基于NMF-SVM模型的上肢sEMG手势识别方法  

Upper Limb sEMG Gesture Recognition Method Based on NMF-SVM Model

作　　者：隋修武[1] 牛佳宝 李昊天 乔明敏 SUI Xiuwu;NIU Jiabao;LI Haotian;QIAO Mingmin(Tianjin Modern Electromechanical Equipment Technology Key Laboratory,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津工业大学天津市现代机电装备技术重点实验室,天津300387

出　　处：《计算机工程与应用》2020年第17期161-166,共6页Computer Engineering and Applications

基　　金：天津市应用基础与前沿技术项目(No.15JCYBJC19700)。

摘　　要：针对基于表面肌电信号进行动作识别的问题,按照不同的运动形态对应的各肌肉激活程度不同的思路,建立基于非负矩阵分解(NMF)与支持向量机(SVM)的联合模型,对从肌电信号中提取的特征值按照行表示肌肉类型,列表示特征值类型的规则组成规律性的特征矩阵,并对特征矩阵进行非负矩阵分解降维,降维得到的表征各肌肉激活程度的系数矩阵送入到支持向量机中训练并分类。基于非负矩阵分解与支持向量机联合模型与传统SVM模型相比,计算效率提高了一半,识别率提高了5.2%;通过样本分离实验表明,该算法依然有91.7%以上的识别率,验证了算法的有效性。A joint model based on Non-negative Matrix Factorization(NMF)and Support Vector Machine(SVM)is proposed for action recognition based on surface Electromyography(sEMG).The main idea of this model is the different activation degree of each muscle corresponding to different exercise patterns.An eigenmatrix with the row representing the muscle type and the column representing the eigenvalue type is constructed from data extracted from the sEMG.The coefficient matrix representing the activation degree of each muscle is obtained through the dimensionality reduction of non-negative matrix decomposition.Then the obtained coefficient matrix is transferred to support vector machine for training classification.Finally,compared with traditional SVM,the computational efficiency and recognition rate of the proposed joint model is improved by half and 5.2%respectively.Sample separation experiments show that the recognition rate of the algorithm is still more than 91.7%,which verifies the effectiveness of the algorithm.

关 键 词：表面肌电信号(sEMG) 非负矩阵分解(NMF) 支持向量机(SVM) 特征矩阵 模式识别 

分 类 号：TN911.72[电子电信—通信与信息系统]