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A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.

Farid Mobasser1, Keyvan Hashtrudi-Zaad2

  • 1Invenium Technologies Corporation Mississauga, Ontario, Canada.

Biomedical Engineering and Computational Biology
|October 8, 2014
PubMed
Summary
This summary is machine-generated.

This study compares Radial Basis Function Artificial Neural Networks (RBF ANN) and Multilayer Perceptron Artificial Neural Networks (MLPANN) for estimating hand force using electromyography (EMG) signals and joint kinematics. Both methods show promise for non-invasive force measurement.

Keywords:
Force estimationelectromyographymultilayer perceptronneural networksradial basis function

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Area of Science:

  • Biomechanics
  • Neuroscience
  • Artificial Intelligence

Background:

  • Accurate hand force estimation is crucial for applications like prosthetic control and athletic training.
  • Traditional force sensors are expensive and cumbersome, necessitating alternative methods.
  • Electromyography (EMG) and joint kinematics offer a portable and cost-effective approach.

Purpose of the Study:

  • To investigate and compare the performance of Radial Basis Function Artificial Neural Networks (RBF ANN) and Multilayer Perceptron Artificial Neural Networks (MLPANN) for hand force estimation.
  • To evaluate the efficacy of using EMG signals, elbow joint position, velocity, and acceleration as inputs for these neural networks.

Main Methods:

  • Utilized EMG signals from upper-arm muscles, elbow angular position, and velocity as inputs for both RBF ANN and MLPANN.
  • Investigated the additional input of elbow angular acceleration for enhanced force estimation.
  • Experimentally compared the force estimation accuracy of RBF ANN and MLPANN under various operational conditions.

Main Results:

  • Both RBF ANN and MLPANN demonstrated capability in estimating hand force from EMG and kinematic data.
  • The inclusion of elbow angular acceleration as an input potentially improved the accuracy of force estimation.
  • Direct comparison of RBF ANN and MLPANN performance was conducted for hand force estimation.

Conclusions:

  • RBF ANN and MLPANN are viable non-model-based methods for estimating hand force using non-invasive techniques.
  • The choice of ANN architecture and input features (including acceleration) can influence estimation accuracy.
  • This research supports the development of cost-effective and portable systems for human movement analysis and control.