Hand motion estimation by EMG signals using linear multiple regression models.

  • Kitamura T
  • Tsujiuchi N
  • Koizumi T
  • 4

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Abstract

The purpose of this research is to construct an intelligent upper limb prosthesis control system that uses electromyogram (EMG) signals. The signal processing of EMG signals is performed using a linear multiple regression model that can learn parameters in a short time. Using this model, joint angles are predicted, and the motion pattern discrimination is conducted. Discriminated motions were grip, open, and chuck of a hand. Predicted joint angles were multi-finger angles corresponding to these three motions. In several experiments we proved the usefulness of processing EMG signals with a linear multiple regression model.

Author-supplied keywords

  • Algorithms
  • Computer Simulation
  • Electromyography
  • Electromyography: methods
  • Hand
  • Hand: physiology
  • Humans
  • Linear Models
  • Models, Biological
  • Movement
  • Movement: physiology
  • Muscle Contraction
  • Muscle Contraction: physiology
  • Muscle, Skeletal
  • Muscle, Skeletal: physiology
  • Pattern Recognition, Automated
  • Pattern Recognition, Automated: methods
  • Regression Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity

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Authors

  • Toru Kitamura

  • Nobutaka Tsujiuchi

  • Takayuki Koizumi

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