Elman神经网络在表面肌电连续估计肘关节角度中的应用  

Application of Elman neural network in continuous estimation of elbow joint angle with sEMG

作　　者：刘永柏 王刚 柴媛媛 刘克平[1] 金龙 孙中波 LIU Yongbai;WANG Gang;CHAI Yuanyuan;LIU Keping;JIN Long;SUN Zhongbo(Department of Control Engineering,Changchun University of Technology,Changchun 130000,China;School of information Science and Engineering,Lanzhou University,Lanzhou 730000,China)

机构地区：[1]长春工业大学控制工程系,吉林长春130000 [2]兰州大学信息科学与工程学院,甘肃兰州730000

出　　处：《机器人外科学杂志（中英文）》2021年第4期295-305,共11页Chinese Journal of Robotic Surgery

基　　金：国家自然科学基金项目(6187330);中国博士后科学基金项目(2018M641784,2019T120240);吉林省科技发展计划项目(20200404208YY,20200201291JC)。

摘　　要：目的:估计肘关节角度和提高模型的速度和精度。方法:建立并研究基于表面肌电信号(Surface electromyogram,sEMG)的Elman神经网络(Elman neural network,ENN),通过在肱二头肌(Biceps muscle,BM)和肱三头肌(Triceps muscle,TM)的皮肤表面上放置电极来采集sEMG信号,并通过惯性测量单元(Inertial measurement unit,IMU)记录实际的肘关节角度。结果:通过实验结果以及基于模型阶数和隐层神经元数量的参数讨论,进一步证明了ENN可达到的最小均方根(Root mean square,RMS)误差为18.1899度。结论:在最优的参数下应用ENN估计肘关节角度时,均方根误差达到了可控范围。理论分析和实验结果都证明ENN在估计关节角度方面是有效的。Objective:To estimate the elbow joint angle and improve the rapidity and precision of the model.Methods:The elman neural network(ENN)based on surface electromyogram(sEMG)was established and investigated.The sEMG signals were collected by the electrodes placed on the skin surfaces of biceps muscle(BM)and triceps muscle(TM),and the actual elbow joint angle was recorded by an inertial measurement unit(IMU).Results:Theoretical analysis indicates that the ENN is feasible to be employed for estimating the elbow joint angle.Experimental results and the parameter discussion based on the model order and the number of hidden layer neurons further indicate that the minimum RMS error of ENN is 18.1899 degree.Conclusion:The RMS error is controllable when the ENN is used to estimate the elbow joint angle under the optimal parameter.Theoretical analysis and experimental results shows that the ENN is effective in estimation of joint angles.

关 键 词：表面肌电信号 ELMAN神经网络 均方根 意图识别 康复 

分 类 号：R496[医药卫生—康复医学]