A computational framework to quantify neuromechanical constraints in selecting functional muscle activation patterns
Sohn, Mark Hongchul
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Understanding possible variations in muscle activation patterns and its functional implications to movement control is crucial for rehabilitation. Inter-/intra-subject variability is often observed in muscle activity used for performing the same task in both healthy and impaired individuals. However, the extent to which muscle activation patterns can vary under specific neuromuscular conditions and differ in function are still not well understood. Current musculoskeletal modeling approaches using optimization techniques to identify a unique solution cannot adequately address such questions. Here I developed a novel computational framework using detailed musculoskeletal model to reveal the latitude the nervous system has in selecting muscle activation patterns for a given task regarding neuromechanical constraints. I focused on isometric hindlimb endpoint force generation task relevant to balance behavior in cats. By identifying the explicit bounds on activation of individual muscles defined by biomechanical constraints, I demonstrate ample range of feasible activation patterns that account for experimental variability. By investigating the possible neuromechanical bases of using the same muscle activation pattern across tasks, I demonstrate that demand for generalization can affect the selection of muscle activation pattern. By characterizing the landscape of the solution space with respect to multiple functional properties, I demonstrate a possible trade-off between effort and stability. This framework is a useful tool for understanding principles underlying functional or impaired movements. We may gain valuable insights to developing effective rehabilitation strategies and biologically-inspired control principles for robots.