论文标题
基于手势分类的多元同步转换的时频特征的统计分析
Statistical Analysis of Time-Frequency Features Based On Multivariate Synchrosqueezing Transform for Hand Gesture Classification
论文作者
论文摘要
在这项研究中，四个联合时频（TF）矩；从多元同步Queezing Transform（MSST）获得的TF矩阵的均值，方差，偏度和峰度被提议作为手势识别的特征。使用了40名受试者的表面EMG（SEMG）信号的公开数据集，使用了10个手势。根据Kruskal-Wallis（KW）测试获得的P值变量的特征变量的区别能力。可以得出结论，TF矩阵的均值，方差，偏度和峰度可能是识别手势的候选特征集。
In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing 10 hand gestures, was used. The distinguishing power of the feature variables for the tested gestures was evaluated according to their p values obtained from the Kruskal-Wallis (KW) test. It is concluded that the mean, variance, skewness, and kurtosis of TF matrices can be candidate feature sets for the recognition of hand gestures.
