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영어

The use of surface electromyography has increase recently for hand gesture recognition because of the feasible usage of low cost, wearable, non-invasive devices. Hand gesture enhances human-machine interaction to great extent. This paper proposed a robust approach for hand gesture classification using various machine learning classifiers. Six different features such as; minimum, maximum, peak to peak, root mean square, zero crossing and waveform length are extracted from raw data and fed to machine learning classifiers. Data is comprised of 36 individuals and seven gestures are classified with an accuracy of 90% and F1 score of 87% using Support Vector Machine classifier. Our reproducible implementation is available at github.com/talhaanwarch/emg-gesture-classification