System design of upper limb sEMG real-time control manipulator

2019 ◽  
Author(s):  
Cunfang Zheng ◽  
Lixin Ren ◽  
Xin Guo ◽  
Yiming Chen ◽  
Wang Ma ◽  
...  
Keyword(s):  
System Design ◽  
Real Time ◽  
Upper Limb ◽  
Real Time Control ◽  
Time Control

ElectroMyoGram Pattern Recognition for Real-Time Control of Upper Limb Prosthesis

2016 ◽  
Vol 6 (8) ◽  
pp. 1872-1880 ◽  
Author(s):  
Enas Abdulhay ◽  
Ruba Khnouf ◽  
Abeer Bakeir ◽  
Razan Al-Asasfeh ◽  
Heba Khader
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Real Time Control ◽  
Time Control ◽  
Upper Limb Prosthesis ◽  
Limb Prosthesis

scholarly journals Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang ◽  
Xiao Zhang
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Robotic Arm ◽  
Myoelectric Signal ◽  
Real Time Control ◽  
Myoelectric Signals ◽  
Upper Limb Rehabilitation ◽  
Time Control ◽  
Offline Testing ◽  
Limb Rehabilitation

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p  = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

Real-time control system design for complex controlled plants

1995 ◽  
Author(s):  
N Coleman ◽  
A Paz ◽  
M DeVito ◽  
G Papanagopoulos ◽  
T-JLin, Yu, C-F
Keyword(s):  
Control System ◽  
System Design ◽  
Real Time ◽  
Control System Design ◽  
Real Time Control ◽  
Time Control
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