Scalable Biomechanical Modeling of Joint and Muscle Forces in the Study of Knee Osteoarthritis

My research is focused on developing an improved understanding of the natural variability in human gait patterns, or the manner in which we walk, as a basis for which to compare changes from normal due to joint injury or pathology. The coordinated use of our muscles, the movement patterns of the joints in our lower limbs (ankles, knees, hips), and the forces that propagate throughout our joints as we walk vary naturally from person to person. We now have the technology for very sophisticated laboratories that allows us to capture and record all of these aspects over time as we walk. This results in a large set of time-varying and correlated data meant to represent our individual walking pattern. Despite a significant amount of applied research reporting many differences in these variables with injury and disease, there remains a poor understanding of the relationships among and within these time-varying measurements even in healthy individuals, and therefore a limited ability to understand walking patterns that might be at risk for injury or disease. The proposed research combines sophisticated biomechanical and statistical modeling techniques to identify features of healthy walking patterns that describe relationships between joint movement, forces and muscle activity within each joint (hip, knee and ankle), and between our joints to provide a more complete description of lower limb biomechanics during walking. The research will explore age-related changes in healthy walking mechanics, and natural groupings of individuals based on their walking mechanics, as different walking strategies may have implications for future injury or disease. The forces that propagate between the surfaces of our joints as we walk are difficult to calculate, but can contribute to injury and disease development. This research will also explore how joint movement, muscle use and summary force measurements contribute to joint forces during walking, and provide a more efficient method for representing these forces for use in translational gait analysis applications. The proposed research will significantly improve our understanding of the person-to-person variability in walking patterns, and how this may contribute to differences in the mechanical environment of lower limb joints during walking. Understanding the relationships between the joint-level variables used to describe walking patterns in a large healthy population will provide important knowledge of uninjured, non-diseased walking mechanics, and a normative basis for gait analysis applications. The outcome of this research will be used to examine and understand walking pattern deviations in applications ranging from clinical treatment and diagnosis, preventative musculoskeletal health initiatives, and industry/occupational design and assessments.