An Adaptive Closed-Loop Robotic Exoskeleton for Upper Extremity Motor Rehabilitation

中风患者的上肢残疾会对他们一生的生活质量产生毁灭性的影响。仅在美国，每年就报告了大约80万例新的中风病例。恢复手臂四肢和手部灵活性是这一人群的首要任务。近年来，辅助机器人和康复外骨骼平台在运动训练和上肢功能恢复方面显示出良好的效果。然而，目前可用的机器人和外骨骼平台负担不起，需要技术人员和较大的临床手术空间。此外，目前的机器人控制算法在说服患者并使其参与训练循环方面效率低下。该奖项支持开发创新的自适应算法的研究，该算法嵌入创新的便携式外骨骼平台，用于中风患者的手臂肢体训练。该项目经济实惠、用户友好的机器人界面有可能减轻医护人员的负担，并加快整个系统的可扩展性，从医生办公室的培训和测试到满足患者康复需求的家用设备。该项目的目标将通过三项研究努力实现：1)开发一种使用触觉力场驱动的多模式可穿戴外骨骼用于上肢训练；2)利用共享控制理论开发闭环自适应辅助策略；以及3)验证所提出的中风患者上肢损伤康复平台。在这个项目中，将开发一种新的、多通道的、便携式的平面机器人训练平台，以方便以用户为中心的设计，以克服平移障碍，帮助中风患者的手臂肢体恢复。通过利用共享控制理论，并使用一种新颖的自适应闭环卡尔曼滤波算法，将开发出一种自适应和意图驱动的康复算法。患者的多模式生物标志物将以自适应、按需协助、闭环算法的形式合并，以加速皮质可塑性的恢复和补救。这项研究工作不仅将促进对多模式皮质肌肉活动在规划和执行手臂肢体中的作用的基本了解，还将产生新的适应性康复算法，以加速受影响的中风人群的运动恢复。该项目由残疾和康复工程计划(DARE)和既定的激励竞争研究计划(EPSCoR)联合资助。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。

Upper limb disability in individuals with stroke has devastating impacts on their quality of life over their lifespan. Each year, approximately 800,000 new stroke cases are reported in the United States alone. The restoration of arm extremity and hand dexterity is the highest priority among this population. In recent years, assistive robots and rehabilitation exoskeleton platforms showed promising results in motor training and recovery of upper limb function. However, the currently available robotic and exoskeleton platforms are not affordable and require technicians and large clinical space for operation. Additionally, the current robotic control algorithms are not efficient in persuading and engaging the patient into the loop of training. This award supports research to develop innovative adaptive algorithms embedded in an innovative and portable exoskeleton platform for arm extremity training in stroke patients. The project’s affordable, user-friendly robotic interface has the potential to relieve the burden on healthcare workers and accelerate the scalability of the overall system from doctor’s office-based training and testing to home-use devices to fulfill patients’ rehabilitation needs.This project's goals will be accomplished through three research thrusts: 1) developing a multimodal, wearable exoskeleton actuated using a haptic forcefield for upper extremity training; 2) leveraging the shared control theory to develop a closed-loop adaptive assistive strategy; and 3) validating the proposed rehabilitative platform on stroke patients with upper extremity impairment. In this project, a new, multimodal, and portable planar robotic training platform with a convenient user-centered design will be developed to overcome the translational barriers and assist the recovery of arm extremity in stroke patients. By leveraging the shared control theory and using a novel, adaptive, closed-loop Kalman filter algorithm, an adaptive and intention-driven rehabilitative algorithm will be developed. The patient’s multimodal biomarkers will be incorporated in the form of an adaptive, assist-as-needed, closed-loop algorithm to accelerate the recovery and remedy of the cortical plasticity. This research work will not only advance the fundamental understanding of the role of multimodal cortico-muscular activities in the planning and execution of arm extremity but also produce new adaptive rehabilitative algorithms to accelerate the motor recovery in the affected stroke population.This project is jointly funded by the Disabilities and Rehabilitation Engineering Program (DARE) and the Established Program to Stimulate Competitive Research (EPSCoR).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.