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

[38007]由于人口老龄化，对支持神经损伤后步态训练技术的需求正在增加。由于最近在传感、驱动和计算方面的进展，机器人是提供步态训练的理想工具，但它们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确定了关键的困难，这些控制方案需要适应个体之间的步态变化，同时促进稳定和能量高效的步态模式。该项目将下肢外骨骼实验与生物力学建模相结合，以确定受试者特定的辅助策略，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，受试者自适应，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的应用，同时提高了我们对步态生物力学的基本认识。计划的教育和推广部分将针对研究生，本科生和K-12学生社区，在机器人和生物力学的交叉主题。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社区。本课题提出了一种新的控制方法来调节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可变性。为此，我们寻求一种系统的方法来设计步态辅助原语，即在不同的步态速度下，能够调节选定的步态参数的多关节协调模式。该方法基于逆动力学和脉冲扭矩近似，然后通过人在环实验来测试辅助原语在运动适应过程中调节选定步态参数的有效性。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各种患者群体感兴趣的其他步态参数。

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.