Recognition of EMG signal patterns by neural networks

Yuji Matsumura, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, Fumiaki Takeda

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


This paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The NN learns FFT spectra to classify them. Moreover, we structuralized NN for improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

Original languageEnglish
Pages (from-to)623-630
Number of pages8
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2773 PART 1
Publication statusPublished - 2003 Jan 1
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 2003 Sept 32003 Sept 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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