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CAREER: Transparent Robot-Aided Rehabilitation (TRAIN): Robot-Aided Rehabilitation with Refined Characterization of Altered Biomechanics & Enhanced Physical Human-Robot Interaction

职业：透明机器人辅助康复（TRAIN）：具有改变生物力学特征的机器人辅助康复

基本信息

批准号：
1846885
负责人：
Hyunglae Lee
金额：
$ 54.73万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846885&HistoricalAwards=false
关键词：
CAREER Transparent Robot Aided Rehabilitation

项目摘要

Stroke is a leading cause of long-term disabilities in the United States, affecting about 6.5 million Americans. With a decreasing stroke mortality and increase in the aging population, the number of people requiring rehabilitation after a stroke is projected to increase, creating a critical need to improve the effectiveness of stroke rehabilitation services. To address this challenge, this Faculty Early Career Development Program (CAREER) project introduces a new robot-aided rehabilitation framework, namely Transparent Robot-Aided Rehabilitation (TRAIN). This framework builds upon "enhanced transparency" in two distinct aspects: (1) transparency in terms of understanding altered biomechanics following stroke and (2) transparency in physical human-robot interaction, i.e., the concept of a robot being physically imperceptible to a human during motor tasks. The project focuses on developing the TRAIN framework for shoulder rehabilitation and testing the effectiveness for stroke survivors. Shoulder dysfunction is one of the most common complications following stroke, but robot-aided shoulder rehabilitation has not yet been fully explored. Successful application of the TRAIN framework to robotic shoulder exercise therapy will directly benefit the overall motor function of the upper extremity including improved range of motion, strength, and stability. In addition, successful application will lead to secondary benefits of improving the quality of life, such as reduced fatigue during motor tasks and improved independence in daily activities. Research activities and outcomes of this project will be seamlessly integrated into various education and outreach programs in order to excite and attract a diverse group of students, inspire them to pursue careers in STEM, and train next-generation scientists and engineers in robotics and human movement science. A unique "Outreach on Demand" program will promote outreach opportunities for underrepresented minority students.The investigator's long-term research goal is to advance robot-aided rehabilitation through integrated innovations in robot design, controller design, and refined quantification (system identification) of the neuromuscular system. Toward this goal, this project will produce a transformative framework for shoulder rehabilitation using integrated innovations in robot design (a lightweight, parallel-actuated shoulder exoskeleton robot), system identification (refined quantification of 3D shoulder impedance), and controller design (a biomechanics-based active impedance controller). Integration of a novel lightweight, parallel-actuated shoulder exoskeleton robot that minimally impacts natural arm dynamics, in combination with a fast and robust system identification algorithm, will advance understanding of how brain injury due to stroke alters 3D shoulder impedance. The Research Plan is organized under 4 thrusts. The FIRST THRUST is to develop a 5-DOF lightweight, parallel, actuated shoulder exoskeleton Robot. The robot will consist of a fully-actuated/motor driven 3-DOF spherical parallel manipulator (SPM) and a 2-DOF passive slip interface. The SPM consists of three parallel actuators connected to a shoulder piece coupled to the user; the slip interface is a cuff placed on the user's upper arm and is also coupled to the shoulder piece. The actuators are coupled to provide access to a spherical workspace. The optimal configuration of the robot will be determined and its ability to apply precise perturbations and simulate a wide range of impedances at the shoulder joint will be evaluated. The SECOND THRUST is to quantify 3D shoulder impedance during dynamic motor Tasks. Using the optimized robot, a robust system identification algorithm well be developed to quantify 3D shoulder impedances (stiffness, damping and inertia) in the direction of arm movements in young subjects with no history of neuromuscular disorders. The robot and algorithms developed will then be used to determine how shoulder impedance during normal shoulder functions is altered in stroke patients with chronic hemiparesis. The THIRD THRUST is to develop a biomechanics-based active impedance controller. An active impedance controller will further enhance transparency by altering the damping resistance from the robot in response to the user's intent of motion, e. g., to lower damping to reduce the experience of undesired resistance when the user intends to move in a direction. The effectiveness of the controller, i.e. its ability to increase transparency by lowering muscle activity/effort, will then be assessed in the Thrust 2 stroke patients. The FOURTH THRUST is to develop and evaluate patient-specific, adaptive exercise therapy based on the TRAIN Framework. Based on quantification of altered shoulder impedance in each stroke patient participating in earlier Thrusts, robotic exercise therapy that aims to correct altered shoulder impedance towards the unimpaired baseline (as determined from age-matched unaffected controls) will be developed. A 6-week (12 sessions) patient specific robotic training program that provides a unique set of strengthening and stretching exercises will be designed to adjust robotic impedance based on assessment of the patient's motor performance. Finally, the exercise program will be evaluated in the patients for whom they were designed and the effects on improvement of shoulder motor function will be assessed post training and at a 3-month follow-up.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.

