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使用细钢丝肌内电极对目标外在肌肉进行灵巧的假手控制。

Dexterous control of a prosthetic hand using fine-wire intramuscular electrodes in targeted extrinsic muscles.

作者信息

Cipriani Christian, Segil Jacob L, Birdwell J Alex, ff Weir Richard F

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):828-36. doi: 10.1109/TNSRE.2014.2301234. Epub 2014 Jan 21.

Abstract

Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human-machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.

摘要

恢复截肢后等同于人类手部的灵巧运动功能是康复工程的主要目标之一。要实现这一目标,需要建立一个轻松的人机接口,将人造手与意志来源相连接。尝试利用神经信号并将其用作神经假体的控制输入,在神经肌肉系统中的侵入性和层次位置各不相同。然而时至今日,临床上主要可行的控制技术是通过表面电极在外周测量的肌电图。这种方法既不符合生理要求,也不够灵巧,因为无法实现任意手指运动或手部姿势。在此,我们展示了在健康受试者身上使用肌内电极直接从前臂肌肉信号实现对多指假肢进行实时、连续和同步控制的可行性。受试者收缩生理上合适的肌肉,独立控制一个物理机器人手手指的四个自由度。受试者称这种控制直观,并展示了在没有明确训练的情况下将手驱动到12种姿势的能力。这是第一项实时处理外周神经关联并以直观、直接的方式同时控制物理手多个手指的研究。

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