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A review of the key technologies for sEMG-based human-robot interaction systems

机译:基于Semg的人机交互系统关键技术综述

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As physiological signals that are closely related to human motion, surface electromyography (sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some reviews on myoelectric control systems have focused on signal processing, feature extraction and pattern recognition methods. However, to the best of our knowledge, there is no review paper on the comprehensive description of HRISs based on sEMG. Therefore, in this paper, we present a detailed review of the key technologies related to the use of sEMG signals in HRISs. First, the advantages and difficulties of using sEMG signals in HRISs are summarized, and some typical applications are introduced. Second, the research status and existing problems are discussed for four key technologies related to sEMG-based HRISs: signal preprocessing and feature extraction, human motion intention recognition, robustness enhancement for sEMG pattern recognition, and multisource information fusion based on sEMG. Finally, we discuss the bottlenecks hindering the application of sEMG-based HRISs and present prospects for this currently active research area. (C) 2020 Elsevier Ltd. All rights reserved.
机译:作为与人类运动密切相关的生理信号,表面肌电图(SEMG)信号已广泛用于人机交互系统(HRIS)。在肌电控制系统上的一些评论集中于信号处理,特征提取和模式识别方法。然而,据我们所知,基于SEMG的HRISS的综合描述没有审查纸张。因此,在本文中,我们对HRIS中使用SEMG信号的关键技术进行了详细的审查。首先,总结了在HRIS中使用SEMG信号的优点和困难,并引入了一些典型的应用。其次,讨论了与Semg的HRIS相关的四个关键技术讨论了研究现状和现有问题:信号预处理和特征提取,人工运动意图识别,SEMG模式识别的鲁棒性增强,以及基于SEMG的多源信息融合。最后,我们讨论了阻碍了Semg为基于Semg的HRIS的瓶颈,并为此目前活跃的研究区域的应用前景。 (c)2020 elestvier有限公司保留所有权利。

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