Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model

Because the relations between electromyographic signal (EMG) and anisometric joint torque remain unpredictable, the aim of this study was to determine the relations between the EMG activity and the isokinetic elbow joint torque via an artificial neural network (ANN) model. This 3-layer feed-forward...

Full description

Saved in:
Bibliographic Details
Published in:Journal of electromyography and kinesiology 1999-06, Vol.9 (3), p.173-183
Main Authors: Luh, Jer-Junn, Chang, Gwo-Ching, Cheng, Cheng-Kung, Lai, Jin-Shin, Kuo, Te-Son
Format: Article
Language:English
Subjects:
Adult
Artificial neural networks
Computer Simulation
Elbow Joint - physiology
Electromyography
EMG–torque relations
Humans
Isotonic Contraction - physiology
Kinetics
Male
Neural Networks (Computer)
Reproducibility of Results
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Because the relations between electromyographic signal (EMG) and anisometric joint torque remain unpredictable, the aim of this study was to determine the relations between the EMG activity and the isokinetic elbow joint torque via an artificial neural network (ANN) model. This 3-layer feed-forward network was constructed using an error back-propagation algorithm with an adaptive learning rate. The experimental validation was achieved by rectified, low-pass filtered EMG signals from the representative muscles, joint angle and joint angular velocity and measured torque. Learning with a limited set of examples allowed accurate prediction of isokinetic joint torque from novel EMG activities, joint position, joint angular velocity. Sensitivity analysis of the hidden node numbers during the learning and testing phases demonstrated that the choice of numbers of hidden node was not critical except at extreme values of those parameters. Model predictions were well correlated with the experimental data (the mean root-mean-square-difference and correlation coefficient γ in learning were 0.0290 and 0.998, respectively, and in three different speed testings were 0.1413 and 0.900, respectively). These results suggested that an ANN model can represent the relations between EMG and joint torque/moment in human isokinetic movements. The effect of different adjacent electrode sites was also evaluated and showed the location of electrodes was very important to produce errors in the ANN model.
ISSN:1050-6411
1873-5711
DOI:10.1016/S1050-6411(98)00030-3