Abstract
Regarding some dexterous skill related to hands, for example, playing musical instruments, it is challenging for common beginners to imitate the motion of the teacher due to unfamiliarity with both the fingering and the music piece. To assist in solving that, we developed a wearable master-slave gesture learning system based on functional electrical stimulation (FES), where a multi-pad FES system with an electrode array (20 pads) was utilized to stimulate the target muscle group accurately. In terms of determining stimulation parameters, we designed a process and used a wearable surface-electromyography (sEMG) acquisition device, SJU-iMYO, to obtain sEMG signal of associated muscles on the master side (teacher); Afterwards we used an algorithm to determine the electrode set to activate and the correspondent current intensity on the slave side (student) automatically. To assess the performance of the device, several experiments were conducted on four subjects with a virtual-reality data glove. All results indicated that the gesture learning system succeeded in imitating the motion of each finger with fairly good accuracy, except for the little finger.
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Chen, K., Zhang, B., Zhang, D. (2014). Master-Slave Gesture Learning System Based on Functional Electrical Stimulation. In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds) Intelligent Robotics and Applications. ICIRA 2014. Lecture Notes in Computer Science(), vol 8917. Springer, Cham. https://doi.org/10.1007/978-3-319-13966-1_22
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DOI: https://doi.org/10.1007/978-3-319-13966-1_22
Publisher Name: Springer, Cham
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