由于表面肌电信号的时域和频域特征值在手势分类识别中的准确性较低，提出了一种基于变分模态分解（variational mode decomposition, VMD）的方法对表面肌电信号进行分解，并结合粒子群优化支持向量机（particle swarm optimization-support vector machine, PSO-SVM）算法进行手势分类。首先对肌电信号进行预处理，采用变分模态分解（VMD）对信号进行多尺度分解，提取了近似熵、能量熵、置换熵、样本熵、奇异谱熵和谱熵特征。其次，利用相关性分析、随机森林和Lasso方法对提取的特征进行降维处理，以减少冗余信息。最后，基于粒子群优化支持向量机（PSO-SVM）算法对降维后的特征数据进行分类，实现高效且准确的手势识别。实验结果表明，所提出的手势识别方法识别六种手势动作时识别准确率达到了97.30%，具有较高的分类性能和较强的应用潜力。

Due to the limited accuracy of time-domain and frequency-domain features of surface electromyography (sEMG) signals in gesture classification, a novel method was proposed based on variational mode decomposition (VMD) for sEMG signal decomposition, combined with a particle swarm optimization-support vector machine (PSO-SVM) algorithm for gesture classification. First, the sEMG signals were preprocessed and decomposed into multiple scales using VMD. Features such as approximate entropy, energy entropy, permutation entropy, sample entropy, singular spectrum entropy, and spectral entropy were extracted. Next, dimensionality reduction was performed on the extracted features using correlation analysis, random forest, and Lasso methods to eliminate redundant information. Finally, the reduced feature set was classified using the PSO-SVM algorithm to achieve efficient and accurate gesture recognition. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 97.30% for six gesture types, showcasing its high classification performance and strong potential for practical applications.