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- W4380302307 abstract "Human internal state affects operator well-being and production outputs, but it cannot be directly measured and must be estimated. This paper proposes a deep learning approach to unsupervised nonlinear hidden state estimation using an auto-encoder, by framing it as a blind source separation (BSS) problem. The model is composed of an auto- encoder-based recurrent neural network (RNN) and extended to blind source separation through the use of local losses to decorrelate hidden signals. The number of sources can be determined by adjusting the dimension of the hidden state signal. Simulations demonstrate hidden state extraction when the correct dimensionality is selected and separation of multiple sources. Using an auto-encoder in the model restricts it to cases where there are more sensors than hidden states. This makes it well-suited for domains with redundant sensors, such as drones and self-driving cars." @default.
- W4380302307 created "2023-06-13" @default.
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- W4380302307 date "2023-05-06" @default.
- W4380302307 modified "2023-09-26" @default.
- W4380302307 title "Human internal state estimation as blind source separation using a dynamic auto-encoder" @default.
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- W4380302307 doi "https://doi.org/10.1109/icaci58115.2023.10146132" @default.
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