Muscular fatigue detection using sEMG in dynamic contractions

In this work we have studied different indicators of muscle fatigue from the electrical signal produced by the muscles when contract (sEMG or EMG: surface electromyography): Mean Frequency of the power spectrum (MNF), Median Frequency (F med ), Dimitrov Spectral Index (FI nsm5 ), Root Mean Square (R...

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Published in2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2015; pp. 494 - 497
Main Authors Bueno, Diana R., Lizano, J. M., Montano, L.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.08.2015
Subjects
Correlation
Electromyography
Fatigue
Indexes
Muscles
Read only memory
Training
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ISSN1094-687X
1557-170X
DOI10.1109/EMBC.2015.7318407

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Summary:In this work we have studied different indicators of muscle fatigue from the electrical signal produced by the muscles when contract (sEMG or EMG: surface electromyography): Mean Frequency of the power spectrum (MNF), Median Frequency (F med ), Dimitrov Spectral Index (FI nsm5 ), Root Mean Square (RMS), and Zerocrossing (ZC). The most reliable features are selected to develop a detection algorithm that estimates muscle fatigue. The approach used in the algorithm is probabilistic and is based on the technique of Gaussian Mixture Model (GMM). The system is divided into two stages: training and validation. During training, the algorithm learns the distribution of data regarding fatigue evolution; after that, the algorithm is validated with data that have not been used to train. Therefore, two experimental sessions have been performed with 6 healthy subjects for biceps.
ISSN:1094-687X
1557-170X
DOI:10.1109/EMBC.2015.7318407