Orange Alerts- Behaviour Modeling and Health of older people in their homes

Research Article

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

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2011.246089,
        author={Denis Elbert and Holger Storf and Michael Eisenbarth and \O{}zg\'{y}r \'{Y}nalan and Mario Schmitt},
        title={An approach for detecting deviations in daily routine for long-term behavior analysis},
        proceedings={Orange Alerts- Behaviour Modeling and Health of older people in their homes},
        publisher={IEEE},
        proceedings_a={AAL},
        year={2012},
        month={4},
        keywords={Behavior Monitoring;  Circadian Rhythm Ambient Assisted  Living},
        doi={10.4108/icst.pervasivehealth.2011.246089}
    }
    
  • Denis Elbert
    Holger Storf
    Michael Eisenbarth
    Özgür Ünalan
    Mario Schmitt
    Year: 2012
    An approach for detecting deviations in daily routine for long-term behavior analysis
    AAL
    IEEE
    DOI: 10.4108/icst.pervasivehealth.2011.246089
Denis Elbert1,*, Holger Storf2, Michael Eisenbarth2, Özgür Ünalan2, Mario Schmitt2
  • 1: Hochschule Mannheim, Mannheim, Germany
  • 2: Fraunhofer IESE, Kaiserslautern, Germany
*Contact email: denis.elbert@gmx.de

Abstract

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 movement data, and a probabilistic model of behavior (PMB) based on the person’s activities of daily living (ADL).