AGACY monitoring: a hybrid model for activity recognition and uncertainty handling

Hela Sfar 1, 2 Amel Bouzeghoub 3, 1, 2 Nathan Ramoly 1, 2 Jerome Boudy 4, 2, 5
3 ACMES-SAMOVAR - Algorithmes, Composants, Modèles Et Services pour l'informatique répartie
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
4 ARMEDIA-SAMOVAR - ARMEDIA
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
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
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01687038
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Thursday, January 18, 2018 - 10:03:12 AM
Last modification on : Thursday, September 12, 2019 - 3:40:03 PM

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Hela Sfar, Amel Bouzeghoub, Nathan Ramoly, Jerome Boudy. AGACY monitoring: a hybrid model for activity recognition and uncertainty handling. ESWC 2017 : 14th European Semantic Web Conference, May 2017, Portorož, Slovenia. pp.254 - 269, ⟨10.1007/978-3-319-58068-5_16⟩. ⟨hal-01687038⟩

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