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

用于上肢运动康复的自适应闭环机器人外骨骼

• 批准号: 2245558
• 负责人: Reza Abiri
• 金额: $ 46万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245558&HistoricalAwards=false
• 关键词: Adaptive; Closed; Loop; Robotic; Exoskeleton

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.

中风患者的上肢残疾对其一生的生活质量产生毁灭性影响，仅在美国，每年就会报告约 800,000 例新中风病例，恢复手臂肢体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了可喜的成果，但目前可用的机器人和外骨骼平台价格昂贵，需要技术人员和庞大的临床空间。此外，当前的机器人控制算法在说服和参与训练循环方面效率不高，该奖项支持开发嵌入创新型便携式外骨骼平台的创新自适应算法，用于中风患者的手臂肢体训练。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。该项目的目标是完成的通过三个研究重点：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；3）验证所提出的中风康复平台。在该项目中，将开发一种新型、多模式、便携式平面机器人训练平台，该平台具有以用户为中心的便利设计，以克服平移障碍和通过利用共享控制理论并使用新颖的自适应闭环卡尔曼滤波器算法，将开发一种自适应且意图驱动的康复算法，以帮助中风患者的手臂肢体恢复。这项研究工作不仅将促进对多模式皮质肌肉活动在其中的作用的基本理解。手臂肢体的规划和执行，同时还产生新的自适应康复算法，以加速受影响的中风人群的运动恢复。该项目由残疾与康复工程计划 (DARE) 和刺激竞争性研究既定计划 (EPSCoR) 共同资助）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。