基于sEMG的上肢关节角度预测方法研究  

Study on upper limb joint angle prediction method based on sEMG

作　　者：孔德智 王文东[1] 郭栋 史仪凯[1] KONG Dezhi;WANG Wendong;GUO Dong;SHI Yikai(School of Mechanical Engineering,Northwestern Polytechnical University,Xi′an 710072,China)

机构地区：[1]西北工业大学机电学院,陕西西安710072

出　　处：《西北工业大学学报》2022年第4期764-770,共7页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(51605385);陕西省自然科学基础研究计划(2020JM-131);广东省基础与应用基础研究基金(2019A1515111176)资助

摘　　要：针对康复训练过程人机交互性差、人机耦合不足的问题,提出上肢关节角度预测模型并完成实验验证。基于表面肌电信号(sEMG)获得可良好表征上肢运动意图的混合向量;完成信号的预处理、特征优化并提取时域特征值;针对当前运动控制领域模型预测精度不理想、预测速度较慢的问题,采用最小二乘支持向量机(LSSVM)方法实现上肢关节角度预测。实验结果证明提出的预测模型可根据表面肌电信号与姿态信息良好地预测人体上肢关节运动轨迹,有效减少预测时滞与误差,在提升人机耦合性方面具有一定的优越性。Aiming at the problems of insufficient human⁃computer interaction and human⁃machine coupling in the rehabilitation training process,a prediction model of upper limb joint angle is proposed and verified by experiments.Firstly,a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG;secondly,the signal preprocessing,feature optimization and extraction of temporal eigenvalues are completed;finally,for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control,the least square method(LSM)is adopted.The upper limb joint angle prediction is realized by multiplying the support vector machine(LSSVM)first.The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information,effectively reduce the prediction time delay and error,and has certain advanta⁃ges.

关 键 词：上肢外骨骼 表面肌电信号 最小二乘支持向量机 连续运动估计 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]