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Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical
1The Wallace H. Coulter Department of Biomedical Engineering, Emory University and the Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Plos Computational Biology
|April 19, 2012
Summary
Optimality principles explain motor control by predicting muscle activity and forces during balance tasks in cats. This framework successfully bridges task-level goals with detailed movement execution.
Area of Science:
- Neuroscience
- Biomechanics
- Motor Control
Background:
- Optimality principles offer a framework for motor control but struggle to predict detailed muscle activity in complex tasks.
- Generalizing these principles to the neuromechanical transformation from task goals to muscle execution remains challenging.
Purpose of the Study:
- To test if optimality principles can predict detailed muscle activity and ground reaction forces during an unrestrained balance task in cats.
- To investigate a unified optimization framework across multiple levels of motor control (muscles and limbs).
Main Methods:
- Utilized an anatomically-realistic musculoskeletal model in cats performing an unrestrained balance task.
- Applied optimality principles to predict center of mass forces and moments, minimizing control effort.
- Simultaneously resolved redundancy across muscles and limbs, comparing predictions to experimental data across various perturbations and postural configurations.
Main Results:
- Optimality principles accurately predicted detailed muscle activity and ground reaction forces, aligning with experimental measures.
- A common optimization framework successfully predicted numerous experimental conditions across different perturbations and postures.
- Incorporating muscle synergy constraints improved prediction accuracy, suggesting their role in neural control of balance.
Conclusions:
- Task-level optimality principles can effectively predict detailed motor execution in balance tasks, integrating hierarchical control.
- Muscle synergies may achieve similar kinetics to optimal solutions but with higher control effort.
- Findings support the use of hierarchical, task-level control mechanisms in automatic balance pathways.
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