Abstract

Hand gesture recognition has many applications in engineering and medical fields. This paper proposes a real-time hand gesture recognition method using the surface electromyographic (sEMG) signal on the forearm. we apply a sliding window which allow us to observe a segment of the signals. Features are extracted from the signals of each sliding window, and then are inputted into a feed-forward artificial neural network (ANN) classifier which is trained at first. When the number of one hand gesture type of identified features reaches the threshold, it would be considered that the hand gesture is identified. Experiments show that the classification accuracy of real-time systems reaches 96%, and hand gestures can be recognized before they are completed.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.