摘要
作为与人体运动密切相关的生理信号,表面肌电(surface electromyography, sEMG)信号的解析在人机交互领域具有重要的作用。针对肌电信号分类效率和精度难以兼顾的问题,提出了一种特征筛选与分类器超参数优化相结合的上肢sEMG分类方法,该方法采用二进制粒子群优化(binary particle swarm optimization, BPSO)算法对特征进行筛选后,进一步采用粒子群优化(particle swarm optimization, PSO)算法调整最小二乘支持向量机(least squares support vector machine, LSSVM)的超参数。通过采集人上体4个部位的表面肌电信号并提取其中48维特征,对上肢常见的4种动作进行分类实验,结果表明,BPSO-PSO-LSSVM算法仅保留肌电数据的21维特征,得到的平均分类准确率达到97.54%,证明该方法可以有效筛选出用于上肢动作分类的最佳特征组合,并且提高运动分类的准确率。
sEMG(surface electromyography)signals are physiological signal closely related to human movement,and the analysis of sEMG signals play an important role in the field of human-machine interaction.Aiming at the difficulty of both efficiency and accuracy of electromyographic signal classification,an upper limb sEMG classification method was innovatively proposed,which combined feature screening with classifier hyperparameter optimization.BPSO(binary particle swarm optimization)algorithm was adopted to screen the features.PSO(particle swarm optimization)algorithm was further utilized to adjust the hyperparameters of the LSSVM(least-squares support vector machine).By collecting sEMG signals from four parts of the human upper body and extracting 48-dimensional features from them,classification experiments were conducted on four common movements of upper limb.The results show that the BPSO-PSO-LSSVM algorithm retains only the 21-dimensional features of the EMG data,and the average classification accuracy obtained reaches 97.54%.It is proved that this method can effectively screen out the optimal combination of features for upper limb motion classification and improve the accuracy of movement classification.
作者
贠今天
苗冠
李帅
耿梓敬
YUN Jin-tian;MIAO Guan;LI Shuai;GENG Zi-jing(School of Mechanical Engineering,Tiangong University,Tianjin 300387,China)
出处
《科学技术与工程》
北大核心
2025年第18期7686-7692,共7页
Science Technology and Engineering
基金
国家自然科学基金(51975409)。
关键词
表面肌电信号
特征选择
二进制粒子群优化
粒子群优化
动作分类
最小二乘支持向量机
sEMG(surface electromyography)signal
feature selection
BPSO(binary particle swarm optimization)
PSO(particle swarm optimization)
motion classification
LSSVM(least squares support vector machine)
