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NRI: Small: EEG and EMG Human Model-Based Adaptive Control of a Dexterous Artificial Hand

NRI: Small: EEG and EMG Human Model-Based Adaptive Control of a Dexterous Artificial Hand
NRI:小型:基于脑电图和肌电图人体模型的灵巧假手自适应控制
批准号:
1317952
负责人:
Erik Engeberg
金额:
$25.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-15 至 2015-06-30
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项目摘要

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中文摘要
翻译
该奖项的研究目标是探索上肢截肢者用脑波控制灵巧假手的不同方法。生物医学信号处理技术将被开发来实现这一点,将一个单一的小记录电极非侵入性地放置在截肢者的头部。记录下来的脑电波将被无线实时传输到手中。将开发一个顶级控制器来解释截肢者的意图,而一个低级控制器将用于同步假手的灵巧抓取动作。还将开发使用指尖的触觉反馈的算法,以自动防止抓到的物体在运输或干扰时意外掉落。截肢者将参与一项研究,将新开发的脑波人工手控制技术与日常生活中常见任务中使用肌肉信号的传统控制技术进行比较。如果成功,这项研究将为截肢者提供一种非侵入性的、经济的方法来控制脑波灵巧的假手。这可以极大地改善许多截肢者的假手功能。最小硬件的使用将促进这项技术的临床采用,自主的低级控制算法将产生一个脑机接口,给操作员带来较低的认知负担。这项研究也可以很容易地应用于使许多残疾人受益,包括中风患者和四肢瘫痪患者,并可以对机器人的其他领域产生积极影响,如简易爆炸解除武装、水下和空间探索以及救援机器人。未被充分代表的工科学生将从这项研究计划中受益。此外,本科生和工程学研究生将受益于这项研究产生的新开发的课程和实验室练习。
英文摘要
The research goal of this award is to explore different methods for upper limb amputees to control dexterous artificial hands with brain waves. Biomedical signal processing techniques will be developed to enable this with a single, small recording electrode placed noninvasively on the amputees' heads. The recorded brain waves will be wirelessly transmitted to the hand in real time. A top-level controller will be developed to interpret the intent of the amputees while a low-level controller will be used to synchronize the dexterous grasp motions of the artificial hand. Algorithms will also be developed using tactile feedback from the fingertips to automatically prevent grasped objects from being accidentally dropped when they are transported or disturbed. Amputees will participate in a study to compare the newly developed artificial hand control techniques with brain waves to conventional control techniques with muscle signals during common tasks of daily life.If successful, this research will result in a noninvasive and economic method for amputees to control a dexterous artificial hand with brain waves. This could substantially improve the functionality of prosthetic hands for many amputees. The use of minimal hardware will facilitate the clinical adoption of this technique and the autonomous low-level control algorithms will produce a brain machine interface that places a low cognitive burden on the operator. This research can also be readily applied to benefit many disabled people including stroke victims and quadriplegics and can positively impact other areas of robotics such as improvised explosive disarmament, underwater and space exploration, and rescue robotics. Underrepresented engineering students will benefit from being included in this research plan. Additionally, undergraduate and graduate engineering students will benefit from newly developed classes and laboratory exercises resulting from this research.
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