Direct Estimation of Hand Motion Speed from Surface Electromyograms Using a Selective Desensitization Neural Network

• Horie Kazumasa

  University of Tsukuba

• Suemitsu Atsuo

  Japan Advanced Institute of Science and Technology

• Morita Masahiko

  University of Tsukuba

抄録

During the application of surface electromyograms (EMGs) in human-machine interfaces, direct estimates of multiple hand motion speeds are required to facilitate operations that fully reflect the user's intentions. However, no practical methods are available for this purpose because conventional function approximators cannot learn the complex relationships between the motion speeds and surface EMGs within a practical period of time. By contrast, it has been shown that a selective desensitization neural network (SDNN) can learn complex input-output relationships with low computational costs. In this study, we propose a method for the direct estimation of hand motion speeds from surface EMGs using a SDNN. We estimated the motion speed in practice to assess the efficacy of this proposed method. Our experimental results show that the proposed method can estimate the approximate speeds of six basic motions in real time.

収録刊行物

• 信号処理

  信号処理 18 (4), 225-228, 2014

  信号処理学会

詳細情報

• CRID: 1390282679441916672
• NII論文ID: 130004678189
• DOI: 10.2299/jsp.18.225
• ISSN: 18801013
• ISSN: 13426230
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可