scholarly journals Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training

2018 ◽  
Vol 9 ◽  
Author(s):  
Qingcong Wu ◽  
Xingsong Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Active Control ◽  
Upper Limb ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Powered Exoskeleton ◽  
Limb Rehabilitation

scholarly journals Pilot Study of a Powered Exoskeleton for Upper Limb Rehabilitation Based on the Wheelchair

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Qiaoling Meng ◽  
Qiaolian Xie ◽  
Haicun Shao ◽  
Wujing Cao ◽  
Feng Wang ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Upper Limb ◽  
Joint Angle ◽  
Maximum Error ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Flexion Extension ◽  
Powered Exoskeleton ◽  
Limb Rehabilitation ◽  
Flexion And Extension

To help hemiplegic patients with stroke to restore impaired or lost upper extremity functionalities efficiently, the design of upper limb rehabilitation robotics which can substitute human practice becomes more important. The aim of this work is to propose a powered exoskeleton for upper limb rehabilitation based on a wheelchair in order to increase the frequency of training and reduce the preparing time per training. This paper firstly analyzes the range of motion (ROM) of the flexion/extension, adduction/abduction, and internal/external of the shoulder joint, the flexion/extension of the elbow joint, the pronation/supination of the forearm, the flexion/extension and ulnar/radial of the wrist joint by measuring the normal people who are sitting on a wheelchair. Then, a six-degree-of-freedom exoskeleton based on a wheelchair is designed according to the defined range of motion. The kinematics model and workspace are analyzed to understand the position of the exoskeleton. In the end, the test of ROM of each joint has been done. The maximum error of measured and desired shoulder flexion and extension joint angle is 14.98%. The maximum error of measured and desired elbow flexion and extension joint angle is 14.56%. It is acceptable for rehabilitation training. Meanwhile, the movement of drinking water can be realized in accordance with the range of motion. It demonstrates that the proposed upper limb exoskeleton can also assist people with upper limb disorder to deal with activities of daily living. The feasibility of the proposed powered exoskeleton for upper limb rehabilitation training and function compensating based on a wheelchair is proved.

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 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.

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.

scholarly journals Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

2019 ◽  
Vol 9 (8) ◽  
pp. 1620 ◽  
Author(s):  
Bai ◽  
Song ◽  
Li
Keyword(s):  
Upper Limb ◽  
Motion Tracking ◽  
Stroke Patients ◽  
Mean Velocity ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Post Stroke ◽  
Reachable Workspace ◽  
Rehabilitation System ◽  
Limb Rehabilitation

In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

Sign in / Sign up

Export Citation Format

Share Document