Abstract
Surface electromyographic (SEMG) signals are widely used as inputs for the controllers of upper limb prostheses. The purpose of this paper is to assess several timedomain features used as inputs for two classifiers implemented using linear and quadratic discriminant analysis. Four movements of the forearm were classified using several combinations of features for each type of classifier. The best recognition rate of 95% was obtained for a linear discriminant analysis classifier.
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.
