muscles; Band-pass filters; Feature extraction; Noise reduction; Prosthetics;

机译：弯头;平滑方法;肌肉;带通滤波器;特征提取;降噪;修复;;

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4. Artificial elbow joint classification using upper arm based on surface-EMG signal [C] . Jicheng Wang, Warit Wichakool IEEE International Conference on Engineering Technologies and Social Sciences . 2017

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Artificial elbow joint classification using upper arm based on surface-EMG signal

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