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- W2320625002 abstract "A lot of real-life mobile sensing applications are becoming available. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. In this paper, we propose a LSTM-based feature extraction approach to recognize human activities using tri-axial accelerometers data. The experimental results on the (WISDM) Lab public datasets indicate that our LSTM-based approach is practical and achieves 92.1% accuracy." @default.
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- W2320625002 date "2016-01-01" @default.
- W2320625002 modified "2023-10-17" @default.
- W2320625002 title "LSTM Networks for Mobile Human Activity Recognition" @default.
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- W2320625002 doi "https://doi.org/10.2991/icaita-16.2016.13" @default.
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