首页|Recent Research from School of Electrical Engineering Highlight Findings in Supp ort Vector Machines (Hand Gesture Classification Framework Leveraging the Entrop y Features From Semg and Vmd Multi-class Svm)
Recent Research from School of Electrical Engineering Highlight Findings in Supp ort Vector Machines (Hand Gesture Classification Framework Leveraging the Entrop y Features From Semg and Vmd Multi-class Svm)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning - Su pport Vector Machines have been presented. According to news reporting out of Ta mil Nadu, India, by NewsRx editors, research stated, “To improve the classificat ion accuracy of hand movements from sEMG signals, this paper puts forward a unif ied hand gesture classification framework which exploits the potentials of varia tional mode decomposition (VMD) and multi-class support vector machine (SVM). Ac quiring the sEMG signals from 25 intact subjects for ten functional activities i n real-time, we implement a non-recursive adaptive decomposition technique to sE MG signals and perform power spectral analysis to identify the dominant narrow-b and intrinsic mode functions (IMFs) that contain prominent biomarkers.”
Tamil NaduIndiaAsiaMachine Learnin gSupport Vector MachinesSchool of Electrical Engineering
NETL
NSTL
Elsevier
