An intelligent control framework for robot-aided resistance training using hybrid system modeling and impedance...

Area of Science:

• Robotics
• Biomedical Engineering
• Rehabilitation Science

Background:

• Robot-assisted therapy offers potential for enhanced rehabilitation.
• Monitoring patient's muscle strength during training is crucial for effective therapy.
• Existing methods may lack adaptability to real-time physiological changes.

Purpose of the Study:

• To develop a novel therapy control method for robot-assisted resistance training.
• To create an intelligent control framework for adaptive resistive force generation and safety.
• To evaluate the method's effectiveness in enhancing muscle strength and its practicability.

Main Methods:

• Utilized hybrid system modeling technology and estimated patient's bio-impedance changes.
• Developed an intelligent control framework based on hybrid system theory.
• Online evaluation of muscle strength progress via bio-damping and bio-stiffness estimation.
• Verified the method with a custom therapeutic robot system and a Barrett WAM™ manipulator.

Main Results:

• The proposed method automatically generates desired resistive force based on bio-impedance.
• Accommodating emergency behavior was implemented for safety during training.
• Muscle strength progress was effectively evaluated online.
• Preliminary results demonstrated enhancement in impaired limb muscle strength.

Conclusions:

• The novel therapy control method is effective for robot-assisted resistance training.
• The approach enhances impaired limb muscle strength through adaptive therapy.
• The strategy shows significant practicability for robot-aided rehabilitation training.