A muscle synergy-based method to improve robot-assisted movements.

Area of Science:

• Robotics
• Neurorehabilitation
• Biomechanics

Background:

• Assistive robotic devices are increasingly used for motor re-learning in neurological impairments.
• Designing effective control strategies using muscle synergies for rehabilitation robots is an emerging research area.

Purpose of the Study:

• To propose a novel synergy-based objective function to evaluate the impact of varying robotic assistance levels on muscle synergies during a reaching task.
• To assess motor coordination and muscle activation patterns in healthy participants under different robotic assistance conditions.

Main Methods:

• Healthy participants performed a 2D reaching task with varying levels of robotic assistance while holding a weight.
• Electromyography (EMG) signals from seven muscles were recorded and decomposed into muscle synergies.
• Three reference synergies were defined, and three metrics were used to quantify variations in muscle activation patterns relative to these references.

Main Results:

• Muscle synergies were successfully decomposed and analyzed across different assistance levels.
• The proposed objective function demonstrated statistically significant differences in motor coordination across assistance levels.
• Individual responses to robotic assistance varied, indicating the need for personalized support.

Conclusions:

• The developed synergy-based objective function is a promising tool for assessing motor coordination in robotic-assisted rehabilitation.
• Personalized robotic assistance is essential, as a one-size-fits-all approach is not effective for all individuals.
• This study provides a foundation for human-in-the-loop optimization in future rehabilitation technologies.