Abstract
This paper proposes a hand motion recognition system for classifying different complex hand motions based on Surface Electromyography (SEMG). By defining ten common hand motions, the SEMG signals are recorded based on a SEMG capture device. A series of signal processing methods, including signal denoising, and feature extraction are analyzed to acquire the SEMG features. A trained Random Forest (RF) algorithm is used for the classification of ten different hand motions. The experimental results show that the proposed hand motion recognition system has a higher classification accuracy for identifying different hand motions.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
