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- W2743704790 abstract "This paper presents a daily behavior identification algorithm based on sEMG to improve the accuracy of behavior identification. In the preprocessing stage, the original sEMG signal is effectively denoised by the combination of EMD denoising and wavelet denoising. In the feature extraction stage, the characteristics of MAV and AR model are extracted by time-frequency domain to express the behavior patterns. In the behavior classification stage, 8 features from 4 sEMG channels of MAV and AR model are use an input neurons of the BP neural network to improve the accuracy of behavior classification identification. Through the learning of a large number of training samples, the accuracy of the behavioral identification on the test samples comes to 91.02% in the experiment, which indicates that the daily behavior identification based on sEMG is a valuable method." @default.
- W2743704790 created "2017-08-17" @default.
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- W2743704790 date "2017-01-01" @default.
- W2743704790 modified "2023-10-12" @default.
- W2743704790 title "Daily behavior identification based on sEMG" @default.
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- W2743704790 doi "https://doi.org/10.1063/1.4992824" @default.
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