CAREER: Neuromechanics of human-robot interaction via robot-assisted in-vivo imaging of neuromuscular function

机器人越来越多地用于支持神经康复，性能增强，机器人辅助制造和动力假肢中的人类运动功能。虽然在过去的20年里，技术能力的提高令人印象深刻，但由于对人类-机器人辅助运动功能中人类组成部分的基本理解不足，进展受到限制。这个CAREER项目将通过非侵入性测量大脑功能和肌肉力量和刚度来解决这一限制，同时受试者与机器人互动，在磁共振扫描仪内执行手腕任务。该框架将用于研究神经运动障碍如何影响肌肉协调，为机器人辅助运动功能的精细神经肌肉骨骼模型和个性化人机交互策略铺平道路，以增强性能和神经康复。该项目将为研究生和本科生提供生物力学，神经科学和机器人之间交叉问题的培训，帮助创建一个多学科的劳动力队伍，准备应对学术界和工业界康复工程研究的复杂挑战。开发的计算模型将成为一个新的外展计划的基础，该计划将在神经肌肉建模的背景下引入游戏，该计划将被用于初中和高中学生，以提高他们对追求STEM教育的兴趣。研究者的长期研究目标是开发新的神经科学干预措施，用于神经运动障碍后的运动康复。为了实现这一目标，研究人员目前的研究重点是：1）开发和验证用于人类辅助和康复的机器人技术; 2）在实验研究中使用这些新技术对健康和受损的人类运动控制进行建模。 该CAREER项目旨在使用MRI兼容的机器人通过功能性磁共振成像（fMRI）和磁共振弹性成像（MRE）在运动任务期间非侵入性地测量大脑功能，这些运动任务要求受试者在MR扫描仪中执行动态点对点和等距手腕任务。结合神经力学建模与先进的实验方法和假设驱动的实验，这个框架将被用来量化人体的内在神经肌肉动力学如何影响物理人的相互作用，并研究最佳的控制策略，在现实的神经肌肉动力学的存在下，人机交互。 该项目建立在研究人员成功开发一系列MRI兼容机器人设备和多肌肉磁共振弹性成像（MM-MRE）成像技术的基础上，用于确定肌肉协调的基本标准。研究计划分为两个重点。第一个重点是使用结合建模、行为实验和神经成像的多方面方法研究强调力和阻抗控制的神经机制。 将开发一个计算框架，用于分析手腕指向运动的力和阻抗控制，包括精确的肌肉骨骼动力学。 该框架将在对健康受试者进行的实验中进行验证，这些受试者学习在稳定或不稳定的任务中控制力或阻抗。将识别参与学习产生任务执行所需的适当力和阻抗的大脑区域。 一个使用功能性磁共振成像和磁共振兼容腕式机器人的神经成像实验将被用来测试在动态任务期间力和阻抗的神经表征的假设，特别是“力和阻抗在不同的皮层区域被控制”，以及“肌肉协同激活的表征与快速学习状态的表征重叠。“第二个推力的重点是使用MM-MRE来估计前臂多个肌肉的机械特性。新技术将肌肉特异性MRE测量与通过MRI兼容的仪器手柄获得的关节扭矩测量相结合。这项技术将用于确定适用于手和手腕任务的肌肉协调标准，并量化这些标准在中风后个体中的变化。基于获得的独特测量结果，目前接受的短程刚度模型，尚未直接在体内和人体内验证，将使用高功率MRI兼容机器人施加的机器人扰动进行测试。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Robots are used more and more to support human motor function in neurorehabilitation, performance augmentation, robot-assisted manufacturing, and powered prosthetics. While improvement in technological capabilities has been impressive during the last 20 years, progress has been limited by poor fundamental understanding of the human component during human-robot-assisted motor function. This CAREER project will address this limitation by noninvasively measuring brain function and muscle force and stiffness while a subject interacts with a robot to perform wrist tasks within a magnetic resonance scanner. This framework will be used to study how neuromotor impairment affects muscle coordination, paving the way for refined neuromusculoskeletal models of robot-assisted motor function and for personalized human-robot interaction strategies for performance augmentation and neurorehabilitation. This project will provide training for graduate and undergraduate students in problems at the intersection between biomechanics, neuroscience, and robotics, helping create a multidisciplinary workforce ready to tackle the complex challenges of rehabilitation engineering research in both academia and industry. The computational model developed will form the basis of a new outreach program that introduces gaming within the context of neuromuscular modeling, which will be administered to middle and high school students to raise interest in pursuing STEM education.The investigator’s long-term research goal is to develop novel neuroscience-grounded interventions for movement rehabilitation after neuromotor disorders. Toward this goal, the investigator’s current research is focused on 1) developing and validating robotic technologies for human assistance and rehabilitation and on 2) using such novel technologies in experimental studies modeling the healthy and impaired human motor control. This CAREER project seeks to use MRI-compatible robots to non-invasively measure brain function via functional magnetic resonance imaging (fMRI) and muscle function via magnetic resonance elastography(MRE) during motor tasks that require subjects to perform dynamic point-to-point and isometric wrist tasks in an MR scanner. Combining neuromechanical modeling with advanced experimental methods and with hypothesis-driven experiments, this framework will be used to quantify how the intrinsic neuromuscular dynamics of the human body affect physical human-interaction and to study optimal control policies for human-robot interaction in the presence of realistic neuromuscular dynamics. The project builds on the investigator’s success in developing a family of MRI-compatible robotic devices and a multi-muscle magnetic resonance elastography (MM-MRE) imaging technique for identifying fundamental criteria of muscle-coordination. The research plan is divided into two thrusts. The FIRST thrust focuses on studying the neural mechanisms underscoring force and impedance control using a multifaceted approach that combines modeling, behavioral experiments, and neuroimaging. A computational framework will be developed for analyzing force and impedance control for wrist pointing movements that includes accurate musculoskeletal dynamics. This framework will be validated in experiments conducted on healthy subjects that learn to control force or impedance in stable or unstable tasks. Brain regions involved in learning to generate proper force and impedance required for task execution will be identified. A neuroimaging experiment that uses fMRI and an MRI-compatible wrist robot will be used to test hypotheses on the neural representation of force and impedance during dynamic tasks, specifically that "force and impedance are controlled in different cortical regions" and that "the representation of muscle co-activation overlaps with the representation of the fast learning state." The SECOND thrust focuses on using MM-MRE to estimate the mechanical properties of multiple muscles in the forearm. The new technique integrates muscle-specific MRE measurements with measurements of joint torque obtained via an MRI-compatible instrumented handle. This technique will be used to identify criteria of muscle coordination that apply for tasks of the hand and wrist and to quantify how these criteria change in post-stroke individuals. Based on the unique measurements obtained, the currently accepted short-range stiffness model, not yet directly validated in-vivo and in-humans, will be tested using robotic perturbations applied by a high power MRI-compatible robot.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.