Abstract
Decision support concepts such as context awareness and trend analysis are employed in a sensor-enabled environment for monitoring Activities of Daily Living and mobility patterns. Probabilistic Event Calculus is employed for the former; statistical process control techniques are applied for the latter case. The system is tested with real senior users within a lab as well as their home settings. Accumulated results show that the implementation of the two separate components, i.e. Sensor Data Fusion and Decision Support System, works adequately well. Future work suggests ways to combine both components so that more accurate inference results are achieved.
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Billis, A.S., Katzouris, N., Artikis, A., Bamidis, P.D. (2015). Clinical Decision Support for Active and Healthy Ageing: An Intelligent Monitoring Approach of Daily Living Activities. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_14
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DOI: https://doi.org/10.1007/978-3-319-23485-4_14
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