NRI:BMI Control of a Therapeutic Exoskeleton

描述(由申请人提供)：这项研究旨在通过利用患者意图和实时损害评估的好处来加速中风后机器人康复的发展、疗效和使用。验证将使用德克萨斯州休斯敦康复研究所(TIRR)的MAHI EXO-II外骨骼机器人进行。机器人康复是中风患者进行感觉运动训练的有效平台。机器人设备能够准确定位受损的肢体，同时提供帮助和阻力，并收集可用于表征患者运动质量的运动数据。MAHI EXO-II是一种物理人机接口，将使用非侵入性脑机接口(BMI)进行扩展，将患者纳入控制回路，从而使治疗变得积极，并使患者参与到康复任务中来，使患者参与到各种损伤严重程度的范围内。这种方法利用了在机器人康复的其他临床研究中观察到的患者意图启动运动的已知好处，以及使用BMI对皮质可塑性的有益影响。机器人测量 从MAHI EXO-II和BMI上的传感器获取的实时数据得出的运动障碍，将驱动针对患者的治疗过程，以适应患者的能力，由机器人提供帮助或适当地挑战参与者，以最大限度地提高康复效果。机器人康复中的按需协助模式已被证明是有效的；然而，这种模式是被动的，由尚未得到充分验证和核实的绩效指标驱动。此外，在治疗期间进行高强度的练习和持续的“挑战”被认为可以改善康复结果。主要贡献：1)将最先进的EEG-BMI方法应用于中风患者，并开发用于控制MAHI EXO-II的BMI，该方法将a)增加上肢功能，b)增进对大脑可塑性的了解，c)创新康复；2)为特定患者的治疗确定适当的运动障碍的机器人和电生理措施以及相关的控制算法；3)在试点研究中进行临床验证，以评估建议的方法。

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.