Date(s) - 11/18/2013
10:30 am

Jennifer Jackson, PhD student

Computational models are valuable for developing and testing hypotheses that otherwise would be impossible to explore experimentally. They can also provide a theoretical framework to explain experimental observations. In the case of pathological gait, despite many studies reporting that the central nervous system (CNS) regulates angular momentum during walking, no simple control law currently exists to explain how the CNS makes walking efficient or even possible. Fewer studies have looked at how linear momentum is conserved during human locomotion, although recent findings indicate conservation occurs for various locomotion tasks. A computational model that uses basic momentum considerations to predict achievable, improved gait patterns for individuals with pathological gait could be a valuable tool to aid clinicians in making objective, highly effective treatment decisions.


To create a framework for predictive gait optimization, the main objectives are to: 1) Eliminate the pelvis residual loads and improve foot marker tracking by enhancing the residual elimination algorithm through marker weight, tracked acceleration curve, feedback gain, and select model joint and inertial parameter adjustments; 2) Develop a foot-ground contact model that matches all three forces, center of pressure location, and free moment for both feet using physics to model the foot-ground interactions; 3) Demonstrate that whole-body momentum variations for gait tasks cluster differently from one another and that these clusters can be viewed as “momentum signatures” for different gait patterns; and 4) Develop an optimization methodology to predict different subject-specific gait patterns using a subject-specific computational model that matches a specified momentum signature. Eliminating the residual loads makes the resulting motions dynamically consistent while closely tracking the foot markers. This is essential for use with the foot-ground contact model that frees up the motion of the foot, which was constrained using previous inverse dynamics methods. Although this method did not yield desired results, other methods utilizing implicit numerical integration may have success using the developed foot-ground contact model to predict new motions based on experimental data and whole-body angular and linear momentum principles, which may help identify where to focus rehabilitation efforts that are likely to produce the largest functional improvement for a particular patient.