sEMG feature selection and classification using SVM-RFE

It is challenging to obtain good results for hand movements classification. Previous studies expended efforts on filters for sEMG data, feature extraction and classifier algorithms to achieve the best results. This paper proposes the insertion of a step in the classification process that selects whi...

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Bibliographic Details
Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2017; pp. 390 - 393
Main Authors Tosin, Mauricio C., Majolo, Mariano, Chedid, Raissan, Cene, Vinicius H., Balbinot, Alexandre
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2017
Subjects
Classification algorithms
Correlation
Feature extraction
Frequency-domain analysis
Noise measurement
Training
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Summary:It is challenging to obtain good results for hand movements classification. Previous studies expended efforts on filters for sEMG data, feature extraction and classifier algorithms to achieve the best results. This paper proposes the insertion of a step in the classification process that selects which features to use in training aiming to increase accuracy and performance. Feature selection was previously used in other classification tasks but is new in wrist/fingers movements classification. Obtained results were positives as the performance gain is huge (39 to 53 features out of 144 are used for classification) and accuracy reach promising values (above 90% for some subjects).
ISSN:1557-170X
DOI:10.1109/EMBC.2017.8036844