Human-robot global Simulink modeling and analysis for an end-effector upper limb rehabilitation robot.
10.7507/1001-5515.201703070
- Author:
Yali LIU
1
;
Linhong JI
2
Author Information
1. Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, P.R.China.
2. Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, P.R.China.jilh@tsinghua.edu.cn.
- Publication Type:Journal Article
- Keywords:
end-effector upper limb rehabilitation robots;
human-robot global Simulink model;
planar movement;
training trajectory
- From:
Journal of Biomedical Engineering
2018;35(1):8-14
- CountryChina
- Language:Chinese
-
Abstract:
Robot rehabilitation has been a primary therapy method for the urgent rehabilitation demands of paralyzed patients after a stroke. The parameters in rehabilitation training such as the range of the training, which should be adjustable according to each participant's functional ability, are the key factors influencing the effectiveness of rehabilitation therapy. Therapists design rehabilitation projects based on the semiquantitative functional assessment scales and their experience. But these therapies based on therapists' experience cannot be implemented in robot rehabilitation therapy. This paper modeled the global human-robot by Simulink in order to analyze the relationship between the parameters in robot rehabilitation therapy and the patients' movement functional abilities. We compared the shoulder and elbow angles calculated by simulation with the angles recorded by motion capture system while the healthy subjects completed the simulated action. Results showed there was a remarkable correlation between the simulation data and the experiment data, which verified the validity of the human-robot global Simulink model. Besides, the relationship between the circle radius in the drawing tasks in robot rehabilitation training and the active movement degrees of shoulder as well as elbow was also matched by a linear, which also had a remarkable fitting coefficient. The matched linear can be a quantitative reference for the robot rehabilitation training parameters.
