Gesture Classification from sEMG Signals using Machine Learning Approaches
The open-access sEMG dataset  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.
Gesture recognition, EMG, Machine learning