Finger Joint Angle Estimation based on sEMG signals and deep learning method

2021 ◽  
Author(s):  
Chenfei Ma ◽  
Weiyu Guo ◽  
Lisheng Xu ◽  
Guanglin Li
Keyword(s):  
Deep Learning ◽  
Joint Angle ◽  
Finger Joint ◽  
Learning Method ◽  
Angle Estimation ◽  
Semg Signals

Finger Joint Angle Estimation from Electrical Contact Resistance

2017 ◽  
Vol 100 (6) ◽  
pp. 35-44
Author(s):  
JUNKI KAWAGUCHI ◽  
SHUNSUKE YOSHIMOTO ◽  
MASATAKA IMURA ◽  
OSAMU OSHIRO
Keyword(s):  
Contact Resistance ◽  
Joint Angle ◽  
Electrical Contact ◽  
Finger Joint ◽  
Electrical Contact Resistance ◽  
Angle Estimation

Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach

2020 ◽  
Vol 61 ◽  
pp. 102024 ◽  
Author(s):  
Chenfei Ma ◽  
Chuang Lin ◽  
Oluwarotimi Williams Samuel ◽  
Lisheng Xu ◽  
Guanglin Li
Keyword(s):  
Deep Learning ◽  
Upper Limb ◽  
Joint Angle ◽  
Learning Approach ◽  
Continuous Estimation ◽  
Semg Signals

scholarly journals Finger joint angle estimation based on sEMG signals by Attention-MLP

2021 ◽  
Vol 2113 (1) ◽  
pp. 012081
Author(s):  
Zhebin Yu ◽  
Hui Wang ◽  
Wenlong Yu
Keyword(s):  
Joint Angle ◽  
Pearson Correlation ◽  
Estimation Method ◽  
Robotic Arm ◽  
Joint Angles ◽  
Angle Estimation ◽  
Interactive Field ◽  
Robotic Arm Control ◽  
Better Than ◽  
Semg Signals

Abstract 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.

A robot prosthetic finger system based on finger joint angle estimation using EMG signals

2013 ◽  
Vol 5 (6) ◽  
pp. 383 ◽  
Author(s):  
Nozomu Araki ◽  
Shintaro Nakatani ◽  
Kenji Inaya ◽  
Yasuo Konishi ◽  
Kunihiko Mabuchi
Keyword(s):  
Joint Angle ◽  
Finger Joint ◽  
Angle Estimation ◽  
Prosthetic Finger

scholarly journals Neural network committees for finger joint angle estimation from surface EMG signals

2009 ◽  
Vol 8 (1) ◽  
pp. 2 ◽  
Author(s):  
Nikhil A Shrirao ◽  
Narender P Reddy ◽  
Durga R Kosuri
Keyword(s):  
Neural Network ◽  
Joint Angle ◽  
Surface Emg ◽  
Finger Joint ◽  
Angle Estimation
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