Discrimination of Forearm's Motions by Surface EMG Signals using Neural Network.

• Itakura Naoaki

  Department of Communications and Systems Engineering, The University of Electro-Communications

• Kinbara Yoh

  Department of Communications and Systems Engineering, The University of Electro-Communications

• Fuwa Teruhiko

  Department of Communications and Systems Engineering, The University of Electro-Communications

• Sakamoto Kazuyoshi

  Department of Communications and Systems Engineering, The University of Electro-Communications

抄録

We tried to discriminate different forearm's motions by surface EMG signals using neural network. In order to get a higher discrimination rate, the positions of electrodes were improved. We also tried to discriminate similar motions in order to clarify the limitation of the discrimination by surface EMG signals. Two experiments were carried out. One was to discriminate five different motions: grasp, wrist flexion, wrist extension, forearm pronation, and forearm supination (Experiment 1). The other was to discriminate four similar motions which have different quantitative definitions at grasp, wrist flexion/extension, or forearm pronatio/supination (Experiment 2). Four surface electrodes were placed on the skin above the main active muscles: short radial extensor m. of wrist, supinator m., long radial extensor m. of wrist, and ulnar flexor m. of wrist, considering anatomical functions of the forearm's muscles. EMG signals were recorded during 2 sec while the subjects kept the motions. Recorded EMG signals were sampled at 200 msec intervals after full-wave rectifying and low-pass filtering. Therefore, the number of sampling data patterns of EMG signals was 10 for every motion. Three layers of neural network was used for discrimination. The number of units in the input layer is 4, and the number of units in the output layer is 5 or 4. In order to get the best discrimination rate of the motions, we changed the number of units in the hidden layer from 3 to 12. The neural network was trained by the back-propagation algorithm. In Experiment 1, the best average values of discrimination rates under three pattems of EMG signals for each subject were 96.0%, 98.0%, and 87.2% when the numbers of units in the hidden layer were 10, 11, and 3 respectively. In Experiment 2 using original EMG patterns, the best average values of discrimination rates at grasp, extension/flexion, and pronation/supination were 59.5%, 76.0%, and 25.0% respectively. By using normalized EMG patterns, these were 40.0%, 84.8%, and 55.5% respectively.

収録刊行物

• Ａｐｐｌｉｅｄ　Ｈｕｍａｎ　Ｓｃｉｅｎｃｅ

  Ａｐｐｌｉｅｄ　Ｈｕｍａｎ　Ｓｃｉｅｎｃｅ 15 (6), 287-294, 1996

  日本生理人類学会

キーワード

• discrimination of motion
• surface EMG
• neural network

詳細情報

• CRID: 1390001204788248192
• NII論文ID: 10002424813
• NII書誌ID: AA11053183
• DOI: 10.2114/jpa.15.287
• COI: 1:STN:280:ByiC287pt1U%3D
• ISSN: 13413473
• PubMed: 9008983
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • PubMed
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可