Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control.
Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control.
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DOI：
10.1097/jpo.0b013e318289950b
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发表时间：
2013-04-01
期刊：
影响因子：
--
通讯作者：
Englehart K
中科院分区：
文献类型：
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作者：
Scheme E;Englehart K
基于模式识别的肌电控制的性能多年来一直受到研究界的极大关注。由于最近在灵巧的假肢设备的发展激增，确定多功能肌电控制的临床可行性已成为至关重要的。有几个因素导致了离线分类准确性和临床可用性之间的差异，但最重要的主题是，在功能使用期间，引起的模式的可变性大大增加。比例控制已被证明可以大大提高传统肌电控制系统的可用性。通常，肌电图的幅度的测量（经校正和平滑的版本）用于指示设备的控制速度。然而，肌电模式分类器的辨别能力也主要基于肌电图的幅度特征。这项工作提出了一个介绍性的收缩强度和比例控制模式识别为基础的控制效果。这些影响进行了研究，使用典型的模式识别数据收集方法，以及实时位置跟踪测试。训练与动态力变化收缩和适当的增益选择显着提高（p<0.001）的分类器的性能和容差比例控制。
The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases greatly during functional use. Proportional control has been shown to greatly improve the usability of conventional myoelectric control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of control of a device. The discriminatory power of myoelectric pattern classifiers, however, is also largely based on amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional control on pattern recognition based control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p<0.001) the classifier’s performance and tolerance to proportional control.