Manipulation of Robot Hand Based on Motion Estimation Using EMG Signals
Manipulation of Robot Hand Based on Motion Estimation Using EMG Signals
复制标题
基于肌电信号运动估计的机械手操纵
DOI：
10.1299/kikaic.73.3024
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
T. Koizumi
中科院分区：
文献类型：
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作者：
T. Kitamura;N. Tsujiuchi;T. Koizumi
本研究旨在建构一个以肌电信号为基础的智能化人工手臂控制系统。利用多元回归方程和马氏距离对肌电信号进行信号处理，可以在短时间内学习参数。用这两种方法进行运动模式判别，并用多元回归方程预测关节角度。多元回归模型提供的精度不如通常使用的人工神经网络，但通过选择合适的输入和产生教师信号来提高精度。从过去的关节角度数据和提取每个运动的特征的信号预测关节角度。实验验证了该方法的有效性。区别的动作是抓握、张开和单手卡盘。预测的关节角度是对应于这三种运动的多指角度。首先，我们验证了这些运动是可区分的肌电信号，并进行运动模式的歧视。因此，这些动议受到歧视。第二，使用机器人手进行实验。在所有五个科目的机器人手控制直观和准确。实验结果证明了该方法对肌电信号处理的有效性。
The purpose of this research is the construction of an intelligence artificial arm control system which uses EMG signals. Signal processings of EMG signals are preformed using multiple regression equation and Mahalanobis' distance, which can learn parameters in a short time. The motion pattern discrimination is conducted with both technique and joint angles are predicted with multiple regression equation. A multiple regression model provides a less adequate accuracy than an artificial neural network generally used, but accuracy is improved by selection of suitable inputs and generation of teacher signals. Joint angles were predicted from joint angles data of the past and the signals that extracted characteristics of each motion. The experiments were conducted to verify the validity of this technique. Discriminated motions were grip, open and chuck at a hand. Predicted joint angles were multi-finger angles corresponding to these three motions. First, we verified these motions are discriminable from EMG signals, and the motion pattern discrimination was conducted. Consequently, these motions were discriminated. Second, the experiment using a robot hand was conducted. A robot hand was controlled intuitively and accurately in all five subjects. From these experiments, the usefulness of processing EMG signals with proposed methods were proved.