A wavelet-based continuous classification scheme for multifunction myoelectric control
A wavelet-based continuous classification scheme for multifunction myoelectric control
复制标题
DOI：
10.1109/10.914793
复制
发表时间：
2001-03-01
影响因子：
4.6
通讯作者：
Parker, PA
中科院分区：
文献类型：
--
作者：
Englehart, K;Hudgins, B;Parker, PA
这项工作代表了一项正在进行的研究，即使用肌电信号来灵活和自然地控制受动力的上肢。当作为模式识别问题来处理时，肌电控制方案的成功在很大程度上取决于分类精度。描述了一种新的方法，该方法比以前的工作表现出更高的准确性。这种方法成功的基础是使用了基于小波的特征集，通过主成分分析进行了降维。进一步研究表明，与单通道或双通道相比，四个通道的肌电数据显著提高了分类精度。这表明，使用稳态肌电信号可以实现异常准确的性能。利用这些成功，构建了一个健壮的在线分类器，它对连续的数据流产生类决策。尽管该方案尚处于初步开发阶段，但与基于离散、瞬时活动爆发的方案相比，该方案有望成为一种更自然、更有效的肌电控制手段。
This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.