Surface electromyography (sEMG) signals have been applied as control commands in numerous human robot interface systems and have been deployed for rehabilitation or clinical applications. Although lots of previous workers have tried to determine features appropriate for specific sEMG-signal classification problems, little of this work has involved deeply searching for the inner characteristics of the signals. In this study, we try to evaluate the properties of the transient state of sEMG signals on randomly m…
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