@inproceedings{c3491dbaa535443c89db446c42f475fb,
title = "A novel hybrid model for activity recognition",
abstract = "Activity recognition focuses on inferring current user activities by leveraging sensory data available. Nowadays, combining data driven with knowledge based methods has show an increasing interest. However, uncertainty of sensor data has not been tackled in previous hybrid models. To address this issue, in this paper we propose a new hybrid model to cope with the uncertain nature of sensors data. We fully implement the system and evaluate it using a large real-world dataset. Experimental results prove the high performance level of the proposal in terms of recognition rates.",
keywords = "Activity recognition, Machine learning, Ontology, Smart home",
author = "Hela Sfar and Amel Bouzeghoub and Nathan Ramoly and J{\'e}r{\^o}me Boudy",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017 ; Conference date: 18-10-2017 Through 20-10-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-67422-3\_15",
language = "English",
isbn = "9783319674216",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "170--182",
editor = "Aoi Honda and Yasuo Narukawa and Vicenc Torra and Sozo Inoue",
booktitle = "Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings",
}