Paper Title:
AI Techniques to Estimate Muscle Force and Joint Torque for Upper Extremity
  Abstract

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. To map the EMG to joint torque, evolutionary techniques with suitable mathematical models are proposed. These models have unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression methods such as GA and SA. Five subjects took part in the experiments and they were asked to perform non-fatiguing and variable force maximal voluntary contractions and sub-maximal voluntary contractions. The recorded EMG signals data of various joints are entered to the model, to estimate the best fit values for the unknown parameters. Once these values of the parameters are obtained they are applied into the model and thus the joint torque is estimated. Predictions made by the proposed techniques are well correlated with experimental data.

  Info
Periodical
Key Engineering Materials (Volumes 467-469)
Edited by
Dehuai Zeng
Pages
788-793
DOI
10.4028/www.scientific.net/KEM.467-469.788
Citation
S. Parasuraman, A. W. Oyong, V. Lestari Jauw, "AI Techniques to Estimate Muscle Force and Joint Torque for Upper Extremity", Key Engineering Materials, Vols. 467-469, pp. 788-793, 2011
Online since
February 2011
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Price
$35.00
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