The ContextAct@A4H Real-Life Dataset of Daily-Living Activities
2017; Springer Science+Business Media; Linguagem: Inglês
10.1007/978-3-319-57837-8_14
ISSN1611-3349
AutoresPaula Lago, Frédéric Lang, Claudia Roncancio, Claudia Jiménez-Guarín, Radu Mateescu, Nicolas Bonnefond,
Tópico(s)Data Management and Algorithms
ResumoResearch 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.
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