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

中风患者的上肢残疾对他们一生的生活质量有毁灭性的影响。每年，仅在美国就报告了大约80万例新的中风病例。恢复手臂末端和手部灵活性是该人群的首要任务。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是负担不起的，并且需要技术人员和大的临床空间进行操作。此外，目前的机器人控制算法在说服和使患者参与训练循环方面效率不高。该奖项支持研究开发创新的自适应算法，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。该项目的价格合理、用户友好的机器人界面有可能减轻医护人员的负担，并加速整个系统的可扩展性，从医生办公室的培训和测试到家用设备，以满足患者的康复需求。该项目的目标将通过三个研究重点来实现：1）开发使用触觉力场致动的用于上肢训练的多模态可穿戴外骨骼; 2）利用共享控制理论来开发闭环自适应辅助策略;以及3）验证所提出的康复平台对脑卒中上肢损伤患者的作用。在这个项目中，一个新的，多模式的，便携式平面机器人训练平台，具有方便的用户为中心的设计将被开发，以克服平移的障碍，并协助恢复中风患者的手臂末端。通过利用共享控制理论，并使用一种新的，自适应的，闭环卡尔曼滤波算法，自适应和意图驱动的康复算法将被开发。患者的多模式生物标志物将以自适应、按需辅助、闭环算法的形式并入，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态皮质-肌肉活动在手臂肢体规划和执行中的作用的基本理解，而且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。该奖项反映了NSF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。

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.