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- W2755355038 abstract "This paper presents the use of two non-iterative methods to perform the classification of 17 different upper-limb movements through sEMG signal processing. The two methods were compared with a SVM classifier using three different databases involving amputee subjects. The non-iterative methods presented equivalent or superior classification accuracy than SVM method. Thereafter a stage of PCA pre-processing method was used in order to promote a better class separation prior the non-iterative classifiers. The best accuracy result without PCA was achieved by the Regularized Extreme Learning Machines algorithm (88,4% for non-amputee subjects and 79,4% for the amputee). The PCA method used boosted the accuracy of the two non-iterative methods which the mean accuracy was 94% for the non-amputee subjects and 85% for the amputee subjects." @default.
- W2755355038 created "2017-09-25" @default.
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- W2755355038 date "2017-07-01" @default.
- W2755355038 modified "2023-09-26" @default.
- W2755355038 title "Using non-iterative methods and random weight networks to classify upper-limb movements through sEMG signals" @default.
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- W2755355038 doi "https://doi.org/10.1109/embc.2017.8037255" @default.
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