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Towards the reduction of the effects of muscle fatigue on myoelectric control of upper limb prostheses

机译:为了减少肌肉疲劳对上肢假体肌电控制的影响

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This paper presents an investigation focused on the impact of muscle fatigue on a pattern recognition scheme for myoelectric control that uses three features sets and a Linear Discriminant Analysis classifier.Separability and repeatability between classes were used to evaluate the features changes while muscle fatigue was induced.Results show that while muscle fatigue is increasing over time, both separability and repeatability of the classes decrease.Finally two training schemes that use data acquired under fatigue, multiconditional training and selective classification, were evaluated using the Total Error Rate (TER).Results indicate that, when LDA classifier was trained whit no-fatigue, moderated fatigue and fatigue data, TER decreased to moderated and fatigue data, but increased to no-fatigue data.On the other hand, using three LDA classifiers to each of the condition, TER decreased to 9.26 % and 11 % in moderated fatigue and fatigue cases, while no-fatigue case was not affected.
机译:本文提出了一项针对肌肉疲劳对肌电控制模式识别方案的影响的研究,该方案​​使用三个特征集和一个线性判别分析分类器,使用类别之间的可分离性和可重复性来评估诱发肌肉疲劳时的特征变化。结果表明,虽然肌肉疲劳随着时间的推移而增加,但类别的可分离性和可重复性均下降。最后,使用总错误率(TER)评估了两种使用疲劳下获得的数据的训练方案,多条件训练和选择性分类。当训练LDA分类器无疲劳,中度疲劳和疲劳数据时,TER降低至中度和疲劳数据,但增加至无疲劳数据;另一方面,对每个条件使用三个LDA分类器TER在中度疲劳和疲劳情况下,分别降至9.26%和11%,而无疲劳情况则不受影响。

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