Many disabilities significantly disrupt walking, including neurological, muscular or orthopedic disorders, and normal aging. For example, as many as 12 million elderly over age 65 and 60% of lower extremity amputees fall each year. The total costs of all fall-related injuries could reach $43.8 billion by 2020. Identifying those at greatest risk of falling so proper interventions can be applied is critical to reducing these numbers. Most falls occur while people are walking. Therefore, the goal of this project is to develop appropriate tools to quantify dynamic stability during walking so we can solve this momentous clinical problem. In mechanics, stability is defined by how a system responds to perturbations. Global Stability defines the set of all perturbations a system can respond to without """"""""falling over"""""""". Global stability in humans can be tested by imposing large perturbations like slips or trips. Local Stability defines how a system responds to very small perturbations. Our lab has developed novel approaches to quantifying local dynamic stability of walking and used these to validate several intuitive clinical observations regarding strategies patients use to maintain local stability during normal (i.e., unperturbed) walking. Our ultimate goal is to develop valid methods to predict falls without having to directly cause falls. Doing this will require determining if and how local stability is related to global stability. This is a very difficult problem because there is no theoretical guarantee that local stability will predict global stability and because the precise mathematical definitions of these quantities, derived for deterministic systems, are not easily applied to noisy biological systems. For this Exploratory / Developmental R21 project, we will first derive and validate a novel set of quantitative measures of dynamic stability that specifically account for stochastic """"""""pseudo-periodic"""""""" motions and are thus appropriate for analyzing human walking data. Second, we will validate our stability measures using a novel biomechanical model designed specifically to analyze walking stability. Our dynamic walking model will incorporate sufficient muscle activation for forward propulsion, and bio-mimetic state feedback control with neuronal noise and physiological time delays to ensure lateral stability. We will conduct similar experiments in both the model and in healthy humans to determine how small-to-moderate perturbations affect local walking stability and how large perturbations affect global walking stability. Together, these efforts will tell us if appropriately defined measures of local stability, obtained during unperturbed or minimally perturbed walking, can predict actual risk of falling when our model and/or human subjects experience large perturbations. If so, the tools developed in this project could potentially significantly improve our ability to predict, and thereby prevent, falls in patients with locomotor disorders. These tools will also provide a coherent platform for determining the biomechanical and neurophysiological mechanisms humans use to prevent falls and for evaluating the efficacy of different therapeutic interventions intended to help augment these mechanisms.
Falls and the injuries that result from falls are a significant health care problem for the elderly and for patients with a wide range of walking disorders, including stroke, amputation, and many others. Finding ways of accurately predicting and preventing these falls will significantly extend and improve the lives of these patients. The proposed work will apply novel engineering concepts to directly quantify dynamic stability during walking to address this critical issue.
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