Deep learning for processing electromyographic signals: A taxonomy-based survey
Deep learning for processing electromyographic signals: A taxonomy-based survey
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DOI：
10.1016/j.neucom.2020.06.139
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发表时间：
2021-06-08
期刊：
影响因子：
6
通讯作者：
Bevilacqua, Vitoantonio
中科院分区：
文献类型：
--
作者：
Buongiorno, Domenico;Cascarano, Giacomo Donato;Bevilacqua, Vitoantonio
深度学习（DL）最近被用于构建智能系统，这些系统在图像识别、机器翻译和自动驾驶汽车等广泛的任务中表现出色。在一些领域，计算硬件的显著改进和对大数据分析的日益增长的需求推动了深度学习的工作。近年来，生理信号处理在深度学习中得到了很大的发展。总的来说，关于使用DL方法处理肌电图信号的研究数量呈指数增长。这种现象主要是由于目前肌电控制假体的局限性以及最近发布的大型肌电记录数据集，例如Ninapro。这种日益增长的趋势激发了我们寻找和回顾最近关于使用DL方法处理肌电信号的论文。参考Scopus数据库，对2014年1月至2019年3月期间发表的论文进行系统文献检索，经全文分析，筛选出65篇论文进行综述。文献计量学研究表明，根据EMG信号分析的最终应用，综述论文可分为四大类：手势分类、言语和情绪分类、睡眠阶段分类和其他应用。评审过程也证实了论文发表量的增加趋势，2018年发表的论文数量确实是前一年的四倍。正如预期的那样，大多数被分析的论文（= 60%）都关注手势的识别，从而支持我们的假设。最后，值得报告的是，卷积神经网络（CNN）是几个涉及深度学习架构中使用最多的拓扑，事实上，大约60%的评论文章考虑了CNN。(c) 2020 Elsevier B.V.版权所有
Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (= 60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN.(c) 2020 Elsevier B.V. All rights reserved.