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The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a
Mazen Al Borno1, Jennifer L Hicks1, Scott L Delp1
1Department of Bioengineering and Mechanical Engineering, Stanford University, Stanford, CA, USA.
Journal of the Royal Society, Interface
|June 4, 2020
Summary
Muscle synergies, a simplified motor control strategy, enable efficient and adaptable human movement. This study demonstrates that muscle synergies allow for rapid learning and flexible execution of new motor tasks.
Area of Science:
- Biomechanics
- Neuroscience
- Motor Control
Background:
- The central nervous system may simplify movement production using muscle synergies.
- Recent research questions if low-dimensional controllers can achieve complex, flexible behaviors.
Purpose of the Study:
- Investigate if muscle synergies cause task deficits.
- Determine if synergies facilitate motor learning.
- Assess synergy generalization to novel movements.
Main Methods:
- Implemented muscle synergies in a human upper extremity biomechanical model.
- Derived synergies from dynamic optimizations for a reaching task.
- Compared independent muscle activation vs. synergy-based controllers.
Main Results:
- Synergy-based controllers showed minimal performance deficits (errors < 1 cm).
- Synergy controllers exhibited faster learning rates compared to full-dimensional controllers.
- Synergy controllers successfully performed new tasks with comparable errors.
Conclusions:
- Muscle synergies provide an efficient and effective control strategy for complex movements.
- Synergy-based control facilitates motor learning and adaptation.
- This low-dimensional approach supports flexible and generalized motor behaviors.
