PDF(Pubmed)
Abstract：
Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts-two factors that have been suggested to cause classification deterioration-affect classifiers\' performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14-21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes\' challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.
摘要：
基于模式识别（PR）的肌电控制系统可以自然地提供对上肢假肢的多功能和直观控制，并恢复失去的肢体功能，但是了解它们的稳健性仍然是一个悬而未决的科学问题。这项研究调查了肢体位置和电极移位-已提出的导致分类恶化的两个因素如何通过使用每个因素作为一个类别并计算可重复性和修改的可分离性指数来量化类别分布的变化来影响分类器的性能。十名肢体完整的参与者参加了这项研究。使用线性判别分析(LDA)作为分类器。结果证实了先前的研究，肢体位置和电极移位会降低分类性能（降低14-21％），因素之间没有差异（p>0.05）。当将肢体位置和电极移位视为类别时，我们可以对它们进行分类，单个和所有运动的准确率为96.13±1.44%和65.40±8.23%，分别。对五名截肢者的测试证实了上述发现。我们已经证明，每个因素都会引入特征空间中的变化，这些变化在统计上是新的类实例。因此，当在两个不同的肢体位置或电极移位中收集相同的运动时,特征空间包含两个统计上可分类的聚类。我们的结果是在理解PR方案对假肢肌电控制的挑战方面向前迈出了一步，需要对更多与截肢者相关的数据集进行进一步的验证。
