NRI:BMI Control of a Therapeutic Exoskeleton

NRI：治疗性外骨骼的 BMI 控制

• 批准号: 8877650
• 负责人: Jose Luis Contreras-Vidal
• 金额: $ 28.51万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8877650
• 关键词: Accounting; Algorithms; Brain; Clinical; Clinical Research; Collection; Data; Development; Electroencephalography; Foundations; Functional Magnetic Resonance Imaging; Goals; Health; Human; Impairment; Individual; Institutes; Learning; Limb structure; Longitudinal Studies; Measures; Methods; Motion; Motor; Movement; Neurologic; Participant; Patients; Performance; Persons; Pilot Projects; Positioning Attribute; Rehabilitation Outcome; Rehabilitation Research; Rehabilitation therapy; Research; Resistance; Robot; Robotics; Severities; Stroke; Survivors; Technology; Texas; Therapeutic; Time; Training; Translating; United States; Upper Extremity; Validation; base; brain machine interface; chronic stroke; clinically relevant; cohort; disability; exoskeleton; improved; innovation; motor function recovery; motor impairment; motor recovery; novel; physical conditioning; relating to nervous system; robot exoskeleton; robot interface; robot rehabilitation; robotic device; sensor; stroke rehabilitation

DESCRIPTION (provided by applicant): This research aims to accelerate the development, efficacy and use of robotic rehabilitation after stroke by capitalizing on the benefits of patient intent and real-time assessment of impairment. Validation will occur using the MAHI EXO-II exoskeleton robot at The Institute for Rehabilitation and Research (TIRR) in Houston, Texas. Robotic rehabilitation is an effective platform for sensorimotor training in stroke patients. A robotic device enables accurate positioning of the impaired limb while simultaneously providing assistance & resistance forces and collection of motion data that can be used to characterize the quality of the patient's movements. The MAHI EXO-II, a physical human-robot interface, will be augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. This approach capitalizes on the known benefits of patient intent in movement initiation observed in other clinical studies of robotic rehabilitation and on the beneficial effects of BMI use on cortical plasticity. Robotic measures of motor impairment, derived from real-time data acquired from sensors on the MAHI EXO-II and from the BMI, will drive patient-specific therapy sessions adapted to the capabilities ofthe individual, with the robot providing assistance or challenging the participant as appropriate, in order to maximize rehabilitation outcomes. Assist-as-needed paradigms in robotic rehabilitation have been shown to be efficacious; however, such paradigms are passive and driven by performance metrics that have not been sufficiently validated and verified. Additionally, intense practice and continual 'challenge' during therapy is known to improve rehabilitation outcomes. Key contributions: 1) Adapting most advanced EEG- BMI methods to stroke patients and developing a BMI for the control of the MAHI EXO-II that will a) increase upper limb function, b) advance understanding of brain plasticity, and c) innovate rehabilitation; 2) Determining appropriate robotic and electrophysiological measures of motor impairment and associated control algorithms for patient-specific therapy; and 3) Clinical validation in pilot studies to evaluate the proposed approach.

描述（由申请人提供）：这项研究旨在通过利用患者意图的益处和实时评估损害的益处来加速中风后机器人康复的开发，功效和使用。使用德克萨斯州休斯敦的康复与研究研究所（TIRR）的Mahi Exo-II外骨骼机器人进行验证。机器人康复是中风患者的感觉运动训练的有效平台。机器人设备可以准确地定位受损的肢体，同时提供辅助和阻力和运动数据的收集，可用于表征患者运动的质量。 Mahi Exo-II是一种物理人类机器人界面，将通过非侵入性的脑机界面（BMI）进行增强，以将患者纳入对照环中，从而使治疗“活跃”并在康复任务中引起多种受损严重性的患者。这种方法利用了患者意图在机器人康复的其他临床研究以及BMI使用对皮质可塑性的有益作用中观察到的已知好处。机器人措施 运动障碍是从Mahi Exo-II和BMI的传感器中获得的实时数据衍生而来的，将推动适合个人能力的患者特定治疗课程，并提供适当的援助或挑战参与者，以最大程度地提高康复疗法。在机器人康复中需要辅助的范式有效。但是，这种范式是被动的，并且受尚未得到充分验证和验证的性能指标的驱动。此外，众所周知，在治疗过程中，强烈的实践和持续的“挑战”可以改善康复结果。主要贡献：1）调整最先进的EEG-BMI方法来抚摸患者并开发BMI以控制Mahi Exo-II，该方法将a）提高上肢功能，b）提高对脑可塑性的了解，c）c）创新的康复； 2）确定针对患者特异性治疗的运动障碍和相关控制算法的适当机器人和电生理测量； 3）试点研究中的临床验证，以评估所提出的方法。