The underlying principles of how the nervous system selects specific muscle activation pattern, among many that produce the same movement, remain unknown. Experimental studies suggest that the nervous system may use fixed groups of muscles, referred to as muscle synergies, to produce functional motor outputs relevant to the task. In contrast, predictions from biomechanical models suggest that minimizing muscular effort may be the criteria how a muscle coordination pattern is organized for muscle synergies. However, both experimental and modeling evidence shows that stability, as well as energetic efficiency, also needs to be considered.
Based on the hypothesis that the nervous system uses functionally stable muscle activation pattern for a muscle synergy, we investigated the stability of muscle patterns using a neuromechanical model of the cat hindlimb. Five unique muscle patterns that generate each of the five experimentally-identified muscle synergy force vectors at the endpoint were found using a minimum-effort criterion. We subjected the model to various perturbed conditions and evaluated functional stability of each of the five minimum-effort muscle synergies using a set of empirical criteria derived from experimental observations.
Results show that minimum-effort muscle synergies can be functionally stable or unstable, suggesting that minimum-effort criterion is not always sufficient to predict physiologically relevant postural muscle synergies. Also, linearized system characteristics can robustly predict the behavior exhibited by fully dynamic and nonlinear biomechanical simulations. We conclude that functional stability, which assesses stability of a biomechanical system in a physiological context, must be considered when choosing a muscle activation pattern for a given motor task.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/42869 |
| Date | 18 November 2011 |
| Creators | Sohn, Mark Hongchul |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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