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- W2529237351 abstract "In this research, a methodology based on artificial neural networks is presented for identifying activation and deactivation times of the triceps muscle in respect to the activity pattern of the biceps muscle. For this purpose, recurrent and dynamic neural networks, NARX and ELMAN were used and the efficiency of these two networks is compared. The EMG signal of muscles was recorded while a healthy person was working with weights. The input of the network is the activity pattern of the biceps in the present and the past moment, and the output is the active and inactive times of the triceps in present moment. In fact the neural networks are used to estimate the times that the behavior switches in the triceps based on the biceps. The results of this research show that the proposed method can correctly identify the switching times of activation and deactivation of the triceps using the activation pattern of the biceps muscle. The best calculated identification accuracy is 98.5597 %." @default.
- W2529237351 created "2016-10-14" @default.
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- W2529237351 date "2015-11-01" @default.
- W2529237351 modified "2023-09-27" @default.
- W2529237351 title "Identification of activating and deactivating times of the triceps muscle during weigth lifting using recurrent neural networks" @default.
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- W2529237351 doi "https://doi.org/10.1109/ictck.2015.7582706" @default.
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