基于肌电信号和极限学习机的下肢关节运动预测

为判断下肢障碍患者的运动意图，通过外骨骼进行康复训练，分析表面肌电信号与下肢关节运动的关系。 提取表面肌电信号的均方根(root mean square，RMS)、绝对值均值(mean absolute value，MAV)、波形长度(waveform length，WL)和方差(variance，VAR)作为特征输入信号，采用极限学习机(extreme learning machine，ELM)建立表面 肌电信号与下肢关节角度之间的映射关系；对输出结果进行优化滤波以降低模型的误差，实现对下肢膝关节角度连 续变化的预测。与传统的反向传播(back propagation，BP)神经网络、径向基神经网络预测结果进行对比，结果证明： 极限学习机在通过表面肌电信号预测下肢关节角度变化中有更高的精度。

Prediction of Lower Limb Joint Motion Based on Surface EMG Signal and Extreme Learning Machine

In order to judge the motor intention of patients with lower limb disorders, the relationship between surface electromyography (SEMG) signal and lower limb joint movement was analyzed through rehabilitation training of exoskeleton. The root mean square (RMS), mean absolute value (MAV), waveform length (WL) and variance (VAR) of SEMG signal are extracted as feature input signals, and the mapping relationship between SEMG signal and lower limb joint angle is established by using extreme learning machine (ELM); the output results are optimized and filtered to reduce the error of the model, and the continuous change of lower limb knee angle is predicted. Compared with the traditional back-propagation neural network and radial basis function neural network, the results show that the extreme learning machine has higher accuracy in predicting the change of lower limb joint angle through SEMG signal.