SUBJECT: Ph.D. Dissertation Defense
BY: Hongchul Sohn
TIME: Monday, March 30, 2015, 12:30 p.m.
PLACE: MRDC Building, 4211
TITLE: A Computational Framework to Quantify Neuromechanical Constraints in Selecting Functional Muscle Activation Patterns
COMMITTEE: Dr. Lena Ting, Chair (ME)
Dr. Thomas J. Burkholder (AP)
Dr. Magnus Egerstedt (ECE)
Dr. David L. Hu (ME)
Dr. Jun Ueda (ME)


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 cannot adequately address such questions because it focuses on identifying a unique optimal solution, among many possible that could produce the same movement. We currently lack tools to explore and characterize the range of possible muscle activation patterns for a given task and to assess the functional implications of such variations. 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 various 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 of the limb and the task, I demonstrate an ample range of feasible activation patterns which may account for experimental variability. By investigating the possible biomechanical and neural bases of using the same muscle activation pattern to perform tasks across postures, I demonstrate that demand for generalization of function can affect the selection of muscle activation pattern, which is not granted by limb biomechanics nor optimality. By characterizing the landscape of the solution space with respect to multiple functional properties, I demonstrate a possible trade-off between effort and stability in neural selection. This framework can be a useful tool for understanding principles underlying functional or impaired movements. We may gain valuable insights to explaining individual differences in healthy and impaired individuals, which is crucial for developing effective patient-specific treatments as well as novel biologically-inspired control principles for rehabilitation robots.