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Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements.
Yongkun Zhao1,2, Mingquan Zhang3, Haijun Wu4
1Division of Human Mechanical Systems and Design, Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, Japan.
Bioengineering (Basel, Switzerland)
|April 28, 2023
Summary
This study introduces a neuromechanics-based neural feedback controller for arm rehabilitation. The controller mimics human arm biomechanics, achieving natural movement trajectories and precise control with minimal muscle strain.
Area of Science:
- Neuromechanics
- Biomedical Engineering
- Rehabilitation Robotics
Background:
- Human arm movement arises from complex neuromechanical interactions.
- Effective neuro-rehabilitation requires controllers that account for musculoskeletal dynamics.
Purpose of the Study:
- To design and validate a neuromechanics-based neural feedback controller for arm reaching movements.
- To improve neuro-rehabilitation training by mimicking natural arm motion.
Main Methods:
- Constructed a musculoskeletal arm model based on human biomechanics.
- Developed a hybrid neural feedback controller simulating multifunctional arm areas.
- Validated controller performance via numerical simulations.
Main Results:
- Simulations showed a natural, bell-shaped movement trajectory.
- Achieved real-time tracking errors within 1 millimeter.
- Maintained stable, low muscle tensile force, preventing excessive excitation and strain.
Conclusions:
- The neuromechanics-based controller effectively replicates natural arm movements.
- This controller shows promise for safe and efficient neuro-rehabilitation.
- The approach minimizes muscle strain, enhancing patient comfort and recovery.
