Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot.
Yang Hu1,2,3, Jingyan Meng1,2,3, Guoning Li2,3
1School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|April 28, 2023
Summary
This study introduces a novel fuzzy adaptive passive (FAP) control strategy for robot-assisted rehabilitation. The FAP controller enhances patient initiative and motor learning by adaptively adjusting assistance force based on performance and impulse, improving stroke recovery outcomes.
Area of Science:
- Robotics
- Rehabilitation Engineering
- Neuroscience
Background:
- Robot-assisted therapy improves upper-limb function in stroke survivors.
- Current controllers often provide excessive assistance and neglect patient interaction, hindering motor intention assessment and initiative.
- This limits rehabilitation effectiveness and motor learning.
Purpose of the Study:
- To propose a fuzzy adaptive passive (FAP) control strategy for robot-assisted rehabilitation.
- To enhance patient initiative and motor learning during therapy.
- To improve the assessment of motor intention and rehabilitation outcomes.
Main Methods:
- Designed a passive controller using potential fields for safe movement guidance.
- Developed a fuzzy logic-based evaluation algorithm using task performance and impulse.
- Implemented adaptive modification of the potential field's stiffness coefficient to adjust assistance force.
Main Results:
- The FAP control strategy improved patient initiative during training.
- Ensured subject safety throughout the rehabilitation process.
- Enhanced the subjects' motor learning ability.
Conclusions:
- The FAP control strategy offers a promising approach for more effective robot-assisted rehabilitation.
- Adaptive assistance based on performance and impulse can stimulate patient initiative.
- This method improves motor learning and rehabilitation outcomes for stroke patients.
