Empirical and musculoskeletal modeling approaches to assess and reshape angular momenta during human locomotion

Daily ambulatory tasks are complex. As an example, locomotor transitions are transient movements that occur when an individual changes their direction and/or terrain and often require significant neuromotor adjustments to successfully navigate these tasks while remaining balanced. Individuals with pathology-specific changes to the neuro-musculoskeletal system may face additional barriers during these and other ambulatory tasks. It is essential to understand the mechanisms of balance regulation of healthy and fall-prone individuals during daily ambulation to prevent falls, improve performance, inform assistive technologies and enhance the quality of lives of specific individuals. However, a knowledge gaps exists in terms of how specific task-dependent factors and musculoskeletal pathologies affect balance regulation, and how balance can be optimized. In this research, empirical and musculoskeletal modeling studies were performed to investigate the angular momentum (i.e. dynamic balance) regulation of healthy and patient populations. First, we perform experimental studies that compare how task anticipation and complexity influence dynamic balance regulation of healthy individuals during locomotor transitions. We found that when given prior knowledge of the future task, individuals made style-dependent (crossover vs. sidestep) anticipatory adjustments before the transition to maintain dynamic balance. We also show that patients with developmental dysplasia of hip and femoroacetabular impingement syndrome, experienced poor dynamic balance regulation in the sagittal plane during stair ambulation, which is strongly correlated with the numeric pain they experience. Finally, we show that patients with Parkinson’s disease exhibit impaired dynamic balance regulation in stair descent ambulation with increased whole-body and lower-limb angular momentum compared to able-bodied individuals. In a second series of studies, we developed and tested a predictive musculoskeletal simulation framework to explore and guide how specific individuals can improve their performance of locomotor transitions and straightline walking. We show in these studies that altered sequencing and magnitude of joint kinematics, as well as muscle mechanical power were required to optimally reshape (i.e., improve the regulation of or regulate in a predefined profile) dynamic balance of locomotor transitions. We also show that during straight-line walking, optimizing various biomechanically-meaningful performance objectives related to metabolic energy cost, muscle effort, dynamic balance and lower-limb mechanical loading elicit observable changes in walking mechanics. Specifically, decreased hip abduction angle and delayed phase of joint kinematics were associated with simulations that regulated dynamic balance, while increased hip adduction, abduction and subtalar moments were related to simulations that minimized metabolic cost. In all simulations, we show that balance, energy and loading can be optimized in isolation or in combination and result in unique observable changes in walking mechanics. This research provides new insights into the dynamic balance regulation in healthy individuals and patient populations during walking, as well as the mechanics that could be used to improve performance, guide the development of rehabilitation training and assistive device interventions that target specific objectives for specific individuals, during specific tasks.