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The ContextAct@A4H Real-Life Dataset of Daily-Living Activities

Activity Recognition Using Model Checking
  • Paula LagoEmail author
  • Fréderic Lang
  • Claudia Roncancio
  • Claudia Jiménez-Guarín
  • Radu Mateescu
  • Nicolas Bonnefond
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10257)

Abstract

Research on context management and activity recognition in smart environments is essential in the development of innovative well adapted services. This paper presents two main contributions. First, we present ContextAct@A4H, a new real-life dataset of daily living activities with rich context data (This research is supported by the Amiqual4Home Innovation Factory, http://amiqual4home.inria.fr funded by the ANR (ANR-11-EQPX-0002)). It is a high quality dataset collected in a smart apartment with a dense but non intrusive sensor infrastructure. Second, we present the experience of using temporal logic and model checking for activity recognition. Temporal logic allows specifying activities as complex events of object usage which can be described at different granularity. It also expresses temporal ordering between events thus palliating a limitation of ontology based activity recognition. The results on using the CADP toolbox for activity recognition in the real life collected data are very good.

Keywords

Smart home Context Activity recognition Temporal logic 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paula Lago
    • 1
    Email author
  • Fréderic Lang
    • 2
    • 3
  • Claudia Roncancio
    • 2
  • Claudia Jiménez-Guarín
    • 1
  • Radu Mateescu
    • 2
    • 3
  • Nicolas Bonnefond
    • 3
  1. 1.Systems and Computing Engineering DepartmentUniversidad de Los AndesBogotáColombia
  2. 2.Univ. Grenoble Alpes, CNRSGrenobleFrance
  3. 3.InriaGrenobleFrance

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