CRCNS Research Proposal: Collaborative Research: Data-driven approaches for restoring naturalistic motor functions using functional neural stimulation

CRCNS 研究提案：合作研究：使用功能性神经刺激恢复自然运动功能的数据驱动方法

• 批准号: 1724263
• 负责人: V John Mathews
• 金额: $ 40.51万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724263&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Collaborative; Data

Individuals with lower limb paralysis retain the ability to plan and initiate gait within the central nervous system but, due to conditions such as spinal cord injury or stroke, are unable to transmit those directives to the muscles of the lower limb. This research project uses a biologically-inspired, data-driven approach to address this deficiency. Specifically, the research team is developing and evaluating machine learning methods and electrical stimulation of nerves to control movements of the lower limbs. The goal is to develop and evaluate methods to restore natural, coordinated, and graceful gait in an animal model of paralysis. This activity is a first step toward providing benefit to the paralyzed community by creating pathways toward the development and commercialization of functional gait restoration systems that evoke more natural, controlled movement of paralyzed limbs. In addition, the project offers unique opportunities to train engineering students in the performance of pre-clinical studies, placing these future researchers at the forefront of engineering technology and medical research. This research project aims to develop and evaluate methods to restore natural, coordinated, and graceful gait in an animal model of paralysis using a synergistic collaboration of a multi-disciplinary and multi-university team of investigators and a combination of innovative modeling and algorithm development supported by a series of experiments. Specifically, the project aims to achieve the following sub-goals involving application of data-driven algorithms in a series of experiments that are designed to evoke increasingly complex movements over the course of the proposed work: (a) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of a single joint in only a single direction, to allow comparison of the data-driven model to earlier, classical controls methods; (b) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of a single joint in both directions, to elucidate methods used by the advanced controller's solution to the under-constrained problem of agonist-antagonist muscle pair control; (c) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of multiple joints in both directions, to elucidate methods used by the advanced controller's solution to the competing-goals problem of biarticular muscle control; and, (d) Recreate natural, coordinated, and graceful gait by use of the advanced controllers arising from the first three goals, and demonstrate this result on a treadmill platform.

下肢瘫痪的个体保留了在中枢神经系统内计划和启动步态的能力，但是由于脊髓损伤或中风等条件，无法将这些指令传输到下肢的肌肉。该研究项目使用以生物学启发的数据驱动方法来解决这种缺陷。具体而言，研究团队正在开发和评估机器学习方法和对神经的电刺激以控制下肢的运动。目的是开发和评估在动物瘫痪模型中恢复自然，协调和优美步态的方法。这项活动是通过为诱发更自然，控制的四肢运动的更自然，受控运动的功能步态恢复系统的发展和商业化来为瘫痪社区提供好处的第一步。此外，该项目为培训工程专业的学生提供了独特的机会，可以使这些未来的研究人员处于工程技术和医学研究的最前沿。该研究项目旨在在动物瘫痪模型中开发和评估方法，以恢复自然，协调和优美的步态，使用多学科和多大学研究人员组成的协同合作以及一系列实验人支持的创新建模和算法开发的结合。具体而言，该项目旨在在一系列实验中实现以下子目标，其中涉及数据驱动算法的应用，这些实验旨在在拟议的工作过程中引起越来越复杂的运动：（a）开发，表征和评估高级控制器的直角和联合扭矩，以便在单个方向上仅在单个方向上生产单个关节，从而可以比较较早的模型，以比较较早的数据，以便于较早的数据进行分类; （b）在两个方向上开发，表征和评估单个接头的高级控制器和关节扭矩产生的高级控制器，以阐明高级控制器解决方案使用的方法，以解决激动剂 - 抗抗激肌对控制的不足问题； （c）开发，表征和评估两个方向上多个关节的关节角度和关节扭矩产生的高级控制器，以阐明高级控制器解决方案使用的方法，以实现双学位肌肉控制的竞争目标问题； （d）通过使用前三个目标引起的高级控制器来重现天然，协调和优美的步态，并在跑步机平台上证明这一结果。