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EMG-based Estimation of Wrist Motion Using Polynomial Models.

Ali Ameri1

  • 1Biomedical Engineering Department, School of Medicine, Shahid Beheshti University of Medical Sciences, Velenjak, Tehran, Iran.

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Polynomial models show limited effectiveness for myoelectric control, especially compared to neural networks. However, incorporating more electromyogram (EMG) features can improve their performance in estimating wrist kinematics.

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Myoelectric control decodes motor intent from electromyogram (EMG) signals for prosthesis and robotic control.
  • Polynomial models offer low complexity for EMG-kinematics modeling.

Purpose of the Study:

  • Investigate the efficacy of polynomial models for estimating wrist kinematics from EMG signals.
  • Compare polynomial model performance against a multilayer perceptron (MLP) neural network.

Main Methods:

  • Measured EMG signals and wrist kinematics in ten able-bodied individuals during mirrored contractions.
  • Utilized time-domain (TD) and time-domain autoregressive (TDAR) EMG feature sets.
  • Applied polynomial models (order 1-4) and an MLP to map EMG to wrist motion.

Main Results:

  • Kinematic estimation accuracy improved with polynomial model order, saturating at the 4th order.
  • MLP significantly outperformed polynomial models with TD features.
  • With TDAR features, 4th order polynomial models approached MLP performance in two degrees of freedom (DoFs).

Conclusions:

  • Polynomial models are less effective than neural networks for highly nonlinear EMG-to-motion mapping.
  • Increasing the diversity of EMG features can enhance polynomial model performance, potentially matching complex models.