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.

中文翻译：

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基于多元同步压缩变换的手势分类时频特征统计分析

在这项研究中，四个联合时频（TF）矩；提出了从多元同步压缩变换 (MSST) 获得的 TF 矩阵的均值、方差、偏度和峰度作为手势识别的特征。使用了一个公开可用的数据集，其中包含 40 名受试者执行 10 个手势的表面 EMG (sEMG) 信号。根据从 Kruskal-Wallis (KW) 测试中获得的 p 值评估测试手势的特征变量的区分能力。得出的结论是TF矩阵的均值、方差、偏度和峰度可以作为手势识别的候选特征集。