Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition

Volume: 9
Published: Oct 29, 2015
Abstract
Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation...
Paper Details
Title
Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition
Published Date
Oct 29, 2015
Volume
9
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.