Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees

Volume: 85, Pages: 51 - 68
Published: Jan 1, 2017
Abstract
The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically....
Paper Details
Title
Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees
Published Date
Jan 1, 2017
Volume
85
Pages
51 - 68
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.