基于sEMG和LSTM的下肢连续运动估计

Estimation of Lower Limb Continuous Movements Based on sEMG and LSTM

针对下肢助力外骨骼的连续运动控制问题,提出了一种基于表面肌电信号(sEMG)与长短时记忆(LSTM)网络的连续运动估计方法.通过LSTM对肌电-运动的映射关系进行训练分析,基于奇异值分解特征值矩阵的误差算法获取主元分析(PCA)算法的主成分数量(降维维度),实现了对下肢三个关节在矢状面内的连续运动估计,且提高了连续运动估计的实时性.通过与传统网络支持向量机(SVM)、反向传播(BP)神经网络训练结果的对比分析,证明了LSTM网络在下肢连续运动预测中的优越性.

A scheme of continuous motion estimation based on surface electromyography(sEMG)and long-short-term memory(LSTM)network is proposed for the control of lower limb assisted exoskeleton.The mapping relationship between EMG and motion is trained and analyzed by LSTM.The number of principal components(dimensionality reduction)for principal component analysis(PCA)algorithm are obtained based on the error algorithm of singular value decomposition eigenvalue matrix.The continuous motion estimation of three lower limb joints in sagittal plane is realized,and the real-time performance of the continuous motion estimation is improved.In comparison of the training results of LSTM network with those of traditional networks such as support vector machine(SVM)and back propagation(BP)neural network,the superiority of LSTM network in continuous motion prediction of lower limbs is proved.