Probabilistic Control of Functional Electrical Stimulation

功能性电刺激的概率控制

• 批准号: 7471566
• 负责人: ANDREW J FUGLEVAND
• 金额: $ 20.85万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7471566
• 关键词: Address; Algorithms; Animal Model; Animals; Attention; Behavior; Behavioral; Brain; Communication; Complex; Computer Interface; Computing Methodologies; Daily; Development; Effectiveness; Electric Stimulation; Electrodes; Employee Strikes; Fingers; Forelimb; Goals; Hand; Hand functions; Human; Implant; Implanted Electrodes; Individual; Joints; Link; Macaca mulatta; Methods; Modeling; Monkeys; Motor; Movement; Muscle; Orthopedics; Paralysed; Patients; Pattern; Physiologic pulse; Public Health; Pulse taking; Quadriplegia; Range; Self-control as a personality trait; Signal Transduction; Skeletal Muscle; Specific qualifier value; Spinal cord injury; Stroke; Surface; System; Testing; Time; Training; Upper Extremity; Upper arm; Upper limb movement; base; design; desire; grasp; improved; limb movement; neuroprosthesis; neurosurgery; restoration; robotic device

DESCRIPTION (provided by applicant): Functional electrical stimulation involves artificial activation of paralyzed muscles with implanted electrodes and has been used successfully to improve the ability of quadriplegics to perform movements important for daily activities. The range of motor behaviors that can be generated by functional electrical stimulation, however, is limited to a relatively small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit specified movements. We plan to use a probabilistic algorithm to predict the patterns of muscle activity associated with a wide range of upper limb movements based on hand trajectory information. The predicted patterns of muscle activity will then be transformed into amplitude-modulated trains of pulses and used to drive muscle stimulators in order to evoke movements in temporarily paralyzed animals. The evoked movements are quantitatively compared to the desired movements to evaluate the overall effectiveness of this approach. Ultimately, this probabilistic method could serve as the requisite interface between brain-derived trajectory information and existing functional electrical stimulation systems to realize a self-contained and self-controlled upper limb neuroprosthetic system. Such an integrated and flexible system would greatly increase movement capability, and independence, in paralyzed individuals. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop a new method to artificially activate and control paralyzed muscles with electrodes implanted in muscles. This effort will contribute to the restoration of voluntary limb movements in individuals paralyzed because of spinal cord injury or stroke.

描述（由申请人提供）：功能性电刺激涉及使用植入电极人工激活瘫痪肌肉，并已成功用于改善四肢瘫痪患者进行日常活动重要运动的能力。然而，功能性电刺激可以产生的运动行为的范围仅限于相对较小的一组预编程运动，例如手抓握和释放。由于与识别引起指定运动所需的肌肉刺激模式相关的实质性挑战，尚未实施更广泛的运动。我们计划使用概率算法来预测与基于手轨迹信息的大范围上肢运动相关的肌肉活动模式。然后，预测的肌肉活动模式将被转换成调幅脉冲串，并用于驱动肌肉刺激器，以唤起暂时瘫痪动物的运动。诱发的运动进行定量比较所需的运动，以评估这种方法的整体效果。最终，这种概率方法可以作为脑源性轨迹信息和现有功能性电刺激系统之间的必要接口，以实现自包含和自控制的上肢神经假体系统。这样一个集成和灵活的系统将大大提高瘫痪患者的运动能力和独立性。公共卫生相关性：该项目的目标是开发一种新方法，通过植入肌肉的电极人工激活和控制瘫痪的肌肉。这项工作将有助于恢复因脊髓损伤或中风而瘫痪的个人的自愿肢体运动。