Preliminary Study of Virtual sEMG Signal-Assisted Classification

Abstract

Surface electromyography (sEMG) is widely used in various fields to analyze user intentions. Conventional sEMG based classifications are electrode-dependent; thus, trained classifiers cannot be applied to other electrodes that have different parameters. This defect degrades the practicability of sEMG based applications. In this study, we propose a virtual sEMG signal-assisted classification to achieve electrode-independent classification. The virtual signal for any electrode configuration can be generated using muscle activation signals obtained from the proposed model. The feasibility of the virtual signal is demonstrated with regard to i) classifications using fewer sEMG channels by a pre-trained classifier without re-training and ii) electrode-independent classifications. This study focuses on preliminary tests of virtual sEMG signal-assisted classification. Future studies should consider model improvement and experiments involving more subjects to achieve plug-and-play classification.