Targeted Reinnervation and Pattern-Recognition Control for Transradial Amputees

经桡动脉截肢者的有针对性的神经再生和模式识别控制

• 批准号: 7599641
• 负责人: Todd Kuiken
• 金额: $ 53.96万
• 依托单位: REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7599641
• 关键词: Algorithms; Amplifiers; Amputation; Amputees; Area; Artificial Arm; Arts; Benchmarking; Biological; Caring; Characteristics; Chicago; Classification; Clinical; Communities; Complement; Complex; Data; Development; Elbow; Electrodes; Ensure; Environment; Esthesia; Feedback; Fingers; Forearm; Foundations; Freedom; Future; Hand; Home environment; Hour; Individual; Institutes; Institution; Laboratories; Lead; Limb Prosthesis; Limb structure; Motor; Movement; Muscle; Myoelectric prosthesis; Nerve; Nerve Transfer; New Brunswick; Operative Surgical Procedures; Patients; Pattern Recognition; Performance; Persons; Physiology; Positioning Attribute; Procedures; Process; Prosthesis; Reaction Time; Rehabilitation therapy; Research; Residual state; Rotation; Science; Sensory; Series; Signal Transduction; Skin; System; Techniques; Technology; Testing; Thumb structure; Time; Training; Universities; Upper Extremity; Upper arm; Work; Wrist; base; clinical application; computerized data processing; design; disability; electric impedance; evaluation/testing; experience; graphical user interface; grasp; improved; improved functioning; innovation; motor control; new technology; novel; public health relevance; reinnervation; relating to nervous system; research clinical testing; research study; sensory feedback; standard of care; theories; tool

DESCRIPTION (provided by applicant): We propose to provide a set of clinically viable tools and procedures to substantially improve the control of myoelectric prostheses by transradial amputees. This proposal includes a pattern-recognition control package based on algorithms with demonstrated efficacy, an innovative new surgical technique for patients with transradial amputation that will substantially improve their ability to control prostheses and receive sensation feedback from them, and advanced pattern-recognition tools that will lead to even more robust adaptive control. In this project we will perform a series of tests that will improve the control of transradial prostheses using pattern recognition techniques. Subjects will be allowed to take these prostheses home for a month-long trial, to observe and rectify any remaining problems. Targeted Muscle Reinnervation (TMR) will be performed on six subjects. TMR is a new surgical technique that transfers amputated nerve to spare muscle and skin. It provides new myoelectric signals allowing intuitive and simultaneous control for improved function in amputees TMR will improve the accuracy of control, the number of classes that may be robustly controlled, and potentially allow for simultaneous, independent control of the hand and wrist. TMR allows for more natural control of the prosthesis, and also provides targeted sensory feedback, in which the subject feels their amputated hand through reinnervated skin on the residual limb. These two surgical procedures will greatly improve the function of transradial prostheses. TMR subjects will also undergo a field trial. Finally, adaptive pattern recognition techniques and parallel classifier technology will be investigated. Adaptive control may be crucial to clinical robustness from day to day as the user adapts to the classifier. Parallel control will allow subjects to simultaneously control wrist and hand classes, with high accuracy. This proposal will advance several areas of science, including pattern recognition and the physiology of reinnervation. Parallel classifiers and adaptive algorithm theory will be substantially developed beyond the current state of the art, and the concept of robustness in the absence of a known class will be explored in the context of electromyographic signals. This proposal will also advance our understanding of motor control as we implement these novel control techniques, and provide support for future experiments which will further develop our understanding in both motor and sensory reinnervation. An outstanding team has been assembled including the Rehabilitation Institute of Chicago, the University of New Brunswick, and Otto Bock, Inc. We believe the proposed research will advance the standard of care of persons with amputation. It will also serve as an important research platform for continuing to improve artificial limb function. PUBLIC HEALTH RELEVANCE. This project will apply an innovative surgery technique to subjects with transradial amputation, to improve control of their prosthesis. It will also develop new technologies to advance the control of prostheses that have more functions including wrist rotation, wrist flexion/extension and hands with moving fingers and thumbs. These studies will significantly improve the function of artificial arms for people with below elbow amputations.

描述（由申请人提供）： 我们建议提供一套临床上可行的工具和程序，以大大提高经桡截肢者的肌电假肢的控制。该提案包括一个模式识别控制包，该控制包基于已证明有效的算法，一种用于经桡动脉截肢患者的创新手术技术，该技术将大大提高他们控制假体的能力并从假体中接收感觉反馈，以及先进的模式识别工具，该工具将导致更强大的自适应控制。在本项目中，我们将进行一系列测试，这些测试将使用模式识别技术改善经桡动脉假体的控制。受试者将被允许将这些假肢带回家进行为期一个月的试验，以观察和纠正任何剩余的问题。将对6名受试者进行靶向肌肉神经再支配（TMR）。TMR是一种新的外科技术，它将切断的神经转移到肌肉和皮肤上。它提供了新的肌电信号，允许直观和同时控制，以改善截肢者的功能。TMR将提高控制的准确性，可以鲁棒控制的类别数量，并可能允许同时独立控制手和手腕。TMR允许对假肢进行更自然的控制，并且还提供有针对性的感觉反馈，其中受试者通过残肢上的重新神经支配的皮肤感觉到他们的截肢手。这两种手术方法将大大改善经桡动脉假体的功能。TMR受试者还将接受田间试验。最后，将研究自适应模式识别技术和并行分类器技术。自适应控制可能是至关重要的临床鲁棒性从一天到一天的用户适应分类器。平行控制将允许受试者同时控制手腕和手类，具有高精度。这一提议将推进几个科学领域，包括模式识别和神经再生生理学。并行分类器和自适应算法理论将大大发展超出目前的技术水平，并在没有一个已知的类的鲁棒性的概念将在肌电图信号的上下文中进行探讨。这项建议也将推进我们的理解，我们实现这些新的控制技术，运动控制，并为未来的实验，这将进一步发展我们的理解，在运动和感觉神经再支配提供支持。一个杰出的团队已经组装，包括康复研究所的芝加哥，新玩法大学，和奥托博克公司。我们相信，拟议的研究将提高截肢者的护理标准。它还将作为继续改善假肢功能的重要研究平台。 公共卫生相关性。该项目将应用一种创新的手术技术，以改善经桡动脉截肢的受试者对假体的控制。它还将开发新技术，以推进具有更多功能的假肢的控制，包括手腕旋转，手腕弯曲/伸展以及具有移动手指和拇指的手。这些研究将显著改善肘下截肢者的假肢功能。