An sEMG-driven neuromusculoskeletal model of upper limb for rehabilitation robot control

2016 ◽  
Author(s):  
Liang Peng ◽  
Zeng-Guang Hou ◽  
Lincong Luo ◽  
Long Peng ◽  
Weiqun Wang ◽  
...  
Keyword(s):  
Upper Limb ◽  
Robot Control ◽  
Rehabilitation Robot ◽  
Neuromusculoskeletal Model

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Upper-limb Rehabilitation Robot Motion Control Based on Dynamic Interpolation

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU
Keyword(s):  
Motion Control ◽  
Upper Limb ◽  
Robot Motion ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Recent Patents on Exoskeletal Rehabilitation Robot for Upper Limb

2017 ◽  
Vol 10 (3) ◽  
Author(s):  
Jingang Jiang ◽  
Biao Huo ◽  
Xuefeng Ma ◽  
Yongde Zhang ◽  
Xiaoyang Yu ◽  
...  
Keyword(s):  
Upper Limb ◽  
Rehabilitation Robot

Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot

2020 ◽  
pp. 1-17
Author(s):  
Qing Sun ◽  
Shuai Guo ◽  
Leigang Zhang
Keyword(s):  
Upper Limb ◽  
Constraint Equation ◽  
Human Robot Interaction ◽  
Rehabilitation Robot ◽  
Connecting Rod ◽  
Robot Interaction ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Training Trajectory

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.

CONTROL OF FOREARM MODULE IN UPPER-LIMB REHABILITATION ROBOT FOR REDUCTION AND BIOMECHANICAL ASSESSMENT OF PRONATOR HYPERTONIA OF STROKE PATIENTS

2016 ◽  
Vol 16 (02) ◽  
pp. 1650008 ◽  
Author(s):  
PIN-CHENG KUNG ◽  
CHOU-CHING K. LIN ◽  
SHU-MIN CHEN ◽  
MING-SHAUNG JU
Keyword(s):  
Upper Limb ◽  
Position Control ◽  
Biomechanical Properties ◽  
Torque Control ◽  
Rehabilitation Robot ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Biomechanical Assessment ◽  
Custom Made ◽  
Limb Rehabilitation

Spastic hypertonia causes loss of range of motion (ROM) and contractures in patients with post-stroke hemiparesis. The pronation/supination of the forearm is an essential functional movement in daily activities. We developed a special module for a shoulder-elbow rehabilitation robot for the reduction and biomechanical assessment of pronator/supinator hypertonia of the forearm. The module consisted of a rotational drum driven by an AC servo motor and equipped with an encoder and a custom-made torque sensor. By properly switching the control algorithm between position control and torque control, a hybrid controller able to mimic a therapist’s manual stretching movements was designed. Nine stroke patients were recruited to validate the functions of the module. The results showed that the affected forearms had significant increases in the ROM after five cycles of stretching. Both the passive ROM and the average stiffness were highly correlated to the spasticity of the forearm flexor muscles as measured using the Modified Ashworth Scale (MAS). With the custom-made module and controller, this upper-limb rehabilitation robot may be able to aid physical therapists to reduce hypertonia and quantify biomechanical properties of the muscles for forearm rotation in stroke patients.

Control System Design of Upper Limb Rehabilitation Robot

2013 ◽  
Vol 310 ◽  
pp. 477-480 ◽  
Author(s):  
Gang Yu ◽  
Jin Wu Qian ◽  
Lin Yong Shen ◽  
Ya Nan Zhang
Keyword(s):  
Control System ◽  
Upper Limb ◽  
Impedance Control ◽  
Medical Personnel ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Simulation And Experiment ◽  
Limb Rehabilitation ◽  
Active Training

In traditional iatrical method, the patients with hemiplegia were assisted mainly by medical personnel to complete rehabilitation training. To make the medical personnel work easily and improve the effect of rehabilitation training, the rehabilitation robot was adopted. And the control system of a four DOF upper limb rehabilitation robot was designed based on impedance control to assist the patients with hemiplegia to complete rehabilitation training after the kinematic and kinetic analysis was finished. Then finished the analysis, simulation, and experiment of monarticular movement and multiarticulate movement after the analyzing the algorithm to tested the control system. The control system based on impedance control of the upper limb rehabilitation robot can realize the passive training which followed the planning trajectory, and active training which followed patients’ awareness of movement.

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