Abstract
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.
Highlights
In the analysis of human motion, one of the most challenging issues is related to the possibility of estimating the joint kinematics during the execution of the continuous movement.The current research (Han et al, 2015; Li et al, 2017) on the continuous movement estimation of limbs mainly focuses on using various surface electromyography features to estimate the joint angle, and there are two ways to achieve it
The model was simplified into three components: contraction, series elasticity, and parallel elasticity. This is the first model to have successfully described the changes of muscle contraction and is gradually developed as a normal way of predicting the joint angle (Huang et al, 2020; Xi et al, 2021)
The experiment was conducted on five subjects, suggesting that the Hill model they designed could complete the finger flexion angle estimation
Summary
The current research (Han et al, 2015; Li et al, 2017) on the continuous movement estimation of limbs mainly focuses on using various surface electromyography (sEMG) features to estimate the joint angle, and there are two ways to achieve it. The model was simplified into three components: contraction, series elasticity, and parallel elasticity. This is the first model to have successfully described the changes of muscle contraction and is gradually developed as a normal way of predicting the joint angle (Huang et al, 2020; Xi et al, 2021). Cavallaro et al (2006) designed an upper limb exoskeleton robot system and used the improved Hill model to conduct continuous movement estimation, and they proposed a 28-channel signal acquisition instrument to obtain sEMG, realizing the continuous angle estimation of the upper limb. The experiment was conducted on five subjects, suggesting that the Hill model they designed could complete the finger flexion angle estimation
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