School of Engineering, Qufu Normal University, Rizhao 276826, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2021 Sep 13;21(18):6147. doi: 10.3390/s21186147.
To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.
为了提高基于表面肌电信号（sEMG）的下肢动作识别率，提出了一种有效的加权特征方法，并设计了一种改进的遗传算法支持向量机（IGA-SVM）。首先，针对表面肌电特征提取过程中特征冗余度高、区分度低的问题，提出了基于肌肉与动作相关性的加权特征方法。其次，为了解决遗传算法选择算子易陷入局部最优解的问题，采用锦标赛排序法设计了改进的遗传算法-支持向量机。最后，将所提方法用于识别设计的 6 种下肢动作，平均识别率达到 94.75%。实验结果表明，该方法在下肢动作识别中具有一定的潜力。
