利用健肢sEMG信号对康复机械腿进行映射控制

Mapping Control of Rehabilitation Mechanical Legs Using sEMG Signals of Healthy Limbs

研究了利用健肢表面肌电信号(sEMG)信号控制康复机械腿的问题。首先采集健全人在沿直线自然行走状态下,下肢八块肌肉的表面肌电信号和髋、膝、踝三个关节在矢状面内的关节角度信号,并对信号进行预处理、特征提取和标准化等操作;然后,建立了一个BP神经网络模型进行训练,将训练得到的神经网络模型用于关节角度的预测;最后以处理后的肌电信号作为模型输入,模型输出值经过移动均值滤波得到最终的关节角度预测值。再将此预测值作为机械腿的控制信号,实现机械腿的实时控制。实验结果显示,下肢三关节实际测量角度和预测值平均均方差(RMSE)为5.8295,平均皮尔逊相关系数(γ)为0.9312,可以较好实现机械腿的实时控制。

The problem of using the limb surface electromyography (sEMG) signal to control the rehabilitation mechanical leg was studied.Firstly,the surface EMG signals of the eight muscles of the lower limbs and the joint angle signals of the hip,knee and ankle joints in the sagittal plane were collected under the natural walking along the straight line,and the signal is preprocessed,characterized and standardized.Wait for the operation.Then,a BP neural network model is established for training,and the trained neural network model is used for the prediction of joint angle.Finally.the processed EMG signal is used as the model input,and the model output value is filtered by moving average to obtain the final joint angle prediction value.Then use this predicted value as the control signal of the mechanical leg to realize real-time control of the mechanical leg.The experimental results show that the average mean square error (RMSE) of the measured angles and predicted values of the lower limbs is 5.8295,and the average Pearson correlation coefficient (γ) is 0.9312,which can achieve real-time control of the mechanical legs.