EMG-BASED HAND GESTURE RECOGNITION SYSTEM WITH TRANSFERABLE ADAPTIVE DOMAIN ADVERSARIAL NEURAL NETWORK FOR VIRTUAL REALITY APPLICATIONS

For human-computer interaction, hand gesture recognition is crucial. Here, we offer a method for recognising hand gestures using an EMG signal. In our framework, two different feature extraction algorithms are used to extract the hand area characteristics from the picture. The labels of hand motions are then predicted using a rule classifier. After seeing and identifying the fingers, a simple rule classifier may be employed to determine the hand motion. According to the quantity and distribution of discovered fingers, the rule classifier forecasts the hand motion. The content of the fingers determines which fingers are recognised. The major goal of this research is to anticipate hand gestures using an effective EMG classification algorithm, which also aims to increase classification accuracy and decrease miss classes.