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.
CITATION STYLE
Lago, P., Lang, F., Roncancio, C., Jiménez-Guarín, C., Mateescu, R., & Bonnefond, N. (2017). The contextact@A4H real-life dataset of daily-living activities activity recognition using model checking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10257 LNAI, pp. 175–188). Springer Verlag. https://doi.org/10.1007/978-3-319-57837-8_14
Mendeley helps you to discover research relevant for your work.