Open AccessFinger joint angle estimation based on sEMG signals by Attention-MLP
Author(s) -
Zhebin Yu,
Hui Wang,
Wenlong Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2113/1/012081
Subject(s) - mean squared error , joint (building) , artificial intelligence , pattern recognition (psychology) , computer science , multilayer perceptron , perceptron , grasp , pearson product moment correlation coefficient , gaussian , correlation coefficient , root mean square , mathematics , artificial neural network , speech recognition , statistics , machine learning , engineering , architectural engineering , physics , quantum mechanics , programming language , electrical engineering
sEMG(Surface electromyography) signal was widely applied in human-machine interactive field, especially in robotic arm control. In this paper, we built the Attention-MLP (Multilayer Perceptron) model to implement a type of continuous joint angle estimation method based on sEMG for six grasp movements, we tested this model on Ninapro dataset and the average Pearson correlation coefficient (CC) and the average root mean square error (RMSE) of the proposed Attention-MLP method achieved 0.812±0.02 and 10.51±1.98; the average CC and RMSE of this method are better than Sparse Pseudo-input Gaussian processes (SPGP), its average CC and RMSE are 12.14±2.30 and 0.727±0.07. Compared with the traditional method SPGP, our model performed better on continuously estimation of ten main hand joint angles under 6 grip movements.