Abstract
We describe a novel application of Higher Order Statistics (HOS) for classifying Surface Electromyogram (sEMG) signals. We have followed seven approaches to identify discriminating signals representative of four primitive motions, i.e., elbow flexion/extension and forearm supination/pronation. The Sequential Forward Selection (SFS) method is utilized to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information. The SFS selected the kurtosis of sEMG as well as its second order statistics as discriminating features. Our method is robust, and does not require additional computations as compared to existing efficient methods for providing higher rates of correct classification of sEMG, which make it useful in practical sEMG' controlled prostheses.
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.
