增加运动类数时肌电模式识别技术的性能评价

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引用次数: 0

摘要

在过去几年的肌电控制领域的研究中,许多研究人员提出了几种模型,利用不同的特征和分类器的组合来增加运动类别,但这些工作都未能解释多类别分类与其准确性之间是否存在任何相关性。本文的重点是寻找决定机器学习算法能够准确区分的运动类限制的因素,并在保持系统简单性的同时增加运动类的数量,使用表面肌电信号评估模式分类技术的性能。通过4种分类器(支持向量机(SVM)、k -近邻(K-NN)、决策树(DT)和Naïve贝叶斯(NB)),使用7个独立的时域特征和4个特征集组合,获得了8通道表肌电信号的结果。然后以5、7、10、12、15个类别的方式增加类别的数量,以确定具有上述特征的表面肌电信号系统能够有效分类的运动类别的最高数量。并观察了增加运动类数对系统精度的影响。使用MFL特征的SVM获得了所有五个类进展的最高精度,对于使用mav的DT,成功地观察到NB分类器对于本工作中使用的特征具有最小的性能损耗
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes
:In the past few years of research done in the field of myoelectric control, many researchers have proposed several models imploying a combination of different features and classifiers to increase the movement classes, but all that work fails to explain if there is any correlation between multi-class classification and its accuracy. This paper focuses on finding the factors that decide the limit of movement classes that machine learning algorithms can accurately differentiate and to evaluate the performance of pattern classification techniques using the sEMG signal when the number of movement classes is increased while keeping the simplicity of the system. The results were obtained for eight channels sEMG signal using 7 independent time-domain features and four feature set combinations over 4 classifiers (Support Vector Machine(SVM), K-Nearest Neighbour(K-NN), Decision Tree(DT), and Naïve Bayes(NB)). Then the number of classes was increased in the manner of 5, 7, 10, 12, and 15 classes to determine the highest number of movement classes that the sEMG system with above-described features can classify efficiently. And the effect of increasing the number of movement classes on system accuracy was observed. The highest accuracies for all five class progression were obtained for SVM with the MFL feature, and for DT using MAV, it was successfully observed that the NB classifier had minimum performance depletion for the features used in this work
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