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The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularlydoi:10.3390/s20174892 pmid:32872508 pmcid:PMC7506981 fatcat:dgxskbldhza3xbjxeisf7mn5pe