Interactions between the neural and musculoskeletal systems are a prerequisite for the production of robust movement. In spite of this, the neural control and musculoskeletal structure underlying biological movements are typically studied independently, with little attention paid to how changes in one may affect the other. Understanding these interactions may be critical to improving current rehabilitation technologies and therapy methods. As an example, balance disorders are multifactorial in nature and identifying whether biomechanical or neural changes are the source of instability remains an unanswered question. I have used a combined experimental and modeling approach to understand neural and biomechanical interactions governing human balance control. I developed a simple four-bar linkage model with delayed feedback to investigate frontal-plane standing balance. Using methods from time-delay systems I present evidence from this model that biomechanical structure is important for behavioral function and show that neural control and biomechanical structure co-vary for stable human balance. Predictions from the model were tested experimentally to dissociate the effects of inertia and postural configuration on balance. In addition, I applied robust control methods to solve the difficult problem of comparing the relative performance between neuromechanical systems that differ in parameter values and predicted a common mechanism to explain changes in neural control across biomechanical contexts. In the future, the analytical tools and simulation methods I have developed can be generalized to investigate changes in neuromechanical interactions of various deficits in biomechanics (ACL rupture, amputation) and neural control (Parkinson's disease, stroke). Furthermore, this approach can be used to explain how neural control and biomechanical structure relate to the diversity of animal form and function, as well as suggest biomimetic control policies for robotics.

By understanding and mimicking characteristics of postural control used by animals, scientist and engineers may develop standing autonomous robots that work safely within home environments, and treatment strategies that help people overcome postural impairments. To increase our understanding of postural control we developed physical and computational models of standing posture to explain the interrelation of stance width and feedback gain in controlling the stability and dynamics of the postural response. These models facilitated precise analysis of mechanical dynamics and their effects on compliant feedback control, and provided a physical implementation to verify predictions developed from simulation. We show that a scaling of active feedback gain is required to maintain postural stability. These results are consistent with previous studies that have shown that a correlation exists between increased stance width and decreased postural responses. However, these studies have not quantified the relation between stance and the active control of standing posture. This scaling of gains that we show is dependent on the changing kinematic relations of the mechanical structure as it undergoes stance width adjustments. Specifically, we show that increasing stance width increases the leverage of the mechanical system. Feedback gains must be reduced by the reciprocal of the increase in mechanical leverage in order to maintain a consistent postural response; otherwise, the system may become unstable with increasing oscillations. We also showed that increasing magnitudes of intrinsic stiffness increases postural stability by facilitating stable responses over larger ranges of active feedback gain and increasing the stability of responses by decreasing settling time, oscillations, and displacement magnitude. The conclusions of this study were that the variation of mechanical leverage is responsible for changing the dynamics of the response during stance width variation, and that scaling of feedback gains with the changing mechanical leverage of stance width variations is required to maintain consistent response dynamics across stance widths.