Hand Gesture Classification Using Emg Signal

Abstract

The art of gesture recognition involves identification and classification of gestures. A gesture is any reproducible action or a sequence of actions. There are lots of techniques and algorithms to recognize gestures. In the project, gestures are recognized using biological signals generated by the human body. There are many biological signals that can be used for gesture recognition. Some of them are Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG). EMG signals are generally used because they have good signal strength (in the order of mV). Thus we use emg signal as the acquisition of EMG signals is easy and less complex ascompared to the above mentioned signals. Five different gestures such as Six features such as . root mean square, mean, standard deviation, variance, maximum and minimum values are extracted from the emg signals. The classifier used under the study is SVM , giving classification accuracy of 96.8%.