中文摘要：

针对智能肌电假手力控制的需要,提出一种基于表面肌电信号（s EMG）和广义回归神经网络（GRNN）的手部输出力估计方法。首先在介绍实验平台的基础上详细描述了肌电信号的采集和特征提取方法以及广义回归神经网络的构建;然后,通过在手臂8个不同部位粘贴肌电传感器来检测手部动作过程中的肌电信号;同时为了全面测量人手在三维空间中的输出力,采用三维力传感器对手部的输出力进行测量;在同步获得手臂上的多通道肌电信号（X）和手部三维力推拉信号（F）后,对采集得到肌电信号进行了特征提取得到特征矩阵X_F;将X_F和F用于构建GRNN网络,并用均方差和残差绝对值均值对手部输出力的估计结果进行评估。为验证该方法的有效性,进行了实验验证,结果表明,该方法能够很好地利用sEMG对手部的输出力进行估计。

英文摘要：

A force estimation method based on the surface electromyograph（sEMG） and generalized regression neural network（ GRNN）is proposed for the demand of the force control of the intelligent EMG prosthetic hand. First,the experimental platform is introduced.The acquisition of the s EMG,the feature extraction of s EMG and the construction of GRNN are described. Then,the s EMG in the hand motions are detected by the EMG sensors with which eight different positions of arm skin surface are attached on. A three dimension force sensor is adopt to measure the force output by the human ＇s hand. The multi channels of the sEMG and the force are measured synchronously. Characteristic matrix of the s EMG and the force signal are used to construct the GRNN. The mean square error is employed to assess the accuracy of the estimated force. Experiments are implemented to verify the effectiveness of the proposed estimation method. The experimental results show that the force output by the human＇s hand can be estimated by the used of s EMG and GRNN.