A neural-network-based robust control strategy applying to omnidirectional lower limbs rehabilitation robot during centre-of-gravity shift

2009 ◽  
Author(s):  
Ying Jiang ◽  
Shuoyu Wang ◽  
Baodong Bai
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Control Strategy ◽  
Lower Limbs ◽  
Rehabilitation Robot ◽  
Centre Of Gravity

Study on the Lower Limbs Rehabilitation Robotic Control System by sEMG

2012 ◽  
Vol 462 ◽  
pp. 826-832
Author(s):  
Xiao Jun Zhang ◽  
Geng Qian Liu ◽  
Jian Hua Zhang ◽  
Yong Feng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Lower Limbs ◽  
Rehabilitation Robot ◽  
Robotic Control ◽  
Rehabilitation Robots ◽  
Fuzzy Neural ◽  
Neural Network Classifiers ◽  
And Control

With help training of the lower limbs rehabilitation robot, the hemiplegia patients can be helped effectively recover. Applicable control method plays an important part in performance of lower limbs rehabilitation robot. According to the preferred method, sEMG was collected from no necrosis and healthy muscle, then, the effective action signals which are extracted from the sEMG transit to Fuzzy-Neural network classifiers to identify the movements intention of paralyzed patients, and then the lower limbs rehabilitation robots can assist paralyzed patients to achieve their intent. The simulation results indicate that the Fuzzy-Neural network classifiers can identify the movements intention well, and control method of sEMG can satisfy the demand of lower limbs rehabilitation robot.

Sign in / Sign up

Export Citation Format

Share Document