Posture Control Research of Pneumatic-hydraulic Upper Limb Rehabilitation Training System

2019 ◽  
Author(s):  
Wang Hongyan ◽  
Peng Di ◽  
Xiong Jingqi
Keyword(s):  
Upper Limb ◽  
Training System ◽  
Posture Control ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Control Research ◽  
Limb Rehabilitation

scholarly journals Clinical Effect of Virtual Reality Technology on Rehabilitation Training of Sports Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jing Chen
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Sports Injury ◽  
Training System ◽  
Average Score ◽  
Virtual Reality Technology ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Rehabilitation System ◽  
Limb Rehabilitation

In order to make most patients recover most of their limb functions after rehabilitation training, virtual reality technology is an emerging human-computer interaction technology, which uses the computer and the corresponding application software to build the virtual reality environment. Completing the training tasks in the virtual environment attracts the patients to conduct repeated training in the game and task-based training mode and gradually realizes the rehabilitation training goals. For the rehabilitation population with certain exercise ability, the kinematics of human upper limbs is mainly analyzed, and the virtual reality system based on HTC VIVE is developed. The feasibility and work efficiency of the upper limb rehabilitation training system were verified by experiments. Adult volunteers who are healthy and need rehabilitation training to participate in the experiment were recruited, and experimental data were recorded. The virtual reality upper limb rehabilitation system was a questionnaire. By extracting the motion data, the system application effect is analyzed and evaluated by the simulation diagram. Follow-up results of rehabilitation training showed that the average score of healthy subjects was more than 4 points and 3.8 points per question. Therefore, it is feasible to perform upper limb rehabilitation training using the HTC VIVE virtual reality rehabilitation system.

Fractional-order modeling and control of pneumatic-hydraulic upper limb rehabilitation training system1

2020 ◽  
Vol 39 (5) ◽  
pp. 7639-7651
Author(s):  
Hongyan Wang ◽  
Zhi Huang ◽  
Jinbo Lu
Keyword(s):  
Fractional Order ◽  
Mass Flow ◽  
Upper Limb ◽  
Control Method ◽  
Controller Design ◽  
Flow Equation ◽  
Training System ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation

In this paper, by replacing the integral mass flow equation to fractional-order mass flow equation, the fractional-order mathematical model of 2DOF pneumatic-hydraulic upper limb rehabilitation training system is established. A new 2DOF fractional-order fuzzy PID (FOFPID) controller is designed, to provides a new reference for improving the control accuracy of the pneumatic system. In the design of the controller, the weight parameters of the input terms are transformed into the weight parameters of the error, and the input, which are analyzed to improve the accuracy of the controller design. The parameters of the control system are determined by multi-objective particle swarm optimization. To prove the effectiveness of the proposed control method, the experimental research was carried out by building the experimental platform of pneumatic-hydraulic upper limb rehabilitation training system. The results show that the 2DOF FOFPID controller has better performance than other designed controllers under different working conditions.

Design of Upper Limb Rehabilitation Training System Combining BCI and AR Technology

2021 ◽  
Author(s):  
Yuan Yao ◽  
Banghua Yang ◽  
Xinxing Xia ◽  
Zan Peng ◽  
Shouwei Gao ◽  
...  
Keyword(s):  
Upper Limb ◽  
Training System ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation

Development of an EMG-ACC-Based Upper Limb Rehabilitation Training System

2017 ◽  
Vol 25 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Ling Liu ◽  
Xiang Chen ◽  
Zhiyuan Lu ◽  
Shuai Cao ◽  
De Wu ◽  
...  
Keyword(s):  
Upper Limb ◽  
Training System ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation

scholarly journals The design of a hemiplegic upper limb rehabilitation training system based on surface EMG signals

2018 ◽  
Vol 12 (1) ◽  
pp. JAMDSM0031-JAMDSM0031 ◽  
Author(s):  
Xiu-Feng ZHANG ◽  
Xia LI ◽  
Ji-Tao DAI ◽  
Guo-Xin PAN ◽  
Ning ZHANG ◽  
...  
Keyword(s):  
Upper Limb ◽  
Surface Emg ◽  
Training System ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation

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.

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