An Event-Driven Approach to Activity Recognition in Ambient Assisted Living

Authors:
Holger Storf;Thomas Kleinberger;Martin Becker;Mario Schmitt;Frank Bomarius;Stephan Prueckner
Affiliations:
Fraunhofer-Institute Experimental Software Engineering, Kaiserslautern, Germany 67663;Fraunhofer-Institute Experimental Software Engineering, Kaiserslautern, Germany 67663;Fraunhofer-Institute Experimental Software Engineering, Kaiserslautern, Germany 67663;Fraunhofer-Institute Experimental Software Engineering, Kaiserslautern, Germany 67663;Fraunhofer-Institute Experimental Software Engineering, Kaiserslautern, Germany 67663;Department of Anaesthesiology and Emergency Medicine, Westpfalz-Klinikum GmbH, Kaiserslautern, Germany 67655
Venue:
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Year:
2009

Citing 3
Cited 3

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Abstract

One central challenge of Ambient Assisted Living systems is reliable recognition of the assisted person's current behavior, so that adequate assistance services can be offered in a specific situation. In the context of emergency support, such a situation might be an acute emergency situation or a deviation from the usual behavior. To optimize prevention of emergencies, reliable recognition of charac teristic Activities of Daily Living (ADLs) is promising. In this paper, we present our approach to processing information for the detection of ADLs in the EMERGE project. The approach is based on our multi-agent activity recog nition framework EARS with its special definition language EARL. An evaluation with controlled experiments has proven its suitability.