摘要
针对表面肌电(sEMG)信号与上肢运动方向之间的映射关系,提出一种基于肌电力映射矩阵的上肢运动方向预测模型。通过实验进行受试者肌电信号的采集,分析上肢在不同姿态下的力方向识别算法的准确性。实验采用9块浅层肌肉的肌电信号作为输入,运用小波滤波和均方根值(RMS)处理信号,并构建了肌电力方向映射矩阵(SFMM)和末端运动方向映射矩阵(EDMM)。通过伪逆法和反向传播神经网络(BPNN)进行模型训练,对比使用原始数据、处理后的数据以及结合位姿变换矩阵的数据这3种方法在单独姿态和混合姿态下的预测能力。研究结果显示,结合位姿变换矩阵的方法在各种姿态下均表现出较高的预测准确度,可有效减小上肢姿态变化对预测结果的影响。本研究为基于sEMG的外骨骼设备力方向预测提供了理论基础。
A prediction model for upper limb motion direction based on the electromyography force mapping matrix was proposed to map the relationship between surface-electromyography(sEMG)signals and the direction of upper limb motion.Electromyography signals of the subjects were collected through experiments,and the accuracy of the force direction recognition algorithm for the upper limb in different postures was analyzed.Electromyography signals of nine superficial muscles were used as input,with wavelet filtering and root mean square(RMS)applied to process the signals.Additionally,the sEMG-force direction mapping matrix(SFMM)and the end motion direction mapping matrix(EDMM)were constructed.The model was trained by pseudo-inverse method and back propagation neural network(BPNN).The prediction performance of directly using the original data,the processed data,and the data combined with the posture transformation matrix in single posture and mixed posture was compared through experiments.The research results show that the method combined with the posture transformation matrix shows high accuracy in various postures,which can effectively reduce the influence of upper limb posture changes on the prediction results.This study provides a theoretical basis for the force direction prediction of exoskeleton devices based on sEMG.
作者
王林
李炜煌
唐鸿雁
WANG Lin;LI Weihuang;TANG Hongyan(Nanjing Technology and Innovation Research Institute,The Hong Kong Polytechnic University,Nanjing 210036,China;Institute of Intelligent Rehabilitation Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《上海理工大学学报》
北大核心
2025年第1期19-29,共11页
Journal of University of Shanghai For Science and Technology
基金
国家自然科学基金资助项目(62403320)
上海市科学技术委员会资助项目(23YF1429900)。
关键词
表面肌电信号
运动意图识别
神经网络
多姿态
映射矩阵
surface-electromyographic
motion intention recognition
neural network
multiple postures
mapping matrix
作者简介
第一作者:王林(1994-),男,副研究员.研究方向:并联机器人、康复机器人.E-mail:lin1wang@polyu.edu.hk;通信作者:唐鸿雁(1992-),男,讲师.研究方向:康复机器人、智能机器人.E-mail:hytang@usst.edu.cn。