中风是美国长期残疾的主要原因，影响了大约650万美国人。随着中风死亡率的下降和人口老龄化的加剧，中风后需要康复的人数预计会增加，这就迫切需要提高中风康复服务的有效性。为了应对这一挑战，这个教师早期职业发展计划（Career）项目引入了一个新的机器人辅助康复框架，即透明机器人辅助康复（TRAIN）。该框架建立在两个不同方面的“增强透明度”的基础上：(1)理解中风后改变的生物力学方面的透明度；(2)物理人机交互方面的透明度，即在运动任务中，人类在物理上无法察觉机器人的概念。该项目侧重于开发用于肩部康复的TRAIN框架，并测试中风幸存者的有效性。肩部功能障碍是中风后最常见的并发症之一，但机器人辅助肩部康复尚未得到充分探索。TRAIN框架在机器人肩部运动治疗中的成功应用将直接有利于上肢的整体运动功能，包括提高运动范围、力量和稳定性。此外，成功的应用将带来改善生活质量的次要好处，例如减少运动任务时的疲劳和提高日常活动的独立性。该项目的研究活动和成果将无缝整合到各种教育和推广计划中，以激发和吸引不同群体的学生，激励他们在STEM领域从事职业，并培养下一代机器人和人体运动科学的科学家和工程师。一个独特的“按需外展”项目将为未被充分代表的少数民族学生提供外展机会。研究者的长期研究目标是通过机器人设计、控制器设计和神经肌肉系统的精细量化（系统识别）的集成创新来推进机器人辅助康复。为了实现这一目标，该项目将利用机器人设计（轻型、并联驱动的肩部外骨骼机器人）、系统识别（3D肩部阻抗的精细量化）和控制器设计（基于生物力学的主动阻抗控制器）的集成创新，为肩部康复提供一个变革性框架。集成了一种新型轻型、并联驱动的肩部外骨骼机器人，该机器人对自然手臂动力学的影响最小，结合了快速、鲁棒的系统识别算法，将促进对脑卒中导致的脑损伤如何改变3D肩部阻抗的理解。研究计划分为四个重点。第一推力是开发一个五自由度轻量级，并联，驱动的肩部外骨骼机器人。该机器人将由一个全驱动/电机驱动的3-DOF球面并联机械臂（SPM）和一个2-DOF被动滑移界面组成。SPM由三个并联执行器组成，它们连接到与用户耦合的肩片上；滑动接口是放置在用户上臂上的袖带，也与肩片耦合。执行器是耦合的，以提供对球形工作空间的访问。将确定机器人的最佳配置，并评估其在肩关节处应用精确扰动和模拟大范围阻抗的能力。第二个推力是量化动态运动任务期间的3D肩部阻抗。利用优化后的机器人，我们开发了一种鲁棒的系统识别算法，用于量化没有神经肌肉疾病史的年轻受试者手臂运动方向的3D肩部阻抗（刚度、阻尼和惯性）。开发的机器人和算法将用于确定慢性偏瘫中风患者正常肩关节功能时肩关节阻抗的变化。第三个目标是开发基于生物力学的主动阻抗控制器。主动阻抗控制器将通过改变机器人的阻尼阻力来响应用户的运动意图，从而进一步提高透明度，例如，当用户打算朝一个方向移动时，降低阻尼以减少不希望的阻力体验。控制器的有效性，即通过降低肌肉活动/努力来增加透明度的能力，将在Thrust 2卒中患者中进行评估。第四个THRUST是开发和评估基于TRAIN框架的患者特异性适应性运动疗法。基于对参与早期推力运动的每位中风患者肩部阻抗改变的量化，将开发机器人运动疗法，旨在将肩部阻抗改变纠正到未受损基线（由年龄匹配的未受影响的对照组确定）。一个为期6周（12次）的患者特定机器人训练计划，提供一套独特的强化和拉伸练习，根据评估患者的运动表现来调整机器人阻抗。最后，我们将对所设计运动方案的患者进行评估，并在训练后和3个月的随访中评估对肩部运动功能改善的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Regulation of 2D Arm Stability Against Unstable, Damping-Defined Environments in Physical Human-Robot Interaction

针对物理人机交互中不稳定、阻尼定义环境的 2D 手臂稳定性调节

DOI：
发表时间：
2020
期刊：
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020
影响因子：
0
作者：
Zahedi, F;Bitz, T;Phillips, C;and Lee, H
通讯作者：
and Lee, H

User-Adaptive Variable Damping Control Using Bayesian Optimization to Enhance Physical Human-Robot Interaction

使用贝叶斯优化的用户自适应可变阻尼控制来增强物理人机交互