Collaborative Research: Detecting Gait Phases with Raised Metabolic Cost using Robotic Perturbations and System Identification for Enabling Targeted Rehabilitation Therapy

合作研究：使用机器人扰动和系统识别来检测代谢成本升高的步态阶段，以实现有针对性的康复治疗

• 批准号: 2203144
• 负责人: Keegan Moore
• 金额: $ 23.53万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203144&HistoricalAwards=false
• 关键词: Collaborative; Research; Detecting; Gait; Phases

Being able to walk easily is strongly associated with independence and quality of life. Aging is accompanied by a significant reduction in mobility. Existing treatments and therapies rely on respiratory measurements of walking effort. These respiratory measurements can only quantify the average effort of walking. As a result of this limitation, existing treatments and therapies sometimes fail to target the phases of the walking motion that need the most assistance. This project will use data-driven approaches and models to overcome limitations in the ability to measure the effort of walking. Access to this new information will enable evaluating how therapies affect different stages of motion. The data-driven methods will be initially developed using a dataset generated by a computer walking models with physically induced changes to specific stages of motion. For example, forward-pulling forces will be applied to the waist of the model to induce changes that can be leveraged to detect the fluctuations in walking effort. This computer walking model provides access to a complete measure of the effort required for walking, which will be used to validate the data-driven methods. Next, the new data-driven methods will be validated using measurements from real human walking experiments. In these human experiments, pulling forces will be applied by a robotic tether connected to the waist of the participant to induce changes that will be used to detect the effort of the different motion stages. In the final studies, the methods will be used to determine how the effort required for walking differs in younger and older adults. The differences in the effort will be characterized in each stage of motion using human experiments with both younger and older adults. The outcomes of this project will help lead to the creation of enhanced treatments and assistive devices that improve all stages of motion. Throughout this project, the investigators will provide courses for older adults on the mechanics and health aspects of walking and data science and digital engineering through the Osher Lifelong Learning Institute.The goal of this project is to leverage new data-driven approaches to characterize differences in metabolic cost of phases of the gait cycle in old versus young adults. The project will combine novel, data-driven approaches based on system identification and robotic perturbations to characterize the time profile of signals that cannot be measured directly, such as metabolic cost. The first objective will produce the time profile of metabolic cost within simulated gait data. Novel data-driven approaches will be developed based on weighted regression, neural networks, and autoencoders to identify the metabolic cost time profile from biomechanical signals. Initially, these methods will be created in a predictive walking simulation from which the metabolic time profile is fully known, such that the new methods can be evaluated during their development. The second objective will evaluate different time profile estimation approaches in human experiments. The methods created in the first objective will be tested using human experiments with robotic perturbations. The capacity of using the data-driven methods to detect changes in swing and push-off will also be investigated using human experiments where elastic ankle tethers or added mass are used to introduce direct changes to the gait cycle. The third objective will characterize the differences in cost contributions of the phases of the gait cycle between older and younger adults. The first subtask will characterize the phase-specific differences in metabolic cost by applying the data-driven methods to compute the instantaneous costs using measured data from younger and older adults. The second subtask will determine the generalizability of the data-driven time-profile estimation approaches across different populations. This research will transform gait analysis by providing access to dynamic metabolic cost time profiles, which cannot be measured using existing techniques. Access to this new information will lead to improvements across multiple biomechanics applications, including (1) diagnosis of motion impairments, (2) prescription of targeted assistive devices, and (3) targeted rehabilitation exercises. This project is jointly funded by the Disability and Rehabilitation Engineering Program (DARE) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

能够轻松行走与独立性和生活质量密切相关。衰老伴随着迁移率的显着降低。现有的治疗和疗法依赖步行量的呼吸测量。这些呼吸测量只能量化步行的平均努力。由于这种限制，现有的治疗和疗法有时无法针对需要最大帮助的步行运动阶段。该项目将使用数据驱动的方法和模型来克服衡量步行努力的能力的限制。访问此新信息将使您能够评估疗法如何影响运动阶段。数据驱动的方法最初将使用由计算机步行模型生成的数据集，该模型对特定运动的特定阶段进行了物理诱导的变化。例如，将向前推力应用于模型的腰部，以诱导可以利用的变化，以检测步行努力的波动。该计算机步行模型可访问步行所需的努力的完整度量，该量度将用于验证数据驱动的方法。接下来，将使用实际人类步行实验的测量结果来验证新的数据驱动方法。在这些人类的实验中，将通过连接到参与者的腰部的机器人系绳来施加拉力，以诱发变化，该变化将用于检测不同运动阶段的努力。在最终研究中，这些方法将用于确定年轻人和老年人所需的努力如何不同。使用与年轻人和老年人的人类实验，将在运动的每个阶段表征这项工作的差异。该项目的结果将有助于创建增强的治疗方法和辅助设备，以改善所有运动阶段。在整个项目中，调查人员将通过Osher终生学习研究所为老年人提供有关步行和数据科学和数字工程学的机械和健康方面的课程。该项目的目的是利用新的数据驱动方法来表征老年人对老年人步态循环阶段的代谢成本的差异。该项目将根据系统识别和机器人扰动来结合新颖的数据驱动方法，以表征无法直接测量的信号的时间概况，例如代谢成本。第一个目标将在模拟步态数据中产生代谢成本的时间概况。新颖的数据驱动方法将根据加权回归，神经网络和自动编码器开发，以确定来自生物力学信号的代谢成本时间概况。最初，这些方法将在预测性步行模拟中创建，从中可以从中完全知道代谢时间概况，从而可以在开发过程中评估新方法。第二个目标将评估人类实验中不同的时间概况估计方法。第一个目标中创建的方法将使用具有机器人扰动的人类实验进行测试。还将使用人类实验研究使用数据驱动方法来检测挥杆和推断的变化的能力，在该实验中，使用弹性踝关节或添加的质量来引入步态周期的直接变化。第三个目标将表征年龄和年轻人之间步态周期阶段的成本贡献差异。第一个子任务将通过应用数据驱动的方法使用来自年轻和老年人的测量数据来计算瞬时成本来表征代谢成本的特定阶段差异。第二个子任务将确定不同人群中数据驱动的时元估计方法的普遍性。这项研究将通过提供动态代谢成本时间概况的访问来改变步态分析，这无法使用现有技术来衡量。访问这些新信息将导致多个生物力学应用程序的改进，包括（1）运动障碍的诊断，（2）针对性辅助设备的处方以及（3）有针对性的康复练习。 该项目由残疾与康复工程计划（DARE）共同资助，启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响审查标准通过评估来进行评估的。