NRI: Goal-Oriented, subject-Adaptive, robot-assisted Locomotor Learning (GOALL)

NRI：目标导向、主题自适应、机器人辅助运动学习 (GOALL)

• 批准号: 1638007
• 负责人: Fabrizio Sergi
• 金额: $ 56.99万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1638007&HistoricalAwards=false
• 关键词: NRI; Goal; Oriented; subject; Adaptive

1638007Sergi, FabrizioDemand for technology to support gait training after neurological injury is increasing due to population aging. Due to recent advances in sensing, actuation, and computation, robots are ideal tools to deliver gait training, but their potential in gait neurorehabilitation has not yet been fully realized. In this context, crucial difficulties are identified in the employed control schemes, which are required to accommodate inter-individual gait variations, while promoting stable and energetically efficient gait patterns. The proposed project combines experiments with a lower limb exoskeleton with biomechanical modeling to determine subject-specific assistance strategies that enable a new approach to robot-aided gait neurorehabilitation, named GOALL (Goal-Oriented, subject Adaptive, robot-assisted Locomotor Learning). The conducted research activities have relevant applications both in rehabilitation and in human augmentation, while improving our basic understanding of gait biomechanics. The planned education and outreach components will be targeted to engage a community of graduate, undergraduate and K-12 students in topics at the intersection of robotics and biomechanics. The dissemination of the research methods and results in an open source format will benefit the robotics and biomechanics communities.The proposed project formalizes new control methods to modulate discrete kinematic variables of gait, achieving controllability of such variables without fully constraining the gait cycle kinematics, thus promoting inter-individual variability in gait kinematics. To this aim, we pursue a systematic approach to the design of gait assistance primitives, i.e. multi-joint coordination patterns capable of modulating a chosen gait parameter, at different gait speeds. The proposed approach is based on inverse dynamics and pulsed torque approximation, and is followed by human-in-the-loop experiments to test the efficacy of assistance primitives to modulate a selected gait parameter during motor adaptation. The experimental investigation is paralleled by neuromechanical modeling of the response to robotic intervention, with the ultimate goal of generalizing the results to other gait parameters of interest for various patient populations.

1638007Sergi, Fabrizio 由于人口老龄化，对支持神经损伤后步态训练的技术的需求不断增加。由于传感、驱动和计算方面的最新进展，机器人成为提供步态训练的理想工具，但其在步态神经康复方面的潜力尚未完全实现。在这种情况下，在所采用的控制方案中发现了关键的困难，这些控制方案需要适应个体间的步态变化，同时促进稳定和高效的步态模式。拟议的项目将下肢外骨骼实验与生物力学建模相结合，以确定特定于主题的辅助策略，从而实现机器人辅助步态神经康复的新方法，称为 GOALL（目标导向、主题自适应、机器人辅助运动学习）。所进行的研究活动在康复和人体增强方面都有相关应用，同时提高了我们对步态生物力学的基本理解。计划中的教育和外展部分将旨在吸引研究生、本科生和 K-12 学生群体参与机器人技术和生物力学交叉领域的主题。以开源格式传播研究方法和结果将有利于机器人和生物力学界。拟议的项目形式化了新的控制方法来调制步态的离散运动学变量，在不完全约束步态周期运动学的情况下实现这些变量的可控性，从而促进步态运动学的个体间变异性。为此，我们追求一种系统的方法来设计步态辅助原语，即能够以不同步态速度调节所选步态参数的多关节协调模式。所提出的方法基于逆动力学和脉冲扭矩近似，并随后进行人机在环实验，以测试辅助原语在电机适应期间调制选定步态参数的功效。实验研究与机器人干预反应的神经力学建模并行，最终目标是将结果推广到不同患者群体感兴趣的其他步态参数。

项目成果

Training Propulsion via Acceleration of the Trailing Limb

• DOI: 10.1109/tnsre.2020.3032094
• 发表时间: 2020-12-01
• 期刊: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
• 影响因子: 4.9
• 作者: Farrens, Andria J.;Lilley, Maria;Sergi, Fabrizio
• 通讯作者: Sergi, Fabrizio

Single-stride exposure to pulse torque assistance provided by a robotic exoskeleton at the hip and knee joints

单步接触由髋关节和膝关节处的机器人外骨骼提供的脉冲扭矩辅助

• DOI: 10.1109/icorr.2019.8779426
• 发表时间: 2019
• 期刊: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR
• 作者: McGrath, Robert L.;Sergi, Fabrizio
• 通讯作者: Sergi, Fabrizio

Using Bayesian Optimization to Identify Optimal Exoskeleton Parameters Targeting Propulsion Mechanics: A Simulation Study

使用贝叶斯优化来确定针对推进力学的最佳外骨骼参数：仿真研究

• DOI: 10.1109/iros51168.2021.9635982
• 发表时间: 2021
• 期刊: Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
• 作者: Kim, GilHwan;Sergi, Fabrizio
• 通讯作者: Sergi, Fabrizio

Walking speed changes in response to novel user-driven treadmill control

• DOI: 10.1016/j.jbiomech.2018.07.035
• 发表时间: 2018-09-10
• 期刊: JOURNAL OF BIOMECHANICS
• 影响因子: 2.4
• 作者: Ray, Nicole T.;Knarr, Brian A.;Higginson, Jill S.
• 通讯作者: Higginson, Jill S.

Dynamic Modeling and State Estimation of Cable-Conduit Actuation During Interaction With Nonpassive Environments

• DOI: 10.1109/tmech.2020.3040159
• 发表时间: 2021-10
• 期刊: IEEE/ASME Transactions on Mechatronics
• 作者: Stephen Buchanan;F. Sergi