Humans rely on cooperation from multiple sensorimotor processes to navigate a complex world. Poor function of one or more components can lead to reduced mobility or increased risk of falls, particularly with age. At present, quantification and characterization of poor postural control typically focus on single sensors rather than the ensemble and lack methods to consider the overall function of sensors, body dynamics, and actuators. To address this gap, I propose a controls framework based on simple mechanistic models to characterize and understand normative postural behavior. The models employ a minimal set of components that typify human behavior and make quantitative predictions to be tested against human data.

This framework is applied to four topics relevant to daily living:​ sensory integration for standing balance, limb coordination for one-legged balance, momentum usage in sit-to-stand maneuvers, and the energetic trade-offs of foot-to-ground clearance while walking. First, I demonstrate that integration of information from multiple physiological sensors can be modeled by an optimal state estimator. Second, I show that feedback control can model multi-limb coordination strategies during one-legged balance. Third, I use optimization models to demonstrate that momentum usage in sit-to-stand maneuvers employs excess work but serves to balance effort between knee and hip. Fourth, I propose a model for preferred ground clearance during swing phase of walking as a cost tradeoff between inadvertent ground contact and foot lifting, modulated by movement variability. These controls-based models demonstrate the mechanisms behind normative behavior and enables predictions under novel situations. Thus these models may serve as diagnostic tools to identify poor postural control or aid design of intervention procedures.