基于GA-RBF神经网络和sEMG的下肢动作识别方法研究

The Study of Lower Limb Motion Recognition Method Based on GA-RBF Neural Network and sEMG Signals

为了提高人体肌电信号对于下肢动作识别的准确率,提出一种基于遗传算法(GA)优化的径向基(RBF)神经网络分类模型。通过采集人体日常8种下肢动作的表面肌电信号并选择“sym6”系小波函数对肌电信号进行滤波预处理,使用主成分分析法(PCA)对时频域特征降维,把特征向量输入GA算法优化的RBF神经网络进行训练和识别。实验结果表明,该方法对同一受试者8种下肢动作的平均识别率为94.00%±0.45%;对15位不同受试下肢动作识别率达到89.30%,比传统BP神经网络的识别准确率提高11.8%,预测时间缩短6 s。所提出的方法为肌电信号应用于下肢智能康复机器人的意图识别研究提供参考,有助于病人的康复。

To improve the accuracy of human surface electromyography(sEMG)signals for the recognition of lower limb movements,an RBF neural network classification model based on genetic algorithm(GA)optimization was proposed in this work.The sEMG of eight kinds of daily lower limb movements was collected,and the‘sym6’wavelet function was selected for filtering preprocessing of sEMG.The principal component analysis(PCA)was used to reduce the dimension of time-frequency domain features,and the feature vectors were input into the RBF neural network optimized by GA for training and recognition.Experimental results showed that the average recognition rate of this method for the eight lower limb movements of the same subject was 94.00%±0.45%,and the recognition rate for the lower limb movements of 15 different subjects reached 89.3%,which was 11.8%higher and 6 s shorter than that of the traditional BP neural network.The proposed method displayed a high recognition accuracy in the application of using sEMG signals to recognize human lower limb movements,providing a reference for the study of intention recognition of lower limb intelligent rehabilitation robot and of assistance in the rehabilitation of patients with lower limb disabilities.