Recognition of EMG signal patterns by neural networks

Y. Matsumura, Yasue Mitsukura, M. Fukumi, N. Akamatsu, Y. Yamamoto, K. Nakaura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

The 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 neural network learns FFT spectra to classify them. Moreover, we perform the principal component analysis using the simple principal component analysis before we perform recognition experiments. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages750-754
Number of pages5
Volume2
ISBN (Print)9810475241, 9789810475246
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 2002 Nov 182002 Nov 22

Other

Other9th International Conference on Neural Information Processing, ICONIP 2002
CountrySingapore
CitySingapore
Period02/11/1802/11/22

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Matsumura, Y., Mitsukura, Y., Fukumi, M., Akamatsu, N., Yamamoto, Y., & Nakaura, K. (2002). Recognition of EMG signal patterns by neural networks. In ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (Vol. 2, pp. 750-754). [1198158] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1198158