Zhang Lei, Long Jingang, Zhao RongGang, Cao Haoyang, Zhang Kai
School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an, China.
Front Bioeng Biotechnol. 2022 Mar 1;9:771255. doi: 10.3389/fbioe.2021.771255. eCollection 2021.
The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time-frequency domain was proposed. As the degree of freedom (DOF) of the pronation-supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation-supination movements.
希尔肌肉模型可用于估计连续运动过程中的人体关节角度。然而，采用该模型需要了解许多参数，如肌纤维的长度和收缩速度，而这些参数容易因个体不同而变化，从而导致估计误差。本研究基于表面肌电图（sEMG）特征和人体运动角度建立了反向传播神经网络模型。首先，定义肌肉在关节旋转中的作用，然后将传感器放置在肌肉组织上获取sEMG，接着构建表面sEMG特征与关节角度之间的关系模型。由于单一肌电图特征无法很好地反映综合肌电信息，提出了一种结合时域、频域和时频域的特征提取方法。由于旋前 - 旋后运动的自由度（DOF）由几块肌肉控制，因此难以进行角度预测。提出了一种基于卡尔曼滤波器校正估计误差的方法来解决这一问题。设计了一个具有一个自由度的外骨骼机器人并将其投入跟踪实验。结果表明，所提出的模型能够提高连续旋前 - 旋后运动过程中关节角度的估计精度。
