Dynamic Stability in Human Walking: From Small to Large Perturbations

描述（由申请人提供）：许多残疾严重干扰行走，包括神经、肌肉或骨科疾病，以及正常的衰老。例如，每年有多达1200万65岁以上的老年人和60%的下肢截肢者摔倒。到2020年，所有与跌倒有关的伤害的总成本可能达到438亿美元。确定那些最容易跌倒的人，以便采取适当的干预措施，对于减少这些数字至关重要。大多数跌倒发生在人们走路的时候。因此，这个项目的目标是开发适当的工具来量化行走过程中的动态稳定性，这样我们就可以解决这个重大的临床问题。在力学中，稳定性是由系统对扰动的反应来定义的。全局稳定性定义了一个系统在不“倾覆”的情况下能够响应的所有扰动的集合。人类的整体稳定性可以通过施加像滑倒或绊倒这样的大扰动来测试。局部稳定性定义了系统对非常小的扰动的反应。我们的实验室已经开发出新的方法来量化步行的局部动态稳定性，并使用这些方法来验证一些直观的临床观察，这些观察涉及患者在正常（即不受干扰）行走期间用于维持局部稳定性的策略。我们的最终目标是开发有效的方法来预测跌倒，而不必直接导致跌倒。要做到这一点，就需要确定局部稳定是否与全球稳定相关，以及如何相关。这是一个非常困难的问题，因为没有理论保证局部稳定性将预测全局稳定性，因为这些量的精确数学定义，为确定性系统导出，不容易应用于嘈杂的生物系统。对于这个探索性/发展性R21项目，我们将首先推导并验证一套新的动态稳定性定量测量方法，该方法专门考虑随机“伪周期”运动，因此适用于分析人类行走数据。其次，我们将使用一种专门用于分析行走稳定性的新型生物力学模型来验证我们的稳定性措施。我们的动态行走模型将包含足够的肌肉激活来向前推进，以及仿生状态反馈控制，包括神经元噪声和生理时间延迟，以确保横向稳定性。我们将在模型和健康人身上进行类似的实验，以确定小到中度的扰动如何影响局部行走稳定性，以及大的扰动如何影响全局行走稳定性。总之，这些努力将告诉我们，当我们的模型和/或人类受试者经历大的扰动时，在无扰动或最小扰动时获得的适当定义的局部稳定性测量是否可以预测实际的跌倒风险。如果是这样，在这个项目中开发的工具可能会显著提高我们预测的能力，从而预防运动障碍患者的跌倒。这些工具还将为确定人类用于预防跌倒的生物力学和神经生理机制以及评估旨在帮助增强这些机制的不同治疗干预措施的功效提供一个连贯的平台。公共卫生相关性：跌倒和由跌倒引起的伤害是老年人和各种行走障碍患者（包括中风、截肢和许多其他疾病）的一个重大卫生保健问题。找到准确预测和预防这些跌倒的方法将大大延长和改善这些患者的生命。提出的工作将应用新颖的工程概念来直接量化步行过程中的动态稳定性，以解决这一关键问题。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.