Learn the Temporal-Spatial Feature of sEMG via Dual-Flow Network

Volume: 16, Issue: 04, Pages: 1941004 - 1941004
Published: Aug 1, 2019
Abstract
Surface electromyography (sEMG) signals have been widely used in human–machine interaction, providing more nature control expedience for external devices. However, due to the instability of sEMG, it is hard to extract consistent sEMG patterns for motion recognition. This paper proposes a dual-flow network to extract the temporal-spatial feature of sEMG for gesture recognition. The proposed network model uses convolutional neural network (CNN)...
Paper Details
Title
Learn the Temporal-Spatial Feature of sEMG via Dual-Flow Network
Published Date
Aug 1, 2019
Volume
16
Issue
04
Pages
1941004 - 1941004
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.