Published Paper Details:

The intellectual computing of an effective human-computer interaction (HCI) or human alternative and augmentative communication (HAAC) is vital in our lives in today's technological environment. One of the most essential approaches for developing a gesture-based interface system for HCI or HAAC applications is hand gesture recognition. As a result, in order to create an advanced hand gesture recognition system with successful applications, it is required to establish an appropriate gesture recognition technique. Human activity and gesture detection are crucial components of the rapidly expanding area of ambient intelligence, which includes applications such as robots, smart homes, assistive systems, virtual reality, and so on. We proposed a method for recognizing hand movements using surface electromyography based on an ANN . The CapgMyo dataset based on the Myo wristband (an eight-channel sEMG device) is utilized to assess participants' forearm sEMG signals in our technique. The original sEMG signal is preprocessed to remove noise and detect muscle activity areas, then signals are subjected to time and frequency based domain feature extraction. We used an ANN classification model to predict various gesture output classes for categorization. Finally, we put the suggested model to the test to see if it could recognize these movements, and it did so with an accuracy of 87.32 percent.