SUMMARY

Standing balance control is an essential daily-life task achieved by various neuromechanical mechanisms. The main question that has remained unanswered is the degree to which muscle activation pattern for a given movement is determined by biomechanical constraint or neural constraint. The goal of this study is to demonstrate the viability of low-dimensional control, using few set of muscle activation patterns, for standing balance task in a musculoskeletal model by characterizing the solution space and testing the feasibility in dynamic simulations.

I will use spatiotemporal muscle coordination in musculoskeletal model of the cat hindlimb as a paradigm to investigate how various costs, constraints and strategies affect the selection of appropriate motor solution for balance control. Aims of this study are to identify the degree to which solution space is constrained by biomechanics, examine how functional properties affect the selection of possible solutions, and investigate functional implications of given solutions at a task-level dynamic behavior of balance task.

Insights gained from this study can aid in interpreting variability in both measured and predicted muscle activity, or assessing confidence of predicted muscle activity as well as identifying possible variations when alternate cost functions or strategies are considered. Furthermore, it can be applied in designing muscle stimulation paradigms (FES), prosthetic devices, and bio-inspired control for robots.