Pilot study on prediction of human hand configuration using transient state of surface-electromyography signals

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 mounted, dry-type electrodes and use this to rapidly predict three kinds of hand configurations - rock, scissors and paper motions. In experiments, subjects performed a rock-scissor-paper game with a virtual hand. For data acquisition, the sEMG signals were sampled at 1 kHz with eight-channel electrodes (wearable, dry type) that were randomly mounted on forearms [2]. The results verified that the proposed algorithm, using the property of the transient state of sEMG signals, works successfully.