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.

翻译：在本研究中,提出了四个联合时间-频率(TF)时刻;从多变同步化变换(MSST)中获得的TF矩阵的平均值、差异、偏差和质变,作为手势识别的特征;使用了一套公开可得的数据集,其中包含40个对象的表面环球气球(SEMG)信号,有10个手势;根据Kruskal-Wallis(KW)测试获得的p值,对测试的手势特征变量的区别功率进行了评估;结论是,TF矩阵的平均值、差异、偏差和质变,可以成为确认手势的候选特征。