Adaptive Smart Controller For Brain-Prosthetic Hand Interface

用于脑假手接口的自适应智能控制器

• 批准号: 7329429
• 负责人: Samuel Thomas Clanton
• 金额: $ 4.6万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7329429
• 关键词: Algorithms; Behavior; Brain; Compatible; Computer software; Data; Development; Devices; Elements; Ensure; Environment; Fingers; Fire - disasters; Freedom; Goals; Hand; Injury; Lead; Life; Limb Prosthesis; Measures; Motor Cortex; Neurodegenerative Disorders; Patients; Physiological; Pliability; Population; Positioning Attribute; Primary Lateral Sclerosis; Primates; Prosthesis; Psychophysiology; Public Health; Research; Robot; Robotics; Shapes; Signal Transduction; Spinal cord injury; System; Testing; Therapeutic; Time; United States; Upper Extremity; Upper arm; Work; clinical application; computerized data processing; functional restoration; grasp; human study; improved; kinematics; loss of function; nervous system disorder; neural prosthesis; neuroregulation; relating to nervous system; research study; vector

DESCRIPTION (provided by applicant): Over 130,000 people in the United States live with partial or complete loss of function of the arm and hand due to injury or neurodegenerative disease. Neural prosthetic approaches could lead to therapeutic devices that have the potential to restore function and independence to this patient population. Our work is in neural prosthetic interfaces that process signals from the motor cortex to control robotic arms and hands. While much progress has been made in the neural control of a prosthetic arm, the complexities inherent in the control of the hand make the development of a neural prosthetic hand interface difficult. Current work is in the development of a neural prosthetic robot hand controller. Our aim is that it will have the flexibility to adapt to variable neural control signals while employing an automated grasp planning system to ensure stable grasping. This system will act as a bridge to complete neural control of the hand while having immediate clinical application in improving current upper limb prostheses. Specific Aim 1: To develop a robotic neural prosthetic hand control system that dynamically integrates elements of a sophisticated automated grasp planning algorithm with grasp-related information derived from neural firing in the motor cortex. Data from past human studies and current primate psychophysical experiments will be used to characterize controlled features of grasping, such as kinematic coordination of the fingers and measures of manipulability of objects with achieved grasps. Recordings from the motor cortex during grasping will be used to characterize correlation of neural firing with hand position, orientation, and shaping using population vectors, an approach used currently for the neural control of a robot arm. This data will be integrated into an existing grasp planning system to focus the search space for planning successful grasping in a way that is compatible with observed physiologic behavior and neural signal-derived intent. Specific Aim 2: To test the hypothesis that a smart hand control system can allow a primate to perform cortically controlled complete reaching and grasping tasks. The grasp planning system will be integrated into an existing setup for performing on-line neural control of an upper limb prosthetic, in which primates will perform neurally controlled robotic grasping tasks. Relation to Public Health: Neural prosthetics have the potential to help people with spinal cord injury and other neurologic disorders achieve a greater degree of independence and control of their lives. Our goal is to create a control system for a neural prosthetic hand that allows natural and effective grasping.

描述（由申请人提供）： 在美国，超过13万人由于受伤或神经退行性疾病而部分或完全丧失手臂和手的功能。神经假体方法可能会导致治疗设备，有可能恢复功能和独立性，这一患者群体。我们的工作是神经假肢接口，处理来自运动皮层的信号，以控制机器人手臂和手。虽然已经取得了很大的进展，在假肢的神经控制，手的控制固有的复杂性，使神经假肢手接口的发展困难。目前的工作是在神经假肢机器人手控制器的发展。我们的目标是，它将具有适应可变神经控制信号的灵活性，同时采用自动抓取规划系统，以确保稳定的抓取。该系统将作为完成手部神经控制的桥梁，同时在改善当前上肢假肢方面具有直接的临床应用。 具体目标1：开发一种机器人神经假手控制系统，该系统动态地将复杂的自动抓取规划算法的元素与来自运动皮层神经放电的抓取相关信息相结合。 从过去的人类研究和目前的灵长类动物的心理物理实验的数据将被用来表征控制功能的把握，如手指的运动协调和措施的可操作性的对象与实现的把握。在抓握过程中来自运动皮层的记录将用于表征神经放电与手的位置、方向和使用群体向量的形状的相关性，目前用于机器人手臂的神经控制的方法。这些数据将被集成到现有的抓取规划系统中，以集中搜索空间，从而以与观察到的生理行为和神经信号兼容的方式规划成功的抓取。衍生意图。 具体目标二：为了检验假设，智能手控制系统可以让灵长类动物执行皮质控制的完整的达到和抓取任务。 把握规划系统将被集成到现有的设置进行在线神经控制的上肢假肢，其中灵长类动物将执行神经控制的机器人抓取任务。与公共卫生的关系：神经修复术有可能帮助脊髓损伤和其他神经系统疾病患者实现更大程度的独立和控制他们的生活。我们的目标是为神经假手创建一个控制系统，使其能够自然有效地抓握。