Abstract:

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.

Key words: surface Electromyography（sEMG）, Non-negative Matrix Factorization（NMF）, Support Vector Machine（SVM）, characteristic matrix, pattern recognition

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。

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