Wrist angle prediction under different loads based on GA‐ELM neural network and surface electromyography

Abstract

In sEMG (surface electromyography) pattern recognition, most of the research focuses on the static pattern recognition of different limbs, ignoring the importance of changing load intensity, and joint angle movement information. Traditional static qualitative pattern recognition cannot adjust the motion amplitude and load intensity, so it is of great significance to study the continuous prediction of wrist angle under different load intensities. Based on the correlation between the surface EMG signal and the joint angle signal, the article is based on the neural network to identify and predict the wrist angle under different loads continuously quantitatively. The sEMG signal in this article was collected with the approval and review of the Ethics Committee and the people's informed consent. Since qualitative pattern recognition cannot adjust the wrist movement range and the different load training intensity, the article establishes an angle prediction model based on a genetic algorithm to optimize the extreme learning machine (ELM). In addition, the article analyzes the influence of different loads on the continuous prediction accuracy of the wrist angle, realizes the continuous quantitative angle of the precise wrist prediction. Experimental analysis shows that the wrist joint angle predicted by the ELM optimized based on genetic algorithm is close to the actual angle, and the average error is about 5.96 degrees.

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

“…Now, more and more intelligent algorithms are used to manipulator control systems (Zhang et al, 2017;Lu, 2018;Jiang et al, 2019b;Huang et al, 2019;Nguyen et al, 2019;Huang et al, 2020;Ozyer, 2020;Sun and Liu, 2020;Liu et al, 2021c;Yu et al, 2021). Due to various factor interferences, such as the environment, the robot cannot be positioned accurately, and the positioning error will gradually increase (Sun et al, 2020b;Ozyer, 2020;Liu X. et al, 2021).…”

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

“…Good performance of ResNet on image recognition and localization tasks showed that characterisation depth is of central importance for many visual recognition tasks. In the following years, excellent feature extraction networks such as ResNeXt, DenseNet, RegNet, SEnet, SKNet, etc ( Xie et al, 2017 ; Liu X. et al, 2021 ; Liu et al, 2021b ; Liu et al, 2021c ; Liu et al, 2021d ; Sun et al, 2020c ; Sun et al, 2020d ). were successively proposed, constantly refreshing the accuracy rate of tasks such as image classification.…”

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

“…In this paper, the thruster distribution of the AUV is used as shown in Figure 1. The motion of the AUV is described using the ground and airframe coordinate systems shown in Figure . The conversion matrix from the AUV airframe coordinate system to the ground coordinate system is (Li et al, 2019c;Tan et al, 2020;Xiao et al, 2021;Liu et al, 2022c)…”

“…The conversion matrix from the AUV airframe coordinate system to the ground coordinate system is ( Li et al, 2019c ; Tan et al, 2020 ; Xiao et al, 2021 ; Liu et al, 2022c ) …”