Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers
Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers
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DOI：
10.1056/nejmoa1300126
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发表时间：
2013-09-26
影响因子：
158.5
通讯作者：
Kuiken, Todd A.
中科院分区：
文献类型：
--
作者：
Hargrove, Levi J.;Simon, Ann M.;Kuiken, Todd A.
由于缺乏鲁棒控制策略，机器人技术在动力膝关节和踝关节假肢中的临床应用受到限制。我们发现，使用肌电图（EMG）信号来自天然神经支配和手术再神经支配的大腿残余肌肉的患者进行了膝关节截肢，改善了机器人假肢的控制。用模式识别算法解码肌电图信号，并结合假体上传感器的数据来解释患者的预期动作。这为行走提供了强大而直观的控制——在平地、楼梯和坡道上行走之间的无缝转换——以及当患者坐着时重新定位腿部的能力。
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation — with seamless transitions between walking on level ground, stairs, and ramps — and of the ability to reposition the leg while the patient was seated.