吴常铖,宋爱国,曾洪,李会军,徐宝国.基于sEMG和GRNN的手部输出力估计[J].仪器仪表学报,2017,38(1):97-104
基于sEMG和GRNN的手部输出力估计
Force estimation based on sEMG and GRNN
  
DOI:
中文关键词:  表面肌电信号  广义回归神经网络  手部输出力估计
英文关键词:surface electromyograph(sEMG)  generalized regression neural network (GRNN)  estimation of force outputted from hand
基金项目:国家自然科学基金(61325018,61663027)、江苏省科技支撑计划(BE2014132)项目资助
作者单位
吴常铖 1.东南大学仪器科学与工程学院南京210096; 2.南京航空航天大学自动化学院南京211106 
宋爱国 东南大学仪器科学与工程学院南京210096 
曾洪 东南大学仪器科学与工程学院南京210096 
李会军 东南大学仪器科学与工程学院南京210096 
徐宝国 东南大学仪器科学与工程学院南京210096 
AuthorInstitution
Wu Changcheng 1.School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;2.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 
Song Aiguo School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
Zeng Hong School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
Li Huijun School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
Xu Baoguo School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
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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(sEMG)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XF;将XF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 sEMG, the feature extraction of sEMG and the construction of GRNN are described. Then, the sEMG 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 sEMG 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 sEMG and GRNN.
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