Adjacent Features for High-Density EMG Pattern Recognition
Adjacent Features for High-Density EMG Pattern Recognition
复制标题
高密度 EMG 模式识别的相邻特征
DOI：
10.1109/embc.2018.8513534
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Zhang, Xiaorong
中科院分区：
文献类型：
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作者：
Donovan, Ian M.;Okada, Kazunori;Zhang, Xiaorong
在基于肌电(EMG)的模式识别(PR)的初期，有限数量的电极通道被解剖地放置在感兴趣的肌肉上。现代方法已经表明，肢体周围规则间隔的电极同样有效，并已在消费者就绪的肌电控制系统中得到演示，例如Thalic Labs的Myo臂章。除了线阵，栅格阵列也被应用于这一领域的研究。虽然电极阵列主要是为了简化放置，但在这项工作中还将利用其他好处。本文提出了一种新的时空特征集，它分别分析被测电信号的强度和结构，并评估相邻电极之间的相似性，因此被称为相邻特征(AF)。本文的结果表明，对于47种手势和手腕手势，AF的分类精度比自回归(AR)系数和Hudgins的时间域(TD)特征高约4%-6%，而计算简单性与TD特征相似。
In the infancy of electromyography (EMG) based pattern recognition (PR) limited numbers of electrode channels were anatomically placed over muscles of interest. Modern methods have shown that regularly spaced electrodes around the circumference of a limb are equally effective and have been demonstrated in consumer-ready myoelectric control systems such as Thalmic Labs' Myo armband. In addition to linear arrays, grid arrays have also been applied in this field of research. Although electrode arrays have mainly been adopted to simplify placement, other benefits will be exploited in this work. Presented in this paper is a novel spatial-temporal feature set that separately analyzes the intensity and structure of the measured electrical signals (MES) and evaluates the similarities between adjacent electrodes, hence the name Adjacent Features (AF). Results in this paper show that AF produced classification accuracies about 4%-6% greater than autoregression (AR) coefficients and Hudgins' time-domain (TD) features for classifying 47 hand and wrist gestures, while having a computational simplicity similar to the TD features.