Prediction of joint moments using a neural network model of muscle activations from EMG signals.

Full Abstract

Because the relationship between electromyographic (EMG) signals and muscle activations remains unpredictable, a new way to determine muscle activations from EMG signals by using a neural network is proposed and realized. Using a neural network to predict the muscle activations from EMG signals avoids establishing a complex mathematical model to express the muscle activation dynamics. The feed-forward neural network model of muscle activations applied here is composed of four layers and uses an adjusted back-propagation training algorithm. In this study, the basic back-propagation algorithm was not applicable, because muscle activation could not be measured, and hence the error between predicted activation and the real activation was not available. Thus, an adjusted back-propagation algorithm was developed. Joint torque at the elbow was calculated from the EMG signals of ten flexor and extensor muscles, using the neural network result of estimated activation of the muscles. Once muscle activations were obtained, Hill-type models were used to estimate muscle force. A musculoskeletal geometry model was then used to obtain moment arms, from which joint moments were determined and compared with measured values. The results show that this neural network model can be used to represent the relationship between EMG signals and joint moments well.

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Author information

Author/s: Wang, Lin (L); Buchanan, Thomas S (TS);

Affiliation: Center for Biomedical Engineering Research, University of Delaware, Newark 19716, USA.

Grants: AR40408 (Agency:NIAMS NIH HHS) ; AR46386 (Agency:NIAMS NIH HHS)

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, U.S. Gov't, P.H.S.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (IEEE Trans Neural Syst Rehabil Eng), published in United States. (Language: eng)

Reference: 2002-Mar; vol 10 (issue 1) : pp 30-7

Dates: Created 2002/08/13; Completed 2003/01/22; Revised 2007/11/14;

PMID: 12173737, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Action Potentials - physiology
Algorithms
Elbow - physiology
Elbow Joint - physiology
Electromyography
Humans
Isometric Contraction - physiology

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