A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction
Identifieur interne : 001F81 ( Ncbi/Checkpoint ); précédent : 001F80; suivant : 001F82A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction
Auteurs : Rita Chattopadhyay ; Mark Jesunathadas ; Brach Poston ; Marco Santello ; Jieping Ye ; Sethuraman PanchanathanSource :
- IEEE transactions on bio-medical engineering [ 0018-9294 ] ; 2012.
Abstract
Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.
Url:
DOI: 10.1109/TBME.2012.2193881
PubMed: 22498666
PubMed Central: 4010244
Affiliations:
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However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. 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