Abstract
Activity recognition in smart environments and healthcare systems is gaining increasing interest. Several approaches are proposed to recognize activities namely intrusive and non-intrusive approaches. This paper presents a new fully non-intrusive approach for recognition of Activities of Daily Living (ADLs) in smart environments. Our approach treats the activity recognition process as an information retrieval problem in which ADLs are represented as hierarchical models, and patterns associated to these ADLs models are generated. A search process for these patterns is applied on the sequences of activities recorded when users perform their daily activities. We show through experiments on real datasets recorded in real smart home how our approach accurately recognizes activities.KeywordsActivity ModelRecognition AccuracyActivity RecognitionSmart HomeSmart EnvironmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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