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Gesture Classification from sEMG Signals using Machine Learning Approaches

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dc.contributor.advisor Peixoto, Nathalia
dc.contributor.author Rabbat, Nada
dc.date.accessioned 2022-05-16T17:24:10Z
dc.date.available 2022-05-16T17:24:10Z
dc.date.issued 2022-05-12
dc.identifier.uri http://hdl.handle.net/1920/12856
dc.description.abstract The open-access sEMG dataset [2] is utilized to classify hand motor movements to the respective gesture they represent, using machine learning approaches. This is done as a prospective benchmark for fast and efficient communication in cases of disability, where the gesture class can be replaced with a letter in the alphabet, to form a sentence from a combination of gestures. The Machine Learning approaches tested in this paper are Logistic Regression, Random Forest, and Bagging Classifier algorithms. All approaches will be tested for their accuracy in classifying the sEMG data. The Bagging classification algorithm had the highest accuracy score, followed by Random Forest and Logistic Regression. en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject gesture recognition en_US
dc.subject EMG en_US
dc.subject machine learning en_US
dc.title Gesture Classification from sEMG Signals using Machine Learning Approaches en_US
dc.identifier.orcid 0000-0002-9466-1322


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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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