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: 2203143
• 负责人: Philippe Malcolm
• 金额: $ 23.84万
• 依托单位: University of Nebraska at Omaha
• 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=2203143&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。衰老伴随着活动能力的显着下降。现有的治疗方法和疗法依赖于步行力度的呼吸测量。这些呼吸测量只能量化步行的平均努力程度。由于这种限制，现有的治疗方法有时无法针对最需要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测量步行强度的限制。获得这些新信息将有助于评估疗法如何影响不同的运动阶段。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来开发，该模型具有物理诱导的特定运动阶段的变化。例如，向前拉力将施加到模型的腰部以引起变化，从而可以利用这些变化来检测步行力的波动。该计算机步行模型提供了步行所需努力的完整测量，这将用于验证数据驱动的方法。接下来，新的数据驱动方法将使用真实人类步行实验的测量结果进行验证。在这些人体实验中，连接到参与者腰部的机器人绳索将施加拉力，以引起变化，从而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步行所需的努力有何不同。将通过对年轻人和老年人进行人体实验来表征每个运动阶段的努力差异。该项目的成果将有助于创建增强的治疗方法和辅助设备，以改善运动的各个阶段。在整个项目中，研究人员将通过奥舍终身学习研究所为老年人提供有关步行、数据科学和数字工程的力学和健康方面的课程。该项目的目标是利用新的数据驱动方法来表征老年人与年轻人步态周期各阶段代谢成本的差异。该项目将结合基于系统识别和机器人扰动的新颖的数据驱动方法来表征无法直接测量的信号的时间分布，例如代谢成本。第一个目标将在模拟步态数据中生成代谢成本的时间曲线。将基于加权回归、神经网络和自动编码器开发新的数据驱动方法，以从生物力学信号中识别代谢成本时间曲线。最初，这些方法将在预测步行模拟中创建，从该模拟中可以完全了解代谢时间曲线，以便可以在开发过程中评估新方法。第二个目标将评估人体实验中不同的时间剖面估计方法。第一个目标中创建的方法将通过机器人扰动的人体实验进行测试。使用数据驱动方法检测摆动和推出变化的能力也将通过人体实验进行研究，其中使用弹性脚踝系绳或附加质量来引入步态周期的直接变化。第三个目标将描述老年人和年轻人步态周期各阶段成本贡献的差异。第一个子任务将通过应用数据驱动的方法，使用年轻人和老年人的测量数据计算瞬时成本，来表征代谢成本的特定阶段差异。第二个子任务将确定数据驱动的时间剖面估计方法在不同人群中的普遍性。这项研究将通过提供动态代谢成本时间曲线来改变步态分析，而现有技术无法测量动态代谢成本时间曲线。获得这些新信息将导致多种生物力学应用的改进，包括（1）运动障碍的诊断，（2）有针对性的辅助设备的处方，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) 和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。