Loss of balance leading to falls is a primary cause of injury and accidental death in older adults. This proposal articulates an innovative and quantitative framework for both investigating and understanding temporal and spatial features of muscle activation patterns for human balance control. The objective of the proposal is to develop and validate a quantitative model to predict spatiotemporal muscle activation patterns during postural control. Using engineering tools, we will integrate novel experimental methods and computer simulations to understand how feedback control of posture is executed by the nervous system. Our framework will demonstrate that complex, high-dimensional muscle coordination patterns for postural control can be explained by just a few parameters related to task-level variables. Advancing our ability to quantify and predict muscle coordination patterns is critical to achieving our long-term goal of using paired experimental measures and engineering models to predict the functional consequences of neuromotor impairments and interventional therapies.
In Specific Aim 1, we will identify muscle synergies and task variables governing spatial organization of muscle activity during multidirectional perturbations to standing posture. Our approach is to extract muscle synergies from experimental data using optimization and correlate the activation of each muscle synergy to the production of a task-related variable. We will use a musculoskeletal model of the leg to determine the structure of synergies required to produce the muscle activation patterns and task-variables measured experimentally.
In Specific Aim 2, we will identify the task- level feedback loops governing temporal organization of muscle activity during sagittal perturbations to standing posture. Our approach is to characterize the feedback relationships between center of mass motion and temporal muscle activation patterns experimentally, and then use a simple inverted pendulum model to test the feasibility of the feedback loops in generating appropriate temporal muscle activation patterns and center of mass kinematics. Our models of postural control will allow us to predict motor dysfunction resulting from changes in motor patterns. Our results will therefore allow us to develop quantitative diagnostic tools for balance and movement disorders and facilitate the design of effective interventional therapies, neural prostheses, and neural repair strategies for motor rehabilitation.
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