%0 Journal Article
%T 利用表面肌电信号的下肢动态关节力矩预测模型<br>Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG
%A 陈江城
%A 张小栋
%A 李睿
%A 石强勇
%A 王贺
%J 西安交通大学学报
%D 2015
%R 10.7652/xjtuxb201512005
%X 为实现表面肌电信号的下肢关节力矩动态解码,建立了从表面肌电信号到关节力矩输出的人体下肢运动系统正向生物力学模型。首先,从幅值和频率两个角度建立表面肌电信号到骨骼肌激活程度模型;其次,根据肌丝滑移理论,构建反映骨骼肌生理结构和微观力学特性的肌肉力模型,同时确定活动肌肉拉力线方向及力作用点位移矢量,将骨骼肌力转换到关节力矩;最后,以牛顿？才防？逆动力学方法获得关节力矩作为准确值,给出正向生物力学模型参数动态标定方法。在模型基础上,对4名对象进行随意步态下膝关节屈伸动态力矩预测试验,结果表明:所建模型对步态行走下的膝关节动态关节力矩具有很好的动态跟踪性能,最大绝对误差为(11.0±1.32) N？m,平均残差为(4.43±0.698) N？m,预测值与准确值之间的平均线性相关系数为0.927±0.042,验证了该方法的正确性和有效性;可为康复训练机器人人机协同过程中的力学交互模式研究提供接口。<br>To achieve the dynamic joint torque decoding from surface electromyography (EMG), the forward biomechanical model of lower limb motor system, which relates the surface EMG and joint torque, is established. The dynamic surface EMG to skeletal muscle activation model is constructed from the perspective of amplitude and frequency. Then the muscle contraction model reflecting physiological structure and micromechanical properties is constructed according to the sliding？？filament theory. The force direction and displacement vector of active muscle are determined and the transformation from muscle force to joint moment is realized. The dynamic calibration for the forward biomechanical model using the exact joint torque value obtained with Newton？？Eular method is finally put forward. Following the calibration, the flexion/extension (FE) knee joint torque of four objects under different speed walking is predicted. The results show that the forward biomechanical model can capture the general shape and timing of the joint torque, the maximum absolute error is (11.0±1.32) N？m, the mean residual error is (4.43±0.698) N？m, and the linear relationship between predicted and exact knee FE torque reaches 0.927±0.042. This prediction model provides an interface for the study of force interaction pattern in the process of human？？machine cooperation in training
%K 表面肌电信号
%K 关节力矩预测
%K 肌肉模型
%K 正向生物力学&lt
%K br&gt
%K surface EMG
%K joint torque prediction
%K muscle model
%K forward biomechanics
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201512005