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经桡骨截肢者对多功能假肢的肌电图模式识别控制

EMG pattern recognition control of multifunctional prostheses by transradial amputees.

作者信息

Li Guanglin, Kuiken Todd A

机构信息

Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago and Department of Physical Medicine and Rehabilitation, Northwestern University, IL 60611, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6914-7. doi: 10.1109/IEMBS.2009.5333628.

DOI:10.1109/IEMBS.2009.5333628
PMID:19964455
Abstract

Electromyogram (EMG) pattern recognition approach has been investigated widely with able-bodied subjects for control of multifunctional prostheses and verified with high performance in identifying different movements. However, it remains unclear whether transradial amputees can achieve similar performance. In this study, we investigated the performance of EMG pattern recognition control of multifunctional transradial prostheses in five subjects with unilateral below-elbow amputation. Testing results on both residual and intact arms showed that the average classification error (21%) of amputated arms for ten motion classes (four wrist movements, six hand grasps) and a 'no movement' class over all five subjects was about 15% higher than that of intact arms. For six basic motion classes (wrist flexion/extension, wrist pronation/supination, and hand open/close), the average classification error over all five subjects was about 7% from residual arms, which was similar to the result from intact arms (6%). Only six optimal electrode channels might be needed to provide an excellent myoelectric control system for the six basic movements. These results suggest that the muscles in the residual forearm may produce sufficient myoelectric information to allow the six basic motion control, but insufficient information for more hand functions with fine finger movements.

摘要

肌电图(EMG)模式识别方法已在健全受试者中得到广泛研究,用于控制多功能假肢,并在识别不同运动方面得到了高性能验证。然而,经桡骨截肢者是否能达到类似的性能仍不清楚。在本研究中,我们调查了五名单侧肘下截肢受试者使用多功能经桡骨假肢的肌电图模式识别控制性能。对残肢和健侧手臂的测试结果表明,在所有五名受试者中,针对十个运动类别(四种腕部运动、六种手部抓握)和一个“无运动”类别,截肢手臂的平均分类误差(21%)比健侧手臂高出约15%。对于六个基本运动类别(腕部屈伸、腕部旋前/旋后以及手部开合),所有五名受试者残肢的平均分类误差约为7%,这与健侧手臂的结果(6%)相似。仅需六个最佳电极通道就能为这六个基本运动提供出色的肌电控制系统。这些结果表明,残肢前臂中的肌肉可能产生足够的肌电信息以实现六个基本运动控制,但对于更多具有精细手指运动的手部功能而言,信息则不足。

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