Abstract
Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k-means and linear support vector machine approaches that served as references.
Funding source: Deutscher Akademischer Austauschdienst
Award Identifier / Grant number: 57507871
Acknowledgment
We would like to thank DAAD organization for supporting the PhD work of Fars Samann.
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Research ethics: The principles we have followed in our work are: voluntary participation, informed consent, anonymity, confidentiality, potential for harm and communication of results.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: This research is funded by the Deutscher Akademischer Austauschdienst (DAAD), Germany with grant reference: 57507871.
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Data availability: The raw data can be obtained on request from the corresponding author.
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