首页|New Research on Support Vector Machines from College of Computer Science and Tec hnology Summarized (Lower Limb Motion Recognition with Improved SVM Based on Sur face Electromyography)
New Research on Support Vector Machines from College of Computer Science and Tec hnology Summarized (Lower Limb Motion Recognition with Improved SVM Based on Sur face Electromyography)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on support vector machines are presented in a new report. According to news reporting out of the College of Computer Science and Technology by NewsRx editors, research stated, "During rob ot-assisted rehabilitation, failure to recognize lower limb movement may efficie ntly limit the development of exoskeleton robots, especially for individuals wit h knee pathology." Financial supporters for this research include Natural Science Foundation of Chi na. Our news correspondents obtained a quote from the research from College of Compu ter Science and Technology: "A major challenge encountered with surface electrom yography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is levera ged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the mult i-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant funct ion method is utilized to perform feature selection and reduce feature dimension . Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method i s leveraged to determine the best parameters for SVM."
College of Computer Science and Technolo gyMachine LearningSupport Vector Machines
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