人体下肢行走关节连续运动表面肌电解码方法
Surface Electromyography Decoding for Continuous Movement of Human Lower Limb During Walking

为实现人体下肢步态动作的连续识别,提出了一种利用表面肌电信号进行下肢关节运动角度连续解码的方法。首先利用光学运动捕捉实现下肢关节运动角度的计算,然后采集下肢运动相关主力肌肉的表面肌电信号并提取其活动强度信息;在此基础上,基于受限玻尔兹曼机构建深度自动编码器(DAE),实现多路表面肌电信号强度时间序列的低维空间编码和最优特征提取;最后,利用BP神经网络建立特征量与关节矢状面运动角度之间的非线性回归模型。实验结果表明:该方法提取的信号特征信息优于传统的主量分析方法,采用提出的模型能够更精确地估计下肢关节连续运动角度,其估计值与真实值的均方误差较传统方法降低25％～35％。研究结果为人机交互接口技术的开发、实现下肢可穿戴智能装备的生物电连续控制、提高人机运动平稳性奠定了基础。
To estimate the continuous movement of human lower limb during walking, a regression model which relates the surface electromyography (EMG) and the movement variables of the lower limb joints is constructed. The joint movement angles of lower limb are calculated accurately based on optical motion capture system, then the surface EMG signals are sampled from the main muscles directly concerned with the lower limb motion; the muscle activities are extracted, and a deep auto？？encoder (DAE) network with restricted Boltzmann machines (RBM) is realized, by which the multi？？channel processed surface EMG signals are encoded in low dimensional space and the optimal features are extracted. The nonlinear model mapping the EMG features to sagittal surface movement angles is established with back propagation (BP) neural network. Extensive experiments indicate that the features extracted with the deep auto？？encoder (DAE) network are outperformed principal components analysis (PCA); the movement angles of lower limb joints can be estimated continuously and precisely with the regression models and the mean square error (MSE) between the estimated values and real values is reduced by 25％？？35％ compared with the traditional method. The proposed strategy is expected to develop human？？machine interaction interface technology for the achievement of continuous bioelectric control and the improvement of motion stability between human and machine, especially for lower limb wearable intelligent equipment