Improving the identification of finger movements using high-density surface electromyography pre-processed with PCA
2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020)(2020)
摘要
We investigated whether identification of different finger tasks only relying on the agonist or antagonist extensor digitorum communis (EDC) can be improved by using high-density sEMG (HDsEMG) pre-processed with principal component analysis (PCA). Monopolar HDsEMG was respectively recorded from EDC when the EDC muscle respectively acted as agonist or antagonist muscles. PCA-based approach was evaluated using k-nearest neighbour (KNN) classifier and compared with the classical spatial filters. Using PCA-based configuration can achieve better classification performance in identification of tasks and effort levels and dramatically outperformed spatial filtering configurations in all cases (p<0.05). It can be concluded that PCA can replace the prevailing spatial filters as a general procedure pre-processed HDsEMG, showing that distinct activation distribution patterns of EDC muscle as a function of individual finger flexion as well as extension and its corresponding contraction levels.
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关键词
high-density surface electromyogram,myoelectric pattern recognition,principal component analysis
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