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 4. 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 gran-ularity. 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.