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This paper discusses the recovery of missing data in surface Electromyography (sEMG) signals that arise during the acquisition process. Missing values in EMG signals occur due to either disconnection of electrodes, artifacts, muscle fatigue or incapability of instruments to collect very low amplitude signals. In many real-world EMG related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employdoi:10.1109/access.2019.2931371 fatcat:qeqnpkk76zguhnbwxvg5m6mnmu