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- W2616785502 abstract "Acquiring an ongoing human activity from raw sensor data is a challenging problem in pervasive systems. Earlier, research in this field has mainly adopted data-driven or knowledge based techniques for the activity recognition, however these techniques suffer from a number of drawbacks. Therefore, recent works have proposed a combination of these techniques. Nevertheless, they still do not handle sensor data uncertainty. In this paper, we propose a new hybrid model called AGACY Monitoring to cope with the uncertain nature of the sensor data. Moreover, we present a new algorithm to infer the activity instances by exploiting the obtained uncertainty values. The experimental evaluation of AGACY Monitoring with a large real-world dataset has proved the viability and efficiency of our solution." @default.
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- W2616785502 date "2017-01-01" @default.
- W2616785502 modified "2023-09-25" @default.
- W2616785502 title "AGACY Monitoring: A Hybrid Model for Activity Recognition and Uncertainty Handling" @default.
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- W2616785502 doi "https://doi.org/10.1007/978-3-319-58068-5_16" @default.
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