@inproceedings{35b19c53b431455987407b41c9019554,
title = "Sensor-based activity recognition using extended belief rule-based inference methodology",
abstract = "The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.",
keywords = "belief rule base, activity recognition, decision support",
author = "A. Calzada and J. Liu and C.D. Nugent and Hui Wang and L. Martinez",
note = "36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014. ; Conference date: 06-11-2014",
year = "2014",
month = nov,
day = "6",
doi = "10.1109/EMBC.2014.6944178",
language = "English",
isbn = "978-1-4244-7929-0",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2694--2697",
booktitle = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}