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

在美国，中风是导致长期残疾的主要原因，影响着大约650万美国人。随着中风死亡率的下降和老龄化人口的增加，中风后需要康复的人数预计将增加，这就产生了改善中风康复服务有效性的迫切需要。为了应对这一挑战，学院早期职业发展计划(CALEAR)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)。这一框架建立在两个不同的方面：(1)在理解中风后改变的生物力学方面的透明度；(2)人与机器人物理交互的透明度，即机器人在执行运动任务时在物理上是不可察觉的概念。该项目的重点是开发肩部康复训练框架，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，但机器人辅助的肩部康复尚未得到充分探索。成功地将训练框架应用于机器人肩部运动疗法将直接有利于上肢的整体运动功能，包括改善运动范围、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，如减少运动任务中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，以激励和吸引不同的学生群体，激励他们在STEM追求职业生涯，并培训机器人和人类运动科学方面的下一代科学家和工程师。研究人员的长期研究目标是通过在机器人设计、控制器设计和神经肌肉系统的精细量化(系统识别)方面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(一种轻便、平行驱动的肩部外骨骼机器人)、系统识别(精确量化3D肩部阻抗)和控制器设计(基于生物力学的主动阻抗控制器)方面的综合创新，为肩部康复创造一个变革性的框架。集成了一种对自然手臂动力学影响最小的新型轻便、并行驱动的肩部外骨骼机器人，结合快速而稳健的系统识别算法，将促进对中风导致的脑损伤如何改变3D肩部阻抗的理解。研究计划分为四个部分。第一个推力是开发一种5自由度轻便、并行、驱动的肩部外骨骼机器人。该机器人将由一个完全驱动/电机驱动的3自由度球面并联机器人(SPM)和一个2自由度被动滑动接口组成。SPM由三个平行的致动器组成，连接到连接到用户的肩部；滑动接口是放置在用户的上臂上的袖带，也耦合到肩部。所述致动器连接以提供通向球形工作空间的通道。将确定机器人的最佳配置，并评估其施加精确扰动和模拟肩关节大范围阻抗的能力。第二个推力是量化动态运动任务中的3D肩阻抗。使用优化后的机器人，可以很好地开发出稳健的系统识别算法，以量化没有神经肌肉疾病病史的年轻受试者手臂运动方向的三维肩阻抗(刚度、阻尼值和惯性)。然后，开发的机器人和算法将用于确定患有慢性偏瘫的中风患者在正常肩部功能期间肩部阻抗是如何改变的。第三个推力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响应于用户的运动意图而改变来自机器人的阻尼力来进一步提高透明度，例如，当用户打算在方向上移动时降低阻尼值以减少不期望的阻力的体验。控制器的有效性，即其通过降低肌肉活动/努力来增加透明度的能力，将在推力2中风患者中进行评估。第四个重点是基于训练框架开发和评估针对患者的适应性运动疗法。基于对参与早期冲刺的每个中风患者肩阻抗变化的量化，将开发出机器人运动疗法，旨在将变化的肩阻抗校正到未受损的基线(根据年龄匹配的未受影响的对照组确定)。为期6周(12节)的患者专用机器人培训计划将提供一套独特的加强和伸展练习，旨在根据对患者运动性能的评估来调整机器人阻抗。最后，锻炼计划将在为其设计的患者身上进行评估，并将在培训后和3个月的随访中评估对改善肩关节运动功能的效果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.