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An approach for detecting deviations in daily routine for long-term behavior analysis

: Elbert, D.; Storf, H.; Eisenbarth, M.; Unalan, O.; Schmitt, M.

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Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering -ICST-, Brussels; Center for Research and Telecommunication Experimentation for Networked Communities, Trento; Institute of Electrical and Electronics Engineers -IEEE-:
5th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2011 : Dublin, Ireland, 23 - 26 May 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-61284-767-2
ISBN: 978-1-936968-15-2
International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) <5, 2011, Dublin>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IESE ()

Rendering and offering adequate reminder services in a situation-aware, proactive manner and providing information for diagnosis support is a major issue for Ambient Assisted Living systems when it comes to dealing with persons suffering from mild dementia. One great challenge therefore is to reliably recognize and assess the long-term behavior of assisted persons. In the context of diagnosis support for caregivers or practitioners, deviations in the daily routine of a person with mild dementia might be an indicator of a deterioration of the affected person's cognitive condition. Based on this information, adequate help can be provided. We developed an approach to processing information regarding the modeling of daily routines and a comparison to previous days. Our solution can be seen as a combination of three approaches: a cosinor analysis based on the theory of circadian rhythms as a special representative of regression analysis, a histogram-based approach based on m ovement data, and a probabilistic model of behavior (PMB) based on the person's activities of daily living (ADL).