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In this manuscript, eight hand motions were classified using ten different extracted features from sEMG signals. These signals were collected from four different muscles placed on the forearm. It was found out that the performance of a classifier was improved through the implementation of more than one feature. We tested two feature combinations; the classification accuracy rate of 94 % was achieved...
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