Abstract
As the world population is growing older, more and more peoples are facing health issues. For elderly, leaving alone can be tough and risky, typically, a fall can have serious consequences for them. Consequently, smart homes are becoming more and more popular. Such sensors enriched environment can be exploited for health-care applications, in particular Anomaly Detection (AD). Currently, most AD solutions only focus on detecting anomalies in the user daily activities while omitting the ones from the environment itself. For instance the user may have forgotten the pan on the stove while he/she is phoning. In this paper, we present a novel approach for detecting anomaly occurring in the home environment during user activities: CAREDAS. We propose a combination between ontologies and Markov Logic Network to classify the situations to anomaly classes. Our system is implemented, tested and evaluated using real data obtained from the Hadaptic platform. Experimental results prove our approach to be efficient in terms of recognition rate.
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Acknowledgements
This work has been partially supported by the project COCAPS (https://agora.bourges.univ-orleans.fr/COCAPS/) funded by Single Interministrial Fund N20 (FUI N20).
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Sfar, H., Ramoly, N., Bouzeghoub, A., Finance, B. (2017). CAREDAS: Context and Activity Recognition Enabling Detection of Anomalous Situation. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_3
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DOI: https://doi.org/10.1007/978-3-319-59758-4_3
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